Bitcoin Cycle High/Low with functional Alert [heswaikcrypt]Introduction
Just as machines are fine-tuned for maximum efficiency, trading indicators must evolve to meet the demands of ever-changing markets.
Credit goes to the initial author, @NoCreditsLeft I only improved the existing Pi-cycle indicator with a functional alert and included a bull mode indicator in the script. The alert can help you get a live alert at candle close when the cycle tops, bottoms, and the potential bull phase switch occurs.
Philip Swift’s Pi Cycle Top Indicator is a brilliant example of leveraging mathematical relationships to signal critical turning points in Bitcoin’s price cycles. Historically, it has identified market and local tops with some relative accuracy, often within three days, as demonstrated in all the previous bull run cycles.
At its core, the Pi Cycle Indicator derives its name from the mathematical constant π (pi), achieved by using simple moving averages (MAs) in a specific ratio: 𝜋 = Long MA/short MA
The Bull mode switch is calculated using a crossover of the short exponentia moving average and the long moving average.
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Knowing when Bitcoin reaches its top—and receiving timely alerts about it—is crucial for successful trading. The indicator is designed to signal;
Potential Bitcoin tops: Purple label
Potential Bitcoin bottoms : green Label, and
Parabolic swing : Yellow diamond shape (relating to the market switching to a potential bull mode)
"Please note: This indicator is tailored for Bitcoin using historical data analysis and should not be considered definitive. However accurate it might be."
Setting alerts
To set the alert conditions, select any alert function call to get alert whenever the conditions are met. The script is configured on dialy TF; you can set it on 1D or weekly TF.
Enjoy and Trade smartly
Cari dalam skrip untuk "Cycle"
NY Time Cycles# New York Time Cycles Indicator
## Overview
The Time Cycles indicator is a specialized technical analysis tool designed to divide the trading day into distinct time blocks based on New York trading hours. Developed for TradingView, this indicator helps traders identify and analyze market behavior during specific time periods throughout the trading session. The indicator displays six consecutive time blocks, each representing 90-minute segments of the trading day, while tracking price ranges within each block.
## Core Concept
The Time Cycles indicator is built on the premise that different periods during the trading day often exhibit unique market characteristics and behaviors. By segmenting the trading day into standardized 90-minute blocks, traders can:
1. Identify recurring patterns at specific times of day
2. Compare price action across different time blocks
3. Recognize potential support and resistance levels based on the high and low of previous time blocks
4. Develop time-based trading strategies specific to certain market hours
## Time Block Structure
The indicator divides the trading day into six sequential 90-minute blocks based on New York time:
1. **Box 1**: 07:00 - 08:30 ET
2. **Box 2**: 08:30 - 10:00 ET
3. **Box 3**: 10:00 - 11:30 ET
4. **Box 4**: 11:30 - 13:00 ET
5. **Box 5**: 13:00 - 14:30 ET
6. **Box 6**: 14:30 - 16:00 ET
These time blocks cover the core US trading session from pre-market into regular market hours.
## Visual Representation
Each time block is represented on the chart as a visual box that:
- Spans the exact time period of the block (horizontally)
- Extends from the highest high to the lowest low recorded during that time period (vertically)
- Is displayed with customizable colors and transparency levels
- Automatically builds in real-time as price action develops
Additionally, the indicator draws dashed projection lines that:
- Display the high and low of the most recently completed time block
- Extend forward in time (for up to 24 hours)
- Help traders identify potential support and resistance levels
## Technical Implementation
The indicator employs several key technical features:
1. **Time Detection**: Accurately identifies the current New York time to place each box in the correct time period
2. **Dynamic Box Creation**: Initializes and updates boxes in real-time as price action develops
3. **Range Tracking**: Continuously monitors and adjusts the high and low of each active time block
4. **Projection Lines**: Creates horizontal dashed lines projecting the high and low of the most recently completed time block
5. **Daily Reset**: Automatically resets all boxes and lines at the start of each new trading day
6. **Customization**: Allows users to set custom colors and transparency levels for each time block
This Time Cycles indicator provides traders with a structured framework for analyzing intraday market movements based on specific time periods. By understanding how the market typically behaves during each 90-minute block, traders can develop more targeted strategies and potentially identify higher-probability trading opportunities throughout the trading day.
Goertzel Cycle Period [Loxx]Goertzel Cycle Period is an indicator that uses Goertzel algorithm to extract the cycle period of ticker's price input to then be injected into advanced, adaptive indicators and technical analysis algorithms.
The following information is extracted from: "MESA vs Goertzel-DFT, 2003 by Dennis Meyers"
Background
MESA which stands for Maximum Entropy Spectral Analysis is a widely used mathematical technique designed to find the frequencies present in data. MESA was developed by J.P Burg for his Ph.D dissertation at Stanford University in 1975. The use of the MESA technique for stocks has been written about in many articles and has been popularized as a trading technique by John Ehlers.
The Fourier Transform is a mathematical technique named after the famed French mathematician Jean Baptiste Joseph Fourier 1768-1830. In its digital form, namely the discrete-time Fourier Transform (DFT) series, is a widely used mathematical technique to find the frequencies of discrete time sampled data. The use of the DFT has been written about in many articles in this magazine (see references section).
Today, both MESA and DFT are widely used in science and engineering in digital signal processing. The application of MESA and Fourier mathematical techniques are prevalent in our everyday life from everything from television to cell phones to wireless internet to satellite communications.
MESA Advantages & Disadvantage
MESA is a mathematical technique that calculates the frequencies of a time series from the autoregressive coefficients of the time series. We have all heard of regression. The simplest regression is the straight line regression of price against time where price(t) = a+b*t and where a and b are calculated such that the square of the distance between price and the best fit straight line is minimized (also called least squares fitting). With autoregression we attempt to predict tomorrows price by a linear combination of M past prices.
