Bitcoin Bull CyclesA simple indicator that identifies the primary upswing of Bitcoin following each Halving, the "Bull Cycle".
A "Bull Cycle" is identified as the first period of positive momentum after each Halving date, defined as the 50 Daily Moving Average (DMA) being above the 200 DMA.
Cerca negli script per "Cycle"
Ehlers Sinewave Indicator V2 [CC]The Sinewave Indicator was created by John Ehlers (Cybernetic Analysis For Stocks And Futures pgs 154-155) and this is an updated version of his original Sinewave Indicator which in my opinion seems to be more reactive to changes. Buy when the blue line crosses over the red line and sell when the blue line crosses under the red line. Also keep in mind that this indicator is based on cycles so it won't act the same as a typical indicator.
Let me know if there are other scripts you would like to see me publish or if you want something custom done!
Ehlers Spectrum Derived Filter Bank [CC]The Spectrum Derived Filter Bank was created by John Ehlers (Stocks & Commodities V. 26:3 (16-22)) and this is technically two indicators in one. This will let you know the current cycle period which is in blue and the other indicator will let you know if you should buy the stock or not. Buy when it is green and sell when it is red.
Let me know if you would like me to publish other scripts or if you want something custom done!
Note: I'm republishing this because the original script couldn't be found in searches so this will fix that.
TOP & BOTTOM Cycle [aamonkey]Works best on BTC and only on the daily timeframe!
This is a TOP / BOTTOM indicator for big market cycles.
These tops and bottoms are based on the difference between the 200MA on the daily and the price.
Recursive StochasticThe Self Referencing Stochastic Oscillator
The stochastic oscillator bring values in range of (0,100). This process is called Feature scaling or Unity-Based Normalization
When a function use recursion you can highlights cycles or create smoother results depending on various factors, this is the goal of a recursive stochastic.
For example : k = s(alpha*st+(1-alpha)*nz(k )) where st is the target source.
Using inputs with different scale level can modify the result of the indicator depending on which instrument it is applied, therefore the input must be normalized, here the price is first passed through a stochastic, then this result is used for the recursion.
In order to control the level of the recursion, weights are distributed using the alpha parameter. This parameter is in a range of (0,1), if alpha = 1, then the indicator act as a normal stochastic oscillator, if alpha = 0, then the indicator return na since the initial value for k = 0. The smaller the alpha parameter, the lower the correlation between the price and the indicator, but the indicator will look more periodic.
Comparison
Recursive Stochastic oscillator with alpha = 0.1 and bellow a classic oscillator (alpha = 1)
The use of recursion can both smooth the result and make it more reactive as well.
Filter As Source
It is possible to stabilize the indicator and make it less affected by outliers using a filter as input.
Lower alpha can be used in order to recover some reactivity, this will also lead to more periodic results (which are not inevitably correlated with price)
Hope you enjoy
For any questions/demands feel free to pm me, i would be happy to help you
IlluminateThe Illuminate script predicts the potential range of Bitcoin's top and bottom prices based on a logarithmic regression model, referencing Bitcoin's historical price trends and halvings. This script is designed to provide valuable insights into Bitcoin's price dynamics and long-term trends using principles derived from the "Bitcoin Law."
Key Features
Power Law Trend Lines
Primary Trend:
Projects the general growth trajectory of Bitcoin prices over time based on a logarithmic power law.
Resistance Line:
Identifies a potential upper limit of Bitcoin prices during market peaks.
Includes an offset trendline for an additional buffer zone.
Support Line:
Represents a possible bottom for Bitcoin prices during market downturns.
Offset trendlines highlight potential zones of price fluctuation near the support line.
Fill Zones:
Between resistance and offset: Semi-transparent Red.
Between support and offset: Semi-transparent Green/Blue.
Bitcoin Halving Events
Automatically marks significant Bitcoin halving dates with yellow vertical lines and labeled annotations.
Current and future halvings (approximate) are included.
Trending Phase Indication
A dynamic visual color fill highlights different phases of Bitcoin's price evolution based on a 4-year cycle.
Colors: Red, Green, Blue, Orange (indicating each phase).
"Trending Phase" label provides insight into the current phase.
Interactive Inputs
Show/Hide Resistance: Toggle resistance trend lines.
Show/Hide Support: Toggle support trend lines.
Show/Hide Halving Dates: Toggle visibility of halving annotations.
