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TASC 2025.01 Linear Predictive Filters█ OVERVIEW This script implements a suite of tools for identifying and utilizing dominant cycles in time series data, as introduced by John Ehlers in the "Linear Predictive Filters And Instantaneous Frequency" article featured in the January 2025 edition of TASC's Traders' Tips . Dominant cycle information can help traders adapt their indicators and strategies to changing market conditions. █ CONCEPTS Conventional technical indicators and strategies often rely on static, unchanging parameters, which may fail to account for the dynamic nature of market data. In his article, John Ehlers applies digital signal processing principles to address this issue, introducing linear predictive filters to identify cyclic information for adapting indicators and strategies to evolving market conditions. This approach treats market data as a complex series in the time domain. Analyzing the series in the frequency domain reveals information about its cyclic components. To reduce the impact of frequencies outside a range of interest and focus on a specific range of cycles, Ehlers applies second-order highpass and lowpass filters to the price data, which attenuate or remove wavelengths outside the desired range. This band-limited analysis isolates specific parts of the frequency spectrum for various trading styles, e.g., longer wavelengths for position trading or shorter wavelengths for swing trading. After filtering the series to produce band-limited data, Ehlers applies a linear predictive filter to predict future values a few bars ahead. The filter, calculated based on the techniques proposed by Lloyd Griffiths, adaptively minimizes the error between the latest data point and prediction, successively adjusting its coefficients to align with the band-limited series. The filter's coefficients can then be applied to generate an adaptive estimate of the band-limited data's structure in the frequency domain and identify the dominant cycle. █ USAGE This script implements the following tools presented in the article: Griffiths Predictor This tool calculates a linear predictive filter to forecast future data points in band-limited price data. The crosses between the prediction and signal lines can provide potential trade signals. Griffiths Spectrum This tool calculates a partial frequency spectrum of the band-limited price data derived from the linear predictive filter's coefficients, displaying a color-coded representation of the frequency information in the pane. This mode's display represents the data as a periodogram . The bottom of each plotted bar corresponds to a specific analyzed period (inverse of frequency), and the bar's color represents the presence of that periodic cycle in the time series relative to the one with the highest presence (i.e., the dominant cycle). Warmer, brighter colors indicate a higher presence of the cycle in the series, whereas darker colors indicate a lower presence. Griffiths Dominant Cycle This tool compares the cyclic components within the partial spectrum and identifies the frequency with the highest power, i.e., the dominant cycle . Traders can use this dominant cycle information to tune other indicators and strategies, which may help promote better alignment with dynamic market conditions. Notes on parameters Bandpass boundaries: In the article, Ehlers recommends an upper bound of 125 bars or higher to capture longer-term cycles for position trading. He recommends an upper bound of 40 bars and a lower bound of 18 bars for swing trading. If traders use smaller lower bounds, Ehlers advises a minimum of eight bars to minimize the potential effects of aliasing. Data length: The Griffiths predictor can use a relatively small data length, as autocorrelation diminishes rapidly with lag. However, for optimal spectrum and dominant cycle calculations, the length must match or exceed the upper bound of the bandpass filter. Ehlers recommends avoiding excessively long lengths to maintain responsiveness to shorter-term cycles.
Pine Script®指標
由PineCodersTASC提供
12
BAERMThe Bitcoin Auto-correlation Exchange Rate Model: A Novel Two Step Approach THIS IS NOT FINANCIAL ADVICE. THIS ARTICLE IS FOR EDUCATIONAL AND ENTERTAINMENT PURPOSES ONLY. If you enjoy this software and information, please consider contributing to my lightning address Prelude It has been previously established that the Bitcoin daily USD exchange rate series is extremely auto-correlated In this article, we will utilise this fact to build a model for Bitcoin/USD exchange rate. But not a model for predicting the exchange rate, but rather a model to understand the fundamental reasons for the Bitcoin to have this exchange rate to begin with. This is a model of sound money, scarcity and subjective value. Introduction Bitcoin, a decentralised peer to peer digital value exchange network, has experienced significant exchange rate fluctuations since its inception in 2009. In this article, we explore a two-step model that reasonably accurately captures both the fundamental drivers of Bitcoin’s value and the cyclical patterns of bull and bear markets. This model, whilst it can produce forecasts, is meant more of a way of understanding past exchange rate changes and understanding the fundamental values driving the ever increasing exchange rate. The forecasts from the model are to be considered inconclusive and speculative only. Data preparation To develop the BAERM, we used historical Bitcoin data from Coin Metrics, a leading provider of Bitcoin market data. The dataset includes daily USD exchange rates, block counts, and other relevant information. We pre-processed the data by performing the following steps: Fixing date formats and setting the dataset’s time index Generating cumulative sums for blocks and halving periods Calculating daily rewards and total supply Computing the log-transformed price Step 1: Building the