One of the major advantages of MESA is that the frequency examined is not constrained to multiples of 1/N (1/N is equal to the DFT frequency spacing and N is equal to the number of sample points). For instance with the DFT and N data points we can only look a frequencies of 1/N, 2/N, Ö.., 0.5. With MESA we can examine any frequency band within that range and any frequency spacing between i/N and (i+1)/N . For example, if we had 100 bars of price data, we might be interested in looking for all cycles between 3 bars per cycle and 30 bars/ cycle only and with a frequency spacing of 0.5 bars/cycle. DFT would examine all bars per cycle of between 2 and 50 with a frequency spacing constrained to 1/100.
Another of the major advantages of MESA is that the dominant spectral (frequency) peaks of the price series, if they exist, can be identified with fewer samples than the DFT technique. For instance if we had a 10 bar price period and a high signal to noise ratio we could accurately identify this period with 40 data samples using the MESA technique. This same resolution might take 128 samples for the DFT. One major disadvantage of the MESA technique is that with low signal to noise ratios, that is below 6db (signal amplitude/noise amplitude < 2), the ability of MESA to find the dominant frequency peaks is severely diminished.(see Kay, Ref 10, p 437). With noisy price series this disadvantage can become a real problem. Another disadvantage of MESA is that when the dominant frequencies are found another procedure has to be used to get the amplitude and phases of these found frequencies. This two stage process can make MESA much slower than the DFT and FFT . The FFT stands for Fast Fourier Transform. The Fast Fourier Transform(FFT) is a computationally efficient algorithm which is a designed to rapidly evaluate the DFT. We will show in examples below the comparisons between the DFT & MESA using constructed signals with various noise levels.
DFT Advantages and Disadvantages.
The mathematical technique called the DFT takes a discrete time series(price) of N equally spaced samples and transforms or converts this time series through a mathematical operation into set of N complex numbers defined in what is called the frequency domain. Why would we what to do that? Well it turns out that we can do all kinds of neat analysis tricks in the frequency domain which are just to hard to do, computationally wise, with the original price series in the time domain. If we make the assumption that the price series we are examining is made up of signals of various frequencies plus noise, than in the frequency domain we can easily filter out the frequencies we have no interest in and minimize the noise in the data. We could then transform the resultant back into the time domain and produce a filtered price series that hopefully would be easier to trade. The advantages of the DFT and itís fast computation algorithm the FFT, are that it is extremely fast in calculating the frequencies of the input price series. In addition it can determine frequency peaks for very noisy price series even when the signal amplitude is less than the noise amplitude. One of the disadvantages of the FFT is that straight line, parabolic trends and edge effects in the price series can distort the frequency spectrum. In addition, end effects in the price series can distort the frequency spectrum. Another disadvantage of the FFT is that it needs a lot more data than MESA for spectral resolution. However this disadvantage has largely been nullified by the speed of today's computers.
Goertzel algorithm attempts to resolve these problems...
What is the Goertzel algorithm?
The Goertzel algorithm is a technique in digital signal processing (DSP) for efficient evaluation of the individual terms of the discrete Fourier transform (DFT). It is useful in certain practical applications, such as recognition of dual-tone multi-frequency signaling (DTMF) tones produced by the push buttons of the keypad of a traditional analog telephone. The algorithm was first described by Gerald Goertzel in 1958.
Like the DFT, the Goertzel algorithm analyses one selectable frequency component from a discrete signal. Unlike direct DFT calculations, the Goertzel algorithm applies a single real-valued coefficient at each iteration, using real-valued arithmetic for real-valued input sequences. For covering a full spectrum, the Goertzel algorithm has a higher order of complexity than fast Fourier transform (FFT) algorithms, but for computing a small number of selected frequency components, it is more numerically efficient. The simple structure of the Goertzel algorithm makes it well suited to small processors and embedded applications.
The main calculation in the Goertzel algorithm has the form of a digital filter, and for this reason the algorithm is often called a Goertzel filter
Where is Goertzel algorithm used?
This package contains the advanced mathematical technique called the Goertzel algorithm for discrete Fourier transforms. This mathematical technique is currently used in today's space-age satellite and communication applications and is applied here to stock and futures trading.
While the mathematical technique called the Goertzel algorithm is unknown to many, this algorithm is used everyday without even knowing it. When you press a cell phone button have you ever wondered how the telephone company knows what button tone you pushed? The answer is the Goertzel algorithm. This algorithm is built into tiny integrated circuits and immediately detects which of the 12 button tones(frequencies) you pushed.
Future Additions:
Bartels test for cycle significance, testing output cycles for utility
Hodrick Prescott Detrending, smoothing
Zero-Lag Regression Detrending, smoothing
High-pass or Double WMA filtering of source input price data
References:
1. Burg, J. P., ëMaximum Entropy Spectral Analysisî, Ph.D. dissertation, Stanford University, Stanford, CA. May 1975.
2. Kay, Steven M., ìModern Spectral Estimationî, Prentice Hall, 1988
3. Marple, Lawrence S. Jr., ìDigital Spectral Analysis With Applicationsî, Prentice Hall, 1987
4. Press, William H., et al, ìNumerical Receipts in C++: the Art of Scientific Computingî,
Cambridge Press, 2002.
5. Oppenheim, A, Schafer, R. and Buck, J., ìDiscrete Time Signal Processingî, Prentice Hall,
1996, pp663-634
6. Proakis, J. and Manolakis, D. ìDigital Signal Processing-Principles, Algorithms and
Applicationsî, Prentice Hall, 1996., pp480-481
7. Goertzel, G., ìAn Algorithm for he evaluation of finite trigonometric seriesî American Math
Month, Vol 65, 1958 pp34-35.
Lunar Cycle Tracker - (Moon + 3 Mercury Retrogrades)This script overlays the lunar and Mercury retrograde cycles directly onto your chart, helping traders visualize natural timing intervals that may influence market behavior.