Customizable Parameters
Fine-tune parameters (A and n) for the main trend line to match your analysis needs.
How to Use
Overlay Analysis:
Add this script to your TradingView chart for direct overlay on Bitcoin's price data.
Interpret the Zones:
Use the resistance and support lines as potential upper and lower bounds for price movements.
Analyze fill zones for areas of likely price oscillation.
Halving Significance:
Observe price behavior before and after halving dates, which historically influence market trends.
Long-Term Perspective:
The model is optimized for long-term projections, making it suitable for strategic, rather than short-term, trading decisions.
Disclaimer:
This indicator is for educational purposes only and should not be used as investment advice. Always do your own research and consult with a financial advisor before making trading decisions.
Chuck Dukas Market Phases of Trends (based on 2 Moving Averages)This script is based on the article “Defining The Bull And The Bear” by Chuck Duckas, published in Stocks & Commodities V. 25:13 (14-22); (S&C Bonus Issue, 2007).
The article “Defining The Bull And The Bear” discusses the concepts of “bullish” and “bearish” in relation to the price behavior of financial instruments. Chuck Dukas explains the importance of analyzing price trends and provides a framework for categorizing price activity into six phases. These phases, including recovery, accumulation, bullish, warning, distribution, and bearish, help to assess the quality of the price structure and guide decision-making in trading. Moving averages are used as tools for determining the context preceding the current price action, and the slope of a moving average is seen as an indicator of trend and price phase analysis.
The six phases of trends
// Definitions of Market Phases
recovery_phase = src > ma050 and src < ma200 and ma050 < ma200 // color: blue
accumulation_phase = src > ma050 and src > ma200 and ma050 < ma200 // color: purple
bullish_phase = src > ma050 and src > ma200 and ma050 > ma200 // color: green
warning_phase = src < ma050 and src > ma200 and ma050 > ma200 // color: yellow
distribution_phase = src < ma050 and src < ma200 and ma050 > ma200 // color: orange
bearish_phase = src < ma050 and src < ma200 and ma050 < ma200 // color red
Recovery Phase : This phase marks the beginning of a new trend after a period of consolidation or downtrend. It is characterized by the gradual increase in prices as the market starts to recover from previous losses.
Accumulation Phase : In this phase, the market continues to build a base as prices stabilize before making a significant move. It is a period of consolidation where buying and selling are balanced.
Bullish Phase : The bullish phase indicates a strong upward trend in prices with higher highs and higher lows. It is a period of optimism and positive sentiment in the market.
Warning Phase : This phase occurs when the bullish trend starts to show signs of weakness or exhaustion. It serves as a cautionary signal to traders and investors that a potential reversal or correction may be imminent.
Distribution Phase : The distribution phase is characterized by the market topping out as selling pressure increases. It is a period where supply exceeds demand, leading to a potential shift in trend direction.
Bearish Phase : The bearish phase signifies a strong downward trend in prices with lower lows and lower highs. It is a period of pessimism and negative sentiment in the market.
These rules of the six phases outline the cyclical nature of market trends and provide traders with a framework for understanding and analyzing price behavior to make informed trading decisions based on the current market phase.
60-period channel
The 60-period channel should be applied differently in each phase of the market cycle.
Recovery Phase : In this phase, the 60-period channel can help identify the beginning of a potential uptrend as price stabilizes or improves. Traders can look for new highs frequently in the 60-period channel to confirm the trend initiation or continuation.
Accumulation Phase : During the accumulation phase, the 60-period channel can highlight that the current price is sufficiently strong to be above recent price and longer-term price. Traders may observe new highs frequently in the 60-period channel as the slope of the 50-period moving average (SMA) trends upwards while the 200-period moving average (SMA) slope is losing its downward slope.
Bullish Phase : In the bullish phase, the 60-period channel showing a series of higher highs is crucial for confirming the uptrend. Additionally, traders should observe an upward-sloping 50-period SMA above an upward-sloping 200-period SMA for further validation of the bullish phase.
Warning Phase : When in the warning phase, the 60-period channel can provide insights into whether the current price is weaker than recent prices. Traders should pay attention to the relationship between the price close, the 50-period SMA, and the 200-period SMA to gauge the strength of the phase.
Distribution Phase : In the distribution phase, traders should look for new lows frequently in the 60-period channel, hinting at a weakening trend. It is crucial to observe that the 50-period SMA is still above the 200-period SMA in this phase.