Key Features:
🌑 New Moon & Full Moon Markers:
Vertical lines and labels indicate new and full moon events each month. You can fully customize their colors.
🌗 Last Quarter Moon Fill:
A soft pink background highlights the last quarter moon phase (from 7.4 days after the full moon to the next new moon).
🪐 Three Mercury Retrograde Zones:
Highlight up to three retrograde periods per year with customizable date inputs and background color. Great for spotting potential reversal or volatility windows.
Customization:
Moon event dates and colors
Manual input for Mercury retrograde periods (year, month, day)
Full compatibility with all timeframes (1H, 4H, daily, etc.)
Great for astro-cycle traders, Gann-based analysts, or anyone who respects time symmetry in the markets.
Fully customizable & works across all timeframes.
This tool was created by AngelArt as part of a larger astro-market model using lunar timing and planetary retrogrades for cycle-based market analysis.
Market Cycle IndicatorThe Market Cycle Indicator is a tool that integrates the elements of RSI, Stochastic RSI, and Donchian Channels. It is designed to detect market cycles, enabling traders to enter and exit the market at the most opportune times.
This indicator provides a unique perspective on the market, combining multiple strategies into one unified and weighted approach. By factoring in the inputs from each of these popular technical analysis methods, it offers a more holistic view of the market trends and cycles.
Parameter Details:
Donchian Channels (DCO):
- donchianPeriod: Sets the period for the Donchian Channel calculation. Default is set to 14.
- donchianSmoothing: Sets the smoothing factor for the Donchian Channel calculation. Default is set to 3.
- donchianPrice: Selects the price type to be used in the Donchian Channel calculation. Default is set to the closing price.
Relative Strength Index (RSI):
- rsiPeriod: Sets the period for the RSI calculation. Default is set to 14.
- rsiSmoothing: Sets the smoothing factor for the RSI calculation. Default is set to 3.
- rsiPrice: Selects the price type to be used in the RSI calculation. Default is set to the closing price.
Stochastic RSI (StochRSI):
- srsiPeriod: Sets the period for the Stochastic RSI calculation. Default is set to 20.
- srsiSmoothing: Sets the smoothing factor for the Stochastic RSI calculation. Default is set to 3.
- srsiK: Sets the period for the %K line in the Stochastic RSI calculation. Default is set to 5.
- srsiD: Sets the period for the %D line in the Stochastic RSI calculation. Default is set to 5.
- srsiPrice: Selects the price type to be used in the Stochastic RSI calculation. Default is set to the closing price.
Weights:
- rsiWeight: Sets the weight for the RSI in the final aggregate calculation. Default is set to 1.
- srsiWeight: Sets the weight for the Stochastic RSI in the final aggregate calculation. Default is set to 1.
- dcoWeight: Sets the weight for the Donchian Channel in the final aggregate calculation. Default is set to 1.
Limits:
- limitHigh: Sets the upper limit for the indicator. Default is set to 80.
- limitLow: Sets the lower limit for the indicator. Default is set to 20.
By customizing these parameters, users can tweak the indicator to align with their own trading strategies and risk tolerance levels. Whether you're a novice or an experienced trader, the Comprehensive Market Cycle Indicator provides valuable insights into the market's behavior.
Uses library HelperTA
Bitcoin Long Time Cycle Detection (RGB Box)Hi!
I tried to analyze bitcoin's cycles since the beggining at INDEX:BTCUSD (on 1D timeframe) using some tools like Moving Averages and Ichimoku Cloud and Fibonacci Levels based on ATH prices of each cyle. Each cycle type is represented by colors:
1- Green is when the price is going to have a new ATH compared to the last ATH
2- Red is when the price is going to move down from the last ATH
3- Blue is when the price seems not going down anymore and moving up go get to the last ATH
The result is very interesting because each cycle has similar behaviours. The Main cycle is when there is a Green, Red and Blue and then there will be a Green again for the new cycle.
Logic of detecting Red part some times makes a Red between two Green s (which is normal but it makes a bit difference in the behaviour of the last part of that shorter Red part) so the valuable part is the Blue !
You can see the interesting noticable similarity of the Blue 's price movement and duration (written in the boxes).
What I understood from this model about each part was:
In the Greens, strongest candles of the whole market appear with higher volumes. which are the shortest parts too.
in the Reds, we see a lot of hammer candles here, price moves down step by step (unless it is going to have a NEW ATH which makes the duration of Red part vert shorter than the main Red parts before the Blue). Temporary resistances make some range channels but finally the price will go down a lot!
in the Blues, the main weak uptrend from the bottom which is finally going to see its last ATH price, but very slowly and weakly compared to the Green part. Some times there will be a lot of temporary downtrends too but in the end, price is going up. this part maybe the best time to buy for long time holding.
What makes this model interesting is that cycles match fundamental events like HALVING and periodic cycle analyses based on that.
In the last cylce we haven't seen the Blue Signal yet! so there should be alot of more patient till we say there will be no more down.
I hope it gives you more insight on the long term trend of crypto. I would be glad to hear your ideas to improve the model.
Hybrid, Zero lag, Adaptive cycle MACD [Loxx]TASC's March 2008 edition Traders' Tips includes an article by John Ehlers titled "Measuring Cycle Periods," and describes the use of bandpass filters to estimate the length, in bars, of the currently dominant price cycle.
What are Dominant Cycles and Why should we use them?
Even the most casual chart reader will be able to spot times when the market is cycling and other times when longer-term trends are in play. Cycling markets are ideal for swing trading however attempting to “trade the swing” in a trending market can be a recipe for disaster. Similarly, applying trend trading techniques during a cycling market can equally wreak havoc in your account. Cycle or trend modes can readily be identified in hindsight. But it would be useful to have an objective scientific approach to guide you as to the current market mode.
There are a number of tools already available to differentiate between cycle and trend modes. For example, measuring the trend slope over the cycle period to the amplitude of the cyclic swing is one possibility.
We begin by thinking of cycle mode in terms of frequency or its inverse, periodicity. Since the markets are fractal; daily, weekly, and intraday charts are pretty much indistinguishable when time scales are removed. Thus it is useful to think of the cycle period in terms of its bar count. For example, a 20 bar cycle using daily data corresponds to a cycle period of approximately one month.
When viewed as a waveform, slow-varying price trends constitute the waveform's low frequency components and day-to-day fluctuations (noise) constitute the high frequency components. The objective in cycle mode is to filter out the unwanted components--both low frequency trends and the high frequency noise--and retain only the range of frequencies over the desired swing period. A filter for doing this is called a bandpass filter and the range of frequencies passed is the filter's bandwidth .
Indicator Features
-Zero lag or Regular MACD/signal calculation
- Fixed or Band-pass Dominant Cycle for MACD and Signal MA period inputs
-10 different moving average options for both MACD and Signal MA calculations
-Separate Band-pass Dominant Cycle calculations for both MACD and Signal MA calculations
- Slow-to-Fast Band-pass Dominant Cycle input to tweak the ratio of MACD MA input periods as they relate to each other
Hurst Cycle Channel Clone [LazyBear]Cycle Channel is loosely based on Hurst's nested channels. Basic idea is to identify and highlight the shorter cycles, in the context of higher degree cycles.
This indicator plots the shorter term (red) & medium term (green) cycles as channels. Some things to note:
As you can see the red channel keeps moving with in the bounds of green channel. When green breaches red channel, it usually signifies extreme market condition.
Both red & green channels provide support/resistance levels. Also, the green channel provides S/R levels to the inner red channel.
Movement of red channel with reference to green highlights reversal points, reducing momentum et al. For ex., point "(x)" in the chart shows how red channel failed to reach the upper green channel line and highlighted the local top.
Use this just like other bands/channels. I have more indicators derived from this idea, will post them later.
Some more examples:
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MSFT 1M:
DXY 1M:
IWM 1M:
More info:
------------
cyclicwave.blogspot.com
List of my free indicators: bit.ly
List of my app-store indicators: blog.tradingview.com
(Support doc: bit.ly)
Schaff Trend Cycle (STC) - t0rdn3Schaff Trend Cycle (STC)
By t0rdn3 (original STC by , now with more descriptive naming)
Description
The Schaff Trend Cycle (STC) is a momentum-based oscillator that combines the speed of a fast EMA crossover with cyclical normalization. Developed by Doug Schaff, it identifies market turning points more responsively than MACD or RSI.
How It Works
1. EMA Difference : Calculates the difference between two EMAs of the source series (default: close).
2. Cycle Percentage : Normalizes that difference to a 0–100 range over the cycle period.
3. Smoothing : Applies exponential smoothing twice—first to the cycle percentage, then to its normalized cycles—to reduce noise.
4. Final STC Line : Produces a smoothed oscillator oscillating between 0 and 100.
Alerts
- "STC turned down above 75" : Fires once when STC makes a local peak above the upper threshold ( 75 ).
- "STC turned up below 25" : Fires once when STC makes a local trough below the lower threshold ( 25 ).
Inputs
Cycle Period : 12 — Lookback in bars for normalization
Fast EMA Length : 26 — Period of the fast EMA
Slow EMA Length : 50 — Period of the slow EMA
Smoothing Factor : 0.5 — Exponential smoothing coefficient (0–1)
Usage
Readings above 75 indicate an overbought cycle; readings below 25 indicate an oversold cycle. Crossings of the 50 midline can confirm trend direction:
- STC rising through 50 → bullish shift
- STC falling through 50 → bearish shift
Combine STC with price action or other trend filters to improve signal quality. You can adjust the cycle period and EMA lengths to match different timeframes or instruments.
Cryptolabs Global Liquidity Cycle Momentum IndicatorCryptolabs Global Liquidity Cycle Momentum Indicator (LMI-BTC)
This open-source indicator combines global central bank liquidity data with Bitcoin price movements to identify medium- to long-term market cycles and momentum phases. It is designed for traders who want to incorporate macroeconomic factors into their Bitcoin analysis.
How It Works
The script calculates a Liquidity Index using balance sheet data from four central banks (USA: ECONOMICS:USCBBS, Japan: FRED:JPNASSETS, China: ECONOMICS:CNCBBS, EU: FRED:ECBASSETSW), augmented by the Dollar Index (TVC:DXY) and Chinese 10-year bond yields (TVC:CN10Y). This index is:
- Logarithmically scaled (math.log) to better represent large values like central bank balances and Bitcoin prices.
- Normalized over a 50-period range to balance fluctuations between minimum and maximum values.
- Compared to prior-year values, with the number of bars dynamically adjusted based on the timeframe (e.g., 252 for 1D, 52 for 1W), to compute percentage changes.
The liquidity change is analyzed using a Chande Momentum Oscillator (CMO) (period: 24) to measure momentum trends. A Weighted Moving Average (WMA) (period: 10) acts as a signal line. The Bitcoin price is also plotted logarithmically to highlight parallels with liquidity cycles.
Usage
Traders can use the indicator to:
- Identify global liquidity cycles influencing Bitcoin price trends, such as expansive or restrictive monetary policies.
- Detect momentum phases: Values above 50 suggest overbought conditions, below -50 indicate oversold conditions.
- Anticipate trend reversals by observing CMO crossovers with the signal line.
It performs best on higher timeframes like daily (1D) or weekly (1W) charts. The visualization includes:
- CMO line (green > 50, red < -50, blue neutral), signal line (white), Bitcoin price (gray).
- Horizontal lines at 50, 0, and -50 for improved readability.
Originality
This indicator stands out from other momentum tools like RSI or basic price analysis due to:
- Unique Data Integration: Combines four central bank datasets, DXY, and CN10Y as macroeconomic proxies for Bitcoin.