Bearish Phase : Lastly, in the bearish phase, the 60-period channel reflecting a series of lower lows confirms the downtrend. Traders should also note that the price close is below both the 50-period SMA and the 200-period SMA, with the relationship of the 50-period SMA being less than the 200-period SMA.
By carefully analyzing the 60-period channel in each phase, traders can better understand market trends and make informed decisions regarding their investments.
Color Agreement Aggregate (CAA)This indicator helps finding patterns within market structure in a highly intuitive manner.
It does this by painting a picture instead of presenting numerical values.
It greatly reduces noise in trend/structure analysis.
----- HOW TO USE IT -----
1) Zoom out of chart to get a clearer picture of overall color patterns.
2) Consider areas of intense reds and greens as areas of interest.
3) There is always a pattern of intense reds followed by intense greens. Consider this pattern as the start of a new cycle.
4) Key spikes and dips are shown when all 3 bands are matching of intense colors.
5) Turn on Precision in the Style tab to get more information on decisive spikes in price (See "Precision" below).
Reach (top band):
This is the fast and more volatile movement of the market. It shows the direction in which the recent price action is reaching towards.
Energy (middle band):
This is the medium speed of market movement. It shows the energy of the Reach and how influential it is to market change.
Frequent and intense change of color in this band can be a precursor of change within the Basis.
Basis (bottom band):
This is the slower, broader movement of the market. It is the basis on which the Reach and Energy sit on.
Intense colors in this band show major changes in price levels and market structure.
Precision:
Precision shows the weaker levels of colors. It does this by making bars in a band half its size.
For example, if there is a light green bar that is half, it means that the current bar is on the weaker level of the light green level.
Precision helps in identifying where there are influential moves in price action. Note, there will never be a half-sized bar in the highest and lowest levels.
This is because these levels are the limits and don't have a weaker half.
See notes in chart for more information. Note, you can turn off the labels in the Style tab.
----- HOW THIS INDICATOR IS ORIGINAL; WHAT IT DOES AND HOW IT DOES IT -----
This indicator has an original, unique ability to paint the overall market structure in a highly intuitive manner. It "paints" an image instead of showing numbers.
It does this by color-coding different levels of varying speeds of market movement. It then presents these levels as simple bars.
Finally, it stacks them all and creates an overall image of clear breaks and/or repeats within market structure.
This greatly reduces noise in pattern finding, finding breaks in market structure, and in confirming repeated patterns.
----- VERSION -----
The only significant information from this indicator are the colors themselves and the patterns, agreement, and aggregate of the colors.
This indicator does not provide any numerical information of the underlying, mathematical calculations.
The levels for the Reach are made by the KPAM; for the Energy, the CCI; and for the Basis, the RSI.
However, this indicator is not a variant, replacement, or presentation of the KPAM, CCI, or the RSI in any way, shape, or form -- this indicator does not present itself as such.
The 3 indicators are only useful to this indicator in as much as they are what the colors are derived from -- nothing more.
They are needed in order to obtain, visualize, and create the overall aggregate and agreement of colors.
Thus, the KPAM, CCI, and RSI cannot be adjust nor are they plotted. They are not, in any way, a focus of this indicator.
BTC ATH ROIThis indicator shows the ROI % of Bitcoin from when it passed its ATH of the previous bull cycle. I found it interesting that each time it crossed its ATH it took around 260-280 days to peak for each one. This bull run seems to follow between both of the previous bull runs including this recent dip.
There are a couple issues I want to fix but can't figure out:
1. You need to completely scroll out and move towards 2013 on the Daily chart for all 3 lines to show up. Would be nice to load all of that data at the start.
2. I can't query the value of the plots after they have been offset. This would be useful to create a prediction bias for the current plot so would could see where btc might go.
If you peeps know of a way to load all data or query plot values after offsets, please share. That would be awesome.
Ehler Stochastic Cyber Cycle Signals/AlertsThis script works based on @everget's version of Ehler Stochastic Cyber Cycle. Unlike @everget's work, my adaptation prints only crossovers into the chart that occur above or below the overbought/oversold zone.
You can find @everget's script with all related documentation here
I didn't change the calculation, I only reinvented how it is presented on the chart and added alerts.
5x Period Cycle SeasonalityShows the average from the last 5 periods for close price cycle. For example to see the annual seasonality of a stock for the last 5 years use on daily chart with the default setting of 252, the number of trading days in a year, approximately.