- Dynamic Prior-Year Analysis: Calculates liquidity changes relative to historical values, adjustable by timeframe.
- Logarithmic Normalization: Enhances visibility of extreme values, critical for cryptocurrencies and macro data.
This combination offers a rare perspective on the interplay between global liquidity and Bitcoin, unavailable in other open-source scripts.
Settings
- CMO Period: Default 24, adjustable for faster/slower signals.
- Signal WMA: Default 10, for smoothing the CMO line.
- Normalization Window: Default 50 periods, customizable.
Users can modify these parameters in the Pine Editor to tailor the indicator to their strategy.
Note
This script is designed for medium- to long-term analysis, not scalping. For optimal results, combine it with additional analyses (e.g., on-chain data, support/resistance levels). It does not guarantee profits but supports informed decisions based on macroeconomic trends.
Data Sources
- Bitcoin: INDEX:BTCUSD
- Liquidity: ECONOMICS:USCBBS, FRED:JPNASSETS, ECONOMICS:CNCBBS, FRED:ECBASSETSW
- Additional: TVC:DXY, TVC:CN10Y
Pi Cycle Top & Bottom Indicator [InvestorUnknown]The Pi Cycle Top & Bottom Indicator is designed for long-term cycle analysis, particularly useful for detecting significant market tops and bottoms in assets like Bitcoin. By comparing the behavior of two moving averages, one with a shorter period (default 111) and the other with a longer period (default 350), the indicator helps investors identify potential turning points in the market.
Key Features:
Dual Moving Average System:
The indicator uses two moving averages (MA) to create a cyclic oscillator. The shorter moving average (Short Length MA) is more reactive to recent price changes, while the longer moving average (Long Length MA) smooths out long-term trends. Users can select between:
Simple Moving Average (SMA): A straightforward average of closing prices.
Exponential Moving Average (EMA): Places more weight on recent prices, making it more responsive to market changes.
Oscillator Mode Options:
The Pi Cycle Indicator offers two modes of oscillation to better suit different analysis styles:
RAW Mode: This mode calculates the raw ratio of the Short MA to the Long MA, offering a simple comparison of the two averages.
LOG(X) Mode: In this mode, the oscillator takes the natural logarithm of the Short MA to Long MA ratio. This transformation compresses extreme values and highlights relative changes more effectively, making it particularly useful for spotting shifts in long-term trends.
Cyclical Analysis:
The core of the Pi Cycle Indicator is its ability to visualize the relationship between the two moving averages. The ratio of the Short MA to the Long MA is plotted as an oscillator. When the oscillator crosses above or below a baseline (which is 1 for RAW mode and 0 for LOG(X) mode), it signals potential market turning points.
Visual Representation:
The indicator provides a clear visual display of market conditions:
Orange Line: Represents the Pi Cycle Oscillator, which shows the relationship between the short and long moving averages.
Gray Baseline: A reference line that dynamically adjusts based on the oscillator mode. Crosses above or below this line help indicate possible trend reversals.
Shaded Areas: Color-filled areas between the oscillator and the baseline, which are shaded green when the market is bullish (oscillator above baseline) and red when bearish (oscillator below baseline). This provides a visual cue to assist in identifying potential market tops and bottoms.
Use Cases:
The Pi Cycle Top & Bottom Indicator is primarily used in long-term market analysis, such as Bitcoin cycles, to identify significant tops and bottoms. These moments often coincide with large cyclical shifts, making it valuable for those aiming to enter or exit positions at key moments in the market cycle.
By analyzing the interaction between short-term and long-term trends, investors can gain insight into broader market dynamics and make more informed decisions regarding entry and exit points. The ability to switch between moving average types (SMA/EMA) and oscillator modes (RAW/LOG) adds flexibility for adapting to different market environments.
Combined EMA, SMMA, and 60-Day Cycle Indicator V2What This Script Does:
This script is designed to help traders visualize market trends and generate trading signals based on a combination of moving averages and price action. Here's a breakdown of its components and functionality:
Moving Averages:
EMAs (Exponential Moving Averages): These are indicators that smooth out price data to help identify trends. The script uses several EMAs:
200 EMA: A long-term trend indicator.
400 EMA: An even longer-term trend indicator.
55 EMA: A medium-term trend indicator.
89 EMA: Another medium-term trend indicator.
SMMA (Smoothed Moving Average): Similar to EMAs but with different smoothing. The script calculates:
21 SMMA: Short-term smoothed average.
9 SMMA: Very short-term smoothed average.
Cycle High and Low:
60-Day Cycle: The script looks back over the past 60 days to find the highest price (cycle high) and the lowest price (cycle low). These are plotted as horizontal lines on the chart.
Color-Coded Clouds:
Clouds: The script fills the area between certain EMAs with color-coded clouds to visually indicate trend conditions:
200 EMA vs. 400 EMA Cloud: Green when the 200 EMA is above the 400 EMA (bullish trend) and red when it’s below (bearish trend).
21 SMMA vs. 9 SMMA Cloud: Orange when the 21 SMMA is above the 9 SMMA and green when it’s below.
55 EMA vs. 89 EMA Cloud: Light green when the 55 EMA is above the 89 EMA and red when it’s below.
Trading Signals:
Buy Signal: This is shown when:
The price crosses above the 60-day low and
The EMAs indicate a bullish trend (e.g., the 200 EMA is above the 400 EMA and the 55 EMA is above the 89 EMA).
Sell Signal: This is shown when:
The price crosses below the 60-day high and
The EMAs indicate a bearish trend (e.g., the 200 EMA is below the 400 EMA and the 55 EMA is below the 89 EMA).
How It Helps Traders:
Trend Visualization: The colored clouds and EMA lines help you quickly see whether the market is in a bullish or bearish phase.
Trading Signals: The script provides clear visual signals (buy and sell labels) based on specific market conditions, helping you make more informed trading decisions.
In summary, this script combines several tools to help identify market trends and provide buy and sell signals based on price action relative to a 60-day high/low and the positioning of moving averages. It’s a useful tool for traders looking to visualize trends and automate some aspects of their trading strategy.
Machine Learning : Dominant Cycle Elastic Volume KNNAbout the Script
Dominant Cycle Elastic Volume KNN ,
is a non-parametric algorithm, which means that, initially it makes no assumptions about the underlying distribution of the time-series price as well as volume.
This approach gives it flexibility so that it can be used on a wide variety of securities at variety of timeframes.(even on lower timeframes such as seconds)
The main purpose of this indicator is to predict the trend of the underlying, by converging price, volume and dominant cycle as dimensions and generate signals of action.
Key terms :
Dominant cycle is a time cycle that has a greater influence on the overall behaviour of a system than other cycles.
The system uses Ehlers method to calculate Dominant Cycle/ Period.
Dominant cycle is used to determine the influencing period for the underlying.
Once the dominant cycle/ period is identified, it is treated as a dynamic length for considering further calculations
Elastic Volume MA is a volume based moving average which is generally used to converge the volume with price, the dominant period is used here as the length parameter
KNN K-Nearest Neighbour is one of the simplest Machine Learning algorithms based on Supervised Learning technique.
K-NN algorithm assumes the similarity between the new case/data and available cases and put the new case into the category that is most similar to the available categories.
K-NN algorithm stores all the available data and classifies a new data point based on the similarity. This means when new data appears then it can be easily classified into a well suite category by using K- NN algorithm. K-NN algorithm can be used for Regression as well as for Classification but mostly it is used for the Classification problems.
So, K-NN is used here to classify the trend of the Dominant Cycle Elastic Volume, and Generate Signals on top of it
How to Use the Indicator ?
The Buy Signal Candle
The Sell Signal Candle
The Buy Setup
The Sell Setup
Stop and Reverse Structure
What Timeframes and Symbols can this indicator be used on ?
The above indicator can be used on any liquid security which has volume information intact with ticker
and it can be used on any timeframe, but the best timeframes are
The indicator can also be used as a trend confirmatory indicators on lower time frames, like 30second
The Script has provision for alerts
Two alerts are there :
Alert 1= "LONG CONDITION : DCEV-ML"
Alert 2= "SHORT CONDITION : DCEV-ML"
How to request for access ?
Simply private message me !
90 Minute Cycles + MTFCredit goes to LuxAlgo for the inspiration from 'Sessions' which allowed users to analyse specific price movements within a user defined period with tools such as trendline, mean and vwap.
Settings
Sessions
Enable Session: Allows to enable or disable all associated elements with a specific user set session.
Session Time: Opening and closing times of the user set session in the hh:mm format.
Range: Highlights the associated session range on the chart.
Ranges Settings
Range Area colour: Set each range to a specific colour.
Range Label: Shows the session label at the mid-point of the session interval.
Usage
By breaking 24hrs in quarters, starting with an Asian range of 18:00 NY time you can visualise the principles of Accumulation, Manipulation, Distribution and Rebalance. Know as AMD or PO3 (Power of Three), the principle is that the Manipulation phase will break above or below the Accumulation, before moving in an apposing direction and then rebalancing. This only works when there is a higher timeframe PD array or liquidity to support an apposing move.
Further to the daily quarters, each one can then be broken down again into 90min cycles. Again, each represents AMD, allowing the user an opportunity to watch for reversals during the 90min manipulation phase.
Note: Ensure the Asian Cycle always begins at 18:00 NY time.
The example shows that the 90min cycle occurs, followed by an apposing move away in price action
Here is the Daily cycle, highlighting the Manipulation phase.
Enjoy!
90 Min Cycles Indicator PatekFynnip (RhyDoo)This indicator is for showing 90 minute cycles. You have an option to change the number of 90 minute cycles.
Also you can change on what timeframe the cycles should be visible. You can change when the 90 minute cycles should start.
You can use the Fib Points of 0.13, 0.33, 0.47, 0.67 and 0.8 inside the cycles.
This is mainly for Forex like AUDUSD , EURUSD , GBPUSD , NZDUSD , USDCAD and USDCHF .
You can also use this for Indices like US30, US100 and US500.
Pi Cycle Top IndicatorThe Pi Cycle Top Indicator plots the 111DMA and 350DMAx2. This is a well know indicator that has predicted Bitcoin cycle tops within a few days in previous cycles.
90 Minute Cycles Full90-Minute Cycles Indicator for London and NY Sessions
This is a more streamlined version of the 90-minute cycle indicator by sunwoo101.
The 90-Minute Cycles Indicator is built to help traders easily follow and trade around key market cycles during the London and New York sessions. Marking important 90-minute intervals and highlighting the True Cycle Open Price provides clear visual cues to help you make more informed trading decisions.
Key Features:
90-Minute Cycles for London and NY: The indicator automatically draws vertical lines marking every 90-minute cycle for the London and NY sessions. These lines are great for timing your trades and spotting potential shifts in market momentum.
True Cycle Open Price: A horizontal line is drawn at the True Cycle Open Price, which stays visible throughout the session. This gives you a key reference point for price levels that tend to act as support or resistance.
Customizable Visuals: You can fully personalize the indicator’s appearance - adjusting the colors and line styles and even controlling when the lines appear - so it blends perfectly with your existing charts.
All Cycles Drawn from the Start: Unlike other indicators, this one draws all the 90-minute cycles right when the session begins, so you can see the full day’s potential market moves as soon as the first cycle starts.
What’s Different About This Indicator:
London Session Support: In addition to the NY session, you now have 90-minute cycles for the London session, complete with its own True Cycle Open Price.
Better Customization: You have more control over the visual aspects of the indicator, so it can be tailored to fit your specific charting preferences.
Complete Cycle Visibility: All cycles are drawn immediately when the session starts, providing a full view of the day’s key moments right from the opening.