Global OECD CLI Diffusion Index YoY vs MoMThe Global OECD Composite Leading Indicators (CLI) Diffusion Index is used to gauge the health and directional momentum of the global economy and anticipate changes in economic conditions. It usually leads turning points in the economy by 6 - 9 months.
How to read: Above 50% signals economic expansion across the included countries. Below 50% signals economic contraction.
The diffusion index component specifically shows the proportion of countries with positive economic growth signals compared to those with negative or neutral signals.
The OECD CLI aggregates data from several leading economic indicators including order books, building permits, and consumer and business sentiment. It tracks the economic momentum and turning points in the business cycle across 38 OECD member countries and several other Non-OECD member countries.
Payday Anomaly StrategyThe "Payday Effect" refers to a predictable anomaly in financial markets where stock returns exhibit significant fluctuations around specific pay periods. Typically, these are associated with the beginning, middle, or end of the month when many investors receive wages and salaries. This influx of funds, often directed automatically into retirement accounts or investment portfolios (such as 401(k) plans in the United States), temporarily increases the demand for equities. This phenomenon has been linked to a cycle where stock prices rise disproportionately on and around payday periods due to increased buy-side liquidity.
Academic research on the payday effect suggests that this pattern is tied to systematic cash flows into financial markets, primarily driven by employee retirement and savings plans. The regularity of these cash infusions creates a calendar-based pattern that can be exploited in trading strategies. Studies show that returns on days around typical payroll dates tend to be above average, and this pattern remains observable across various time periods and regions.
The rationale behind the payday effect is rooted in the behavioral tendencies of investors, specifically the automatic reinvestment mechanisms used in retirement funds, which align with monthly or semi-monthly salary payments. This regular injection of funds can cause market microstructure effects where stock prices temporarily increase, only to stabilize or reverse after the funds have been invested. Consequently, the payday effect provides traders with a potentially profitable opportunity by predicting these inflows.
Scientific Bibliography on the Payday Effect
Ma, A., & Pratt, W. R. (2017). Payday Anomaly: The Market Impact of Semi-Monthly Pay Periods. Social Science Research Network (SSRN).
This study provides a comprehensive analysis of the payday effect, exploring how returns tend to peak around payroll periods due to semi-monthly cash flows. The paper discusses how systematic inflows impact returns, leading to predictable stock performance patterns on specific days of the month.
Lakonishok, J., & Smidt, S. (1988). Are Seasonal Anomalies Real? A Ninety-Year Perspective. The Review of Financial Studies, 1(4), 403-425.
This foundational study explores calendar anomalies, including the payday effect. By examining data over nearly a century, the authors establish a framework for understanding seasonal and monthly patterns in stock returns, which provides historical support for the payday effect.
Owen, S., & Rabinovitch, R. (1983). On the Predictability of Common Stock Returns: A Step Beyond the Random Walk Hypothesis. Journal of Business Finance & Accounting, 10(3), 379-396.
This paper investigates predictability in stock returns beyond random fluctuations. It considers payday effects among various calendar anomalies, arguing that certain dates yield predictable returns due to regular cash inflows.
Loughran, T., & Schultz, P. (2005). Liquidity: Urban versus Rural Firms. Journal of Financial Economics, 78(2), 341-374.
While primarily focused on liquidity, this study provides insight into how cash flows, such as those from semi-monthly paychecks, influence liquidity levels and consequently impact stock prices around predictable pay dates.
Ariel, R. A. (1990). High Stock Returns Before Holidays: Existence and Evidence on Possible Causes. The Journal of Finance, 45(5), 1611-1626.
Ariel’s work highlights stock return patterns tied to certain dates, including paydays. Although the study focuses on pre-holiday returns, it suggests broader implications of predictable investment timing, reinforcing the calendar-based effects seen with payday anomalies.
Summary
Research on the payday effect highlights a repeating pattern in stock market returns driven by scheduled payroll investments. This cyclical increase in stock demand aligns with behavioral finance insights and market microstructure theories, offering a valuable basis for trading strategies focused on the beginning, middle, and end of each month.
CRT AMD indicatorThis indicator is based on the Power of three (Accumulation Manipulation Distribution) Cycle, by marking the candle that Sweep the low or high of the previous candle and then closed back inside the range of the previous candle, indicating a possibility of a Manipulation or Reversal.
Combining the indicator with HTF Array and LTF Setup Entry will significantly improve the accuracy.
BTC x M2 Divergence (Weekly)### Why the "M2 Money Supply vs BTC Divergence with Normalized RSI" Indicator Should Work
IMPORTANT
- Weekly only indicator
- Combine it with BTC Halving Cycle Profit for better results
The "M2 Money Supply vs BTC Divergence with Normalized RSI" indicator leverages the relationship between macroeconomic factors (M2 money supply) and Bitcoin price movements, combined with technical analysis tools like RSI, to provide actionable trading signals. Here's a detailed rationale on why this indicator should be effective:
1. **Macroeconomic Influence**:
- **M2 Money Supply**: Represents the total money supply, including cash, checking deposits, and easily convertible near money. Changes in M2 reflect liquidity in the economy, which can influence asset prices, including Bitcoin.
- **Bitcoin Sensitivity to Liquidity**: Bitcoin, being a digital asset, often reacts to changes in liquidity conditions. An increase in money supply can lead to higher asset prices as more money chases fewer assets, while a decrease can signal tightening conditions and lower prices.
2. **Divergence Analysis**:
- **Economic Divergence**: The indicator calculates the divergence between the percentage changes in M2 and Bitcoin prices. This divergence can highlight discrepancies between Bitcoin's price movements and broader economic conditions.
- **Market Inefficiencies**: Large divergences may indicate inefficiencies or imbalances that could lead to price corrections or trends. For example, if M2 is increasing (indicating more liquidity) but Bitcoin is not rising proportionately, it might suggest a potential upward correction in Bitcoin's price.
3. **Normalization and Smoothing**:
- **Normalized Divergence**: Normalizing the divergence to a consistent scale (-100 to 100) allows for easier comparison and interpretation over time, making the signals more robust.
- **Smoothing with EMA**: Applying Exponential Moving Averages (EMAs) to the normalized divergence helps to reduce noise and identify the underlying trend more clearly. This double-smoothed divergence provides a clearer signal by filtering out short-term volatility.
4. **RSI Integration**:
- **RSI as a Momentum Indicator**: RSI measures the speed and change of price movements, indicating overbought or oversold conditions. Normalizing the RSI and incorporating it into the divergence analysis helps to confirm the strength of the signals.
- **Combining Divergence with RSI**: By using RSI in conjunction with divergence, the indicator gains an additional layer of confirmation. For instance, a bullish divergence combined with an oversold RSI can be a strong buy signal.
5. **Dynamic Zones and Sensitivity**:
- **Good DCA Zones**: Highlighting zones where the divergence is significantly positive (good DCA zones) indicates periods where Bitcoin might be undervalued relative to economic conditions, suggesting good buying opportunities.
- **Red Zones**: Marking zones with extremely negative divergence, combined with RSI confirmation, identifies potential market tops or bearish conditions. This helps traders avoid buying into overbought markets or consider selling.
- **Peak Detection**: The sensitivity setting for detecting upside down peaks allows for early identification of potential market bottoms, providing timely entry points for traders.
6. **Visual Cues and Alerts**:
- **Clear Visualization**: The plots and background colors provide immediate visual feedback, making it easier for traders to spot significant conditions without deep analysis.
- **Alerts**: Built-in alerts for key conditions (good DCA zones, red zones, sell signals) ensure traders can act promptly based on the indicator's signals, enhancing the practicality of the tool.
### Conclusion
The "M2 Money Supply vs BTC Divergence with Normalized RSI" indicator integrates macroeconomic data with technical analysis to offer a comprehensive view of Bitcoin's market conditions. By analyzing the divergence between M2 money supply and Bitcoin prices, normalizing and smoothing the data, and incorporating RSI for momentum confirmation, the indicator provides robust signals for identifying potential buying and selling opportunities. This holistic approach increases the likelihood of capturing significant market movements and making informed trading decisions.
MCG - Meme Coin Gains [Logue]Meme Coin Gains. Investor preference for meme coin trading may signal irrational exuberance in the crypto market. If a large spike in meme coin gains is observed, a top may be near. Therefore, the gains of the most popular meme coins (DOGE, SHIB, SATS, ORDI, BONK, PEPE, and FLOKI) were averaged together in this indicator to help indicate potential mania phases, which may signal nearing of a top. Two simple moving averages of the meme coin gains are used to smooth the data and help visualize changes in trend. In back testing, I found a 10-day "fast" sma and a 20-day "slow" sma of the meme coin gains works well to signal tops and bottoms when extreme values of this indicator are reached.