How to Use:
This indicator is perfect for scalping and short-term trading. Whether trading Forex or Indices and following SMT concepts, the cycle timing can help you pinpoint the best times for entering and exiting trades. The True Cycle Open Price is a crucial level of support or resistance throughout the session, making it a key marker to watch.
Scalpers: Use the 90-minute cycle lines to time your trades with the market's rhythm.
Day Traders: This indicator tracks the London and NY sessions, making it an excellent tool for day trading strategies where timing is critical.
Multi-Session Support:
Whether you're trading the London or New York session, the indicator will automatically adjust to your time zone and align the cycles to the relevant session. This helps you stay on top of key market activity across major trading hubs without changing anything manually.
90min CYCLE (12-18) (Redcrabice)this script was created by REDCRABICEFX.
this script was originally created to help me and the people in my groups.
this script was created to help traders who use 90 min and 450min cycles. dividing 12 min session break with 18 min session. It also has a projection of the next 18 min session so you know when it will start.
this script also has a candle countdown closer to the candle for better sniping entries, it will start appearing when the candle has less than 15 seconds before close, and only works on a 1min TimeFrame.
you can add this to your favorite and uses it without my consense, however, the code is limited to me and people in my group to see to prevent monetization
you can change the color of the 18 min session and the projection
you can change the color of the time label, you can also dissable it.
story: I'm learning 90 min cycle and I needed an indicator that would show me the session without having to redraw every time and ended up with 50 vertical lines across my chart, there weren't any similar ideas that show me exactly how this is.
all codes were put together by me. all I used was the reference and never copied it. Let's hope it doesn't get moderated.
Bitcoin Cycle [BigBeluga]Bitcoin Cycle Indicator is designed exclusively for analyzing Bitcoin’s long-term market cycles, working only on the 1D BTC chart . This indicator provides an in-depth view of potential cycle tops and bottoms, assisting traders in identifying key phases in Bitcoin’s market evolution.
🔵 Key Features:
Heatmap Cycle Phases: The indicator colors each cycle from blue to red , reflecting Bitcoin’s market cycle progression. Cooler colors (blue/green) signal potential accumulation or early growth phases, while warmer colors (yellow/red) indicate maturation and potential top regions.
All-Time High (ATH) and Future ATH Projection: Tracks the current ATH in real-time, while applying a linear regression model to project a possible new ATH in the future. This projection aims to provide insights into the next major cycle peak for long-term strategy.
Dashboard Overview: Displays the current ATH, potential new ATH, and the percentage distance between them. This helps users assess how far the current price is from the projected target.
Top & Bottom Cycle Signals: Red down arrows mark significant price peaks, potentially indicating cycle tops. Up arrows, numbered sequentially (inside each cycle), denote possible bottom signals for strategic DCA (Dollar Cost Averaging) entries.
1D BTC Chart Only: Built solely for the 1D BTC timeframe. Switching to any other timeframe or asset will trigger a warning message: " BTC 1D Only ." This ensures accuracy in analyzing Bitcoin’s unique cyclical behavior.
🔵 When to Use:
Ideal for long-term Bitcoin investors and cycle analysts, the Bitcoin Cycle Indicator empowers users to:
Identify key accumulation and distribution phases.
Track Bitcoin’s cyclical highs and lows with visual heatmap cues.
Estimate future potential highs based on historical patterns.
Strategize long-term positions by monitoring cycle tops and possible accumulation zones.
By visualizing Bitcoin’s cycles with color-coded clarity and top/bottom markers, this indicator is an essential tool for any BTC analyst aiming to navigate market cycles effectively.
Kondratieff Wave & Benner Business CyclesKondratieff Wave Theory
Description: The Kondratieff Wave, also known as K-Waves or Long Waves, is an economic theory that posits long-term cycles of approximately 40-60 years in capitalist economies. These cycles consist of four phases: Spring (expansion and recovery), Summer (prosperity and peak), Autumn (stagnation and recession), and Winter (depression and restructuring). The theory suggests that technological innovations and major economic shifts drive these waves, influencing periods of growth and decline over decades.
Creator Bio: Nikolai Dmitriyevich Kondratieff (1892–1938) was a Russian economist born in the Kostroma Governorate. He studied at the University of St. Petersburg and became a prominent figure in Soviet economics. Kondratieff developed his long-wave theory in the 1920s while analyzing historical economic data, publishing works like The Major Economic Cycles (1925). His ideas clashed with Soviet ideology, leading to his arrest in 1930 during Stalin’s purges. He was executed in 1938, but his work gained recognition posthumously, influencing modern economic cycle analysis.
Benner Cycle Theory
Description: The Benner Cycle, proposed by Samuel Benner, is a predictive model for business and commodity price cycles, focusing on shorter-term economic fluctuations. Benner identified recurring patterns in market peaks (highs), panics (crashes), and buying opportunities (lows), with cycles averaging 8-10 years for highs, 7-8 years for panics, and 8-9 years for buys. His theory, based on historical observations of U.S. markets, aimed to guide farmers and investors by forecasting periods of prosperity and distress.
Creator Bio: Samuel T. Benner (1830s–unknown) was an American farmer and businessman from Ohio, not a formally trained economist. After losing his fortune in the Panic of 1873, Benner turned to studying economic patterns. In 1875, he self-published Benner’s Prophecies of Future Ups and Downs in Prices, a book that charted cycles in pig iron prices and other commodities. His work gained a cult following among traders and remains studied for its empirical approach, despite Benner’s lack of academic credentials and limited biographical records.
Business Cycle Indicators (Normalized)This script aggregates and normalizes several key economic indicators to provide a comprehensive view of the business cycle and overall market conditions. By combining these indicators into a single, normalized average line, the script helps identify overarching trends and shifts in the economy, aiding in more informed trading and investment decisions.
Included Indicators:
Inverted National Financial Conditions Index (NFCI):
Symbol: FRED:NFCI
Measures financial stress in the markets. An inverted NFCI aligns higher values with positive financial conditions.