Meme coins were not traded heavily prior to 2020. Therefore, there is only one cycle to test at the time of initial publication. Also, the meme coin space moves fast, so more meme coins may need to be added later. Also, once a meme coin has finished its mania phase where everyone and their mother has heard of it, it doesn't seem to run again (at least with the data up until time of publication). Therefore, the value of this indicator may not be great unless it is updated frequently.
The two moving averages are plotted. For the indicator, top and bottom "slow" sma trigger lines are plotted. The sma trigger line and the periods (daily) of the moving averages can be modified to your own preferences. The "slow" sma going above or below the trigger lines will print a different background color. Plot on a linear scale if you want to view this as similar to an RSI-type indicator. Plot on a log scale if you want to view as similar to a stochastic RSI.
Use this indicator at your own risk. I make no claims as to its accuracy in forecasting future trend changes of Bitcoin or the crypto market.
MCV - Meme Coin Volume [Logue]Meme Coin Volume. Investor preference for meme coin trading may signal irrational exuberance in the crypto market. If a large spike in meme coin volume is observed, a top may be near. Therefore, the volume of the most popular meme coins was added together in this indicator to help indicate potential mania phases, which may signal nearing of a top. A simple moving average of the meme coin volume also helps visualize the trend while reducing the noise. In back testing, I found a 10-day sma of the meme coin volume works well.
Meme coins were not traded heavily prior to 2020. Therefore, there is only one cycle to test at the time of initial publication. Also, the meme coin space moves fast, so more meme coins may need to be added later.
The total volume is plotted along with a moving average of the volume. For the indicator, you are able to change the raw volume trigger line, the sma trigger line, and the period (daily) of the sma to your own preferences. The raw volume or sma going above their respective trigger lines will print a different background color.
Use this indicator at your own risk. I make no claims as to its accuracy in forecasting future trend changes of Bitcoin or the crypto market.
Financial Astrology Jupiter DeclinationJupiter moving from South declination minima toward the North maxima and during all the travel from North maxima to zero declination produced the strong bullish long term trend, this coincide with other planets declinations observations, in general, we noted that when a planet is in North declination (above zero boundary) this strengthen the planet force and produces more strong price effects.
This Jupiter declination pattern needs more research in others markets, will be great to get the participation from more financial astrologers that could research this declination cycle in other markets and share feedback with us.
Note: The Jupiter declination indicator is based on an ephemeris array that covers years 2010 to 2030, prior or after this years the declination 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.
Financial Astrology Sun DeclinationExtreme Sun declinations occurs at the solstices of summer and fall which correspond to the entry of Sun into Cancer and Capricorn respectively. At this extreme points we can observe that many markets tend to produce corrections. Is very interesting to see that when Sun reach the lowest and highest declination extremes. this events correlates very close to price corrections, is not an infalible rule, don't repeat for all observations but in many occurrences during all the historical data that we have of BTCUSD since 2010 have happened.
Is very likely that this same pattern repeats in others markets so will be great to get the participation of other financial astrologers that could research this cycle and share feedback with us.
Note: The Sun declination indicator is based on an ephemeris array that covers years 2010 to 2030, prior or after this years the declination 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.
Schaff Trend CycleThis indicator was originally developed by Doug Schaff in the 1990s (published in 2008).
DominantCycleCollection of Dominant Cycle estimators. Length adaptation used in the Adaptive Moving Averages and the Adaptive Oscillators try to follow price movements and accelerate/decelerate accordingly (usually quite rapidly with a huge range). Cycle estimators, on the other hand, try to measure the cycle period of the current market, which does not reflect price movement or the rate of change (the rate of change may also differ depending on the cycle phase, but the cycle period itself usually changes slowly). This collection may become encyclopaedic, so if you have any working cycle estimator, drop me a line in the comments below. Suggestions are welcome. Currently included estimators are based on the work of John F. Ehlers
mamaPeriod(src, dynLow, dynHigh) MESA Adaptation - MAMA Cycle
Parameters:
src : Series to use
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
Returns: Calculated period
Based on MESA Adaptive Moving Average by John F. Ehlers
Performs Hilbert Transform Homodyne Discriminator cycle measurement
Unlike MAMA Alpha function (in LengthAdaptation library), this does not compute phase rate of change
Introduced in the September 2001 issue of Stocks and Commodities
Inspired by the @everget implementation:
Inspired by the @anoojpatel implementation:
paPeriod(src, dynLow, dynHigh, preHP, preSS, preHP) Pearson Autocorrelation
Parameters:
src : Series to use
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
preHP : Use High Pass prefilter (default)
preSS : Use Super Smoother prefilter (default)
preHP : Use Hann Windowing prefilter
Returns: Calculated period
Based on Pearson Autocorrelation Periodogram by John F. Ehlers
Introduced in the September 2016 issue of Stocks and Commodities
Inspired by the @blackcat1402 implementation:
Inspired by the @rumpypumpydumpy implementation:
Corrected many errors, and made small speed optimizations, so this could be the best implementation to date (still slow, though, so may revisit in future)
High Pass and Super Smoother prefilters are used in the original implementation
dftPeriod(src, dynLow, dynHigh, preHP, preSS, preHP) Discrete Fourier Transform
Parameters:
src : Series to use
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
preHP : Use High Pass prefilter (default)
preSS : Use Super Smoother prefilter (default)
preHP : Use Hann Windowing prefilter
Returns: Calculated period
Based on Spectrum from Discrete Fourier Transform by John F. Ehlers
Inspired by the @blackcat1402 implementation:
High Pass, Super Smoother and Hann Windowing prefilters are used in the original implementation
phasePeriod(src, dynLow, dynHigh, preHP, preSS, preHP) Phase Accumulation
Parameters:
src : Series to use
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
preHP : Use High Pass prefilter (default)
preSS : Use Super Smoother prefilter (default)
preHP : Use Hamm Windowing prefilter
Returns: Calculated period
Based on Dominant Cycle from Phase Accumulation by John F. Ehlers
High Pass and Super Smoother prefilters are used in the original implementation
doAdapt(type, src, len, dynLow, dynHigh, chandeSDLen, chandeSmooth, chandePower, preHP, preSS, preHP) Execute a particular Length Adaptation or Dominant Cycle Estimator from the list
Parameters:
type : Length Adaptation or Dominant Cycle Estimator type to use
src : Series to use
len : Reference lookback length
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
chandeSDLen : Lookback length of Standard deviation for Chande's Dynamic Length
chandeSmooth : Smoothing length of Standard deviation for Chande's Dynamic Length
chandePower : Exponent of the length adaptation for Chande's Dynamic Length (lower is smaller variation)
preHP : Use High Pass prefilter for the Estimators that support it (default)
preSS : Use Super Smoother prefilter for the Estimators that support it (default)
preHP : Use Hann Windowing prefilter for the Estimators that support it
Returns: Calculated period (float, not limited)
doEstimate(type, src, dynLow, dynHigh, preHP, preSS, preHP) Execute a particular Dominant Cycle Estimator from the list
Parameters:
type : Dominant Cycle Estimator type to use
src : Series to use
dynLow : Lower bound for the dynamic length
dynHigh : Upper bound for the dynamic length
preHP : Use High Pass prefilter for the Estimators that support it (default)
preSS : Use Super Smoother prefilter for the Estimators that support it (default)
preHP : Use Hann Windowing prefilter for the Estimators that support it
Returns: Calculated period (float, not limited)
Full Moon and New Moon IndicatorThe Full Moon & New Moon Indicator is a custom Pine Script indicator which marks Full Moon (Pournami) and New Moon (Amavasya) events on the price chart. This indicator helps traders who incorporate lunar cycles into their market analysis, as certain traders believe these cycles influence market sentiment and price action. The current script is added for the year 2024 and 2025 and the dates are considered as per the Telugu calendar.
Features
✅ Identifies and labels Full Moon & New Moon days on the chart for the year 2024 and 2025
How it Works!
On a Full Moon day, it places a yellow label ("Pournami") above the corresponding candle.
On a New Moon day, it places a blue label ("Amavasya") above the corresponding candle.
Example Usage
When a Full Moon label appears, check for potential trend reversals or high volatility.
When a New Moon label appears, watch for market consolidation or a shift in sentiment.
Combine with candlestick patterns, support/resistance, or momentum indicators for a stronger trading setup.