Inverted Net Percentage of Banks Tightening Lending Standards (DRTSCIS):
Symbol: FRED:DRTSCIS
Reflects changes in bank lending practices. Inverting this indicator means higher values indicate easing lending standards, which is generally positive for economic growth.
HYG Close Price (iShares High Yield Corporate Bond ETF):
Symbol: AMEX:HYG
Represents the performance of high-yield corporate bonds, providing insight into credit market conditions.
Inverted High-Yield Credit Spread (BAMLH0A0HYM2):
Symbol: FRED:BAMLH0A0HYM2
Measures the spread between high-yield bonds and risk-free securities. A narrower (inverted) spread indicates better market conditions.
Manufacturing/Non-Manufacturing New Orders Ratio:
Symbols: ECONOMICS:USMNO (Manufacturing), ECONOMICS:USNMNO (Non-Manufacturing)
Compares manufacturing to non-manufacturing new orders to gauge shifts in economic activity.
US PMI (Purchasing Managers' Index):
Symbol: ECONOMICS:USBCOI
An indicator of the economic health of the manufacturing sector.
10-Year Inflation Breakeven (T10YIE):
Symbol: FRED:T10YIE
Represents market expectations of inflation over the next ten years.
Inverted 10-Year Real Yield (DFII10):
Symbol: FRED:DFII10
Reflects the real yield on 10-year Treasury Inflation-Protected Securities (TIPS). Inverted to align higher values with positive economic sentiment.
Copper/Gold Ratio:
Symbols: CAPITALCOM:COPPER (Copper), TVC:GOLD (Gold)
Compares the prices of copper and gold, often used as a barometer for global economic activity.
Features:
Normalized Indicators: Each indicator is normalized to a 0-100 scale to facilitate direct comparison, regardless of their original units or scales.
Normalized Average Line: Calculates and plots the average of all available normalized indicators, providing a single line that represents the combined economic signals.
Customizable Display:
Show Individual Indicators: Option to display individual normalized indicators for detailed analysis.
Show Normalized Average Line: Option to display the normalized average line for a consolidated view.
Dynamic Labeling: Displays the latest value of the normalized average directly on the chart for quick reference.
How to Use:
Adding the Script:
Apply the script to a chart in TradingView using a timeframe that aligns with the frequency of the economic data (daily or weekly recommended).
Customization:
Show Normalized Average Line: Enabled by default to display the combined indicator.
Show Individual Indicators: Enable this option in the script settings to display all individual normalized indicators.
Interpretation:
Normalized Scale (0-100): Higher values generally indicate stronger economic conditions, while lower values may suggest weakening conditions.
Trend Analysis: Use the normalized average line to identify trends and potential turning points in the business cycle.
Notes:
Data Availability: Ensure you have access to all the data sources used in the script. Some data feeds may require specific TradingView subscriptions.
Indicator Limitations: Economic indicators are subject to revisions and may not reflect real-time market conditions.
No Investment Advice: This script is a tool for analysis and should not be considered as financial advice. Always conduct your own research before making investment decisions.
RoC Momentum CycleRoC Momentum Cycles (RMC) is derived from RoC (Rate of Change) indicator.
Motivation behind RMC: Addressing RoC’s Shortcomings
While the Rate of Change (RoC) indicator is a valuable tool for assessing momentum, it has notable limitations that traders must be aware of. One of the primary challenges with the traditional RoC is its sensitivity to price fluctuations, which can lead to false signals in volatile markets. This often results in premature entries or exits, impacting trading performance.
By smoothing out the RoC calculations and focusing on more consistent signal generation (using SMA on smoothed RoC), RMC offers a more consistent representation of price trends.
Momentum Cycles
RMC helps visualize momentum cycles in a much better way compared to RoC.
Long Momentum Cycle : A cross-over of smoothed RoC (blue line) above averaged signal (orange line) below zero marks start of a new potential upside cycle which ends when the blue line comes back to zero line from above.
Short Momentum Cycle : A cross-under of blue line below orange line above zero marks beginning of a potential downside cycle which ends when the blue line comes back to zero from below.
Rotation Cycles GraphRotation Cycles Graph Indicator
Overview:
The Rotation Cycles Graph Indicator is designed to visualize rotation cycles in financial markets. It aims to provide insights into shifts between various market phases, including growth, weakening, recovery, and contraction, allowing traders to potentially identify changing market dynamics.
Key Components:
Z-Score Calculation:
The indicator employs Z-score calculation to normalize data and identify deviations from the mean. This is instrumental in understanding the current state of the market relative to its historical behavior.
Ehlers Loop Visualization:
The Ehlers Loop function generates a visual representation of rotation cycles. It utilizes x and y coordinates on the chart to represent market conditions. These coordinates determine the position and categorization of the market state.
Table Visualization:
At the bottom of the chart, a table categorizes market conditions based on x and y values. This table serves as a reference to understand the current market phase.
Customizable Parameters:
The indicator offers users the flexibility to adjust several parameters:
Length and Smoothness: Users can set the length and smoothness parameters for the Z-score calculation, allowing for customization based on the market's volatility.
Graph Settings: Parameters such as bar scale, graph position, and the length of the tail for visualization can be fine-tuned to suit individual preferences.
Understanding Coordinates:
The x and y coordinates plotted on the chart represent specific market conditions. Interpretation of these coordinates aids in recognizing shifts in market behavior.
This screenshot shows visual representation behind logic of X and Y and their rotation cycles
Here is an example how rotation marker moved from growing to weakening and to the contraction quad, during a big market crush:
Note:
This indicator is a visualization tool and should be used in conjunction with other analytical methods for comprehensive market analysis.
Understanding the context and nuances of market dynamics is essential for accurate interpretation of the Rotation Cycles Graph Indicator.
Big thanks to @PineCodersTASC for their indicator, what I used as a reference