🚀 Add this indicator to your TradingView chart and explore the market’s reaction to lunar cycles! 🌕
Medium Term Weighted Stochastic (STPMT) by DGTLa Stochastique Pondérée Moyen Terme (STPMT) , or Mᴇᴅɪᴜᴍ Tᴇʀᴍ Wᴇɪɢʜᴛᴇᴅ Sᴛᴏᴄʜᴀꜱᴛɪᴄꜱ created by Eric Lefort in 1999, a French trader and author of trading books
█ The STPMT indicator is a tool which concerns itself with both the direction and the timing of the market. The STPMT indicator helps the trader with:
The general trend by observing the level around which the indicator oscillates
The changes of direction in the market
The timing to open or close a position by observing the oscillations and by observing the relative position of the STPMT versus its moving average
STPMT Calculation
stpmt = (4,1 * stoch(5, 3) + 2,5 * stoch(14, 3) + stoch(45, 14) + 4 * stoch(75, 20)) / 11.6
Where the first argument of the stoch function representation above is period (length) of K and second argument smoothing period of K. The result series is then plotted as red line and its moving average as blue line. By default disabled gray lines are the components of the STPMT
The oscillations of the STPMT around its moving average define the timing to open a position as crossing of STMP line and moving average line in case when both trends have same direction. The moving average determines the direction.
Long examples
█ Tʜᴇ CYCLE Iɴᴅɪᴄᴀᴛᴏʀ is derived from the STPMT. It is
cycle = stpmt – stpmt moving average
It is indicates more clearly all buy and sell opportunities. On the other hand it does not give any information on market direction. The Cycle indicator is a great help in timing as it allows the trader to more easily see the median length of an oscillation around the average point. In this way the traders can simply use the time axis to identify both a favorable price and a favorable moment. The Cycle Indicator is presented as histogram
The Lefort indicators are not a trading strategy. They are tools for different purposes which can be combined and which can serve for trading all instruments (stocks, market indices, forex, commodities…) in a variety of time frames. Hence they can be used for both day trading and swing trading.
👉 For whom that would like simple version of the Cycle indicator on top of the main price chart with signals as presented below.
Please note that in the following code STMP moving average direction is not considered and will plot signals regardless of the direction of STMP moving average. It is not a non-repainting version too.
here is pine code for the overlay version
// © dgtrd
//@version=4
study("Medium Term Weighted Stochastic (STPMT) by DGT", "STPMT ʙʏ DGT ☼☾", true, format.price, 2, resolution="")
i_maLen = input(9 , "Stoch MA Length", minval=1)
i_periodK1 = input(5 , "K1" , minval=1)
i_smoothK1 = input(3 , "Smooth K1", minval=1)
i_weightK1 = input(4.1 , "Weight K1", minval=1, step=.1)
i_periodK2 = input(14 , "K2" , minval=1)
i_smoothK2 = input(3 , "Smooth K2", minval=1)
i_weightK2 = input(2.5 , "Weight K2", minval=1, step=.1)
i_periodK3 = input(45 , "K3" , minval=1)
i_smoothK3 = input(14 , "Smooth K3", minval=1)
i_weightK3 = input(1. , "Weight K3", minval=1, step=.1)
i_periodK4 = input(75 , "K4" , minval=1)
i_smoothK4 = input(20 , "Smooth K4", minval=1)
i_weightK4 = input(4. , "Weight K4", minval=1, step=.1)
i_data = input(false, "Components of the STPMT")
//------------------------------------------------------------------------------
// stochastic function
f_stoch(_periodK, _smoothK) => sma(stoch(close, high, low, _periodK), _smoothK)
//------------------------------------------------------------------------------
// calculations
// La Stochastique Pondérée Moyen Terme (STPMT) or Medium Term Weighted Stochastics calculation
stpmt = (i_weightK1 * f_stoch(i_periodK1, i_smoothK1) + i_weightK2 * f_stoch(i_periodK2, i_smoothK2) + i_weightK3 * f_stoch(i_periodK3, i_smoothK3) + i_weightK4 * f_stoch(i_periodK4, i_smoothK4)) / (i_weightK1 + i_weightK2 + i_weightK3 + i_weightK4)
stpmt_ma = sma(stpmt, i_maLen) // STPMT Moving Average
cycle = stpmt - stpmt_ma // Cycle Indicator
//------------------------------------------------------------------------------
// plotting
plotarrow(change(sign(cycle)), "STPMT Signals", color.green, color.red, 0, maxheight=41)
alertcondition(cross(cycle, 0), title="Trading Opportunity", message="STPMT Cycle : Probable Trade Opportunity\n{{exchange}}:{{ticker}}->\nPrice = {{close}},\nTime = {{time}}")