Base Model To build the base model, we analysed data from the first two epochs (time periods between Bitcoin mining reward halvings) and regressed the logarithm of Bitcoin’s exchange rate on the mining reward and epoch. This base model captures the fundamental relationship between Bitcoin’s exchange rate, mining reward, and halving epoch. where Yt represents the exchange rate at day t, Epochk is the kth epoch (for that t), and epsilont is the error term. The coefficients beta0, beta1, and beta2 are estimated using ordinary least squares regression. Base Model Regression We use ordinary least squares regression to estimate the coefficients for the betas in figure 2. In order to reduce the possibility of over-fitting and ensure there is sufficient out of sample for testing accuracy, the base model is only trained on the first two epochs. You will notice in the code we calculate the beta2 variable prior and call it “phaseplus”. The code below shows the regression for the base model coefficients: \# Run the regression mask = df\ < 2 # we only want to use Epoch's 0 and 1 to estimate the coefficients for the base model reg\_X = df.loc\ [mask, \ \].shift(1).iloc\ reg\_y = df.loc\ .iloc\ reg\_X = sm.add\_constant(reg\_X) ols = sm.OLS(reg\_y, reg\_X).fit() coefs = ols.params.values print(coefs) The result of this regression gives us the coefficients for the betas of the base model: \ or in more human readable form: 0.029, 0.996869586, -0.00043. NB that for the auto-correlation/momentum beta, we did NOT round the significant figures at all. Since the momentum is so important in this model, we must use all available significant figures. Fundamental Insights from the Base Model Momentum effect: The term 0.997 Y suggests that the exchange rate of Bitcoin on a given day (Yi) is heavily influenced by the exchange rate on the previous day. This indicates a momentum effect, where the price of Bitcoin tends to follow its recent trend. Momentum effect is a phenomenon observed in various financial markets, including stocks and other commodities. It implies that an asset’s price is more likely to continue moving in its current direction, either upwards or downwards, over the short term. The momentum effect can be driven by several factors: Behavioural biases: Investors may exhibit herding behaviour or be subject to cognitive biases such as confirmation bias, which could lead them to buy or sell assets based on recent trends, reinforcing the momentum. Positive feedback loops: As more investors notice a trend and act on it, the trend may gain even more traction, leading to a self-reinforcing positive feedback loop. This can cause prices to continue moving in the same direction, further amplifying the momentum effect. Technical analysis: Many traders use technical analysis to make investment decisions, which often involves studying historical exchange rate trends and chart patterns to predict future exchange rate movements. When a large number of traders follow similar strategies, their collective actions can create and reinforce exchange rate momentum. Impact of halving events: In the Bitcoin network, new bitcoins are created as a reward to miners for validating transactions and adding new blocks to the blockchain. This reward is called the block reward, and it is halved approximately every four years, or every 210,000 blocks. This event is known as a halving. The primary purpose of halving events is to control the supply of new bitcoins entering the market, ultimately leading to a capped supply of 21 million bitcoins. As the block reward decreases, the rate at which new bitcoins are created slows down, and this can have significant implications for the price of Bitcoin. The term -0.0004*(50/(2^epochk) — (epochk+1)²) accounts for the impact of the halving events on the Bitcoin exchange rate. The model seems to suggest that the exchange rate of Bitcoin is influenced by a function of the number of halving events that have occurred. Exponential decay and the decreasing impact of the halvings: The first part of this term, 50/(2^epochk), indicates that the impact of each subsequent halving event decays exponentially, implying that the influence of halving events on the Bitcoin exchange rate diminishes over time. This might be due to the decreasing marginal effect of each halving event on the overall Bitcoin supply as the block reward gets smaller and smaller. This is antithetical to the wrong and popular stock to flow model, which suggests the opposite. Given the accuracy of the BAERM, this is yet another reason to question the S2F model, from a fundamental perspective. The second part of the term, (epochk+1)², introduces a non-linear relationship between the halving events and the exchange rate. This non-linear aspect could reflect that the impact of halving events is not constant over time and may be influenced by various factors such as market dynamics, speculation, and changing market conditions. The combination of these two terms is expressed by the graph of the model line (see figure 3), where it can be seen the step from each halving is decaying, and the step up from each halving event is given by a parabolic curve. NB - The base model has been trained on the first two halving epochs and then seeded (i.e. the first lag point) with the oldest data available. Constant term: The constant term 0.03 in the equation represents an inherent baseline level of growth in the Bitcoin exchange rate. In any linear or linear-like model, the constant term, also known as the intercept or bias, represents the value of the dependent variable (in this case, the log-scaled Bitcoin USD exchange rate) when all the independent variables are set to zero. The constant term indicates that even without considering the effects of the previous day’s exchange rate or halving events, there is a baseline growth in the exchange rate of Bitcoin. This baseline growth could be due to factors such as the network’s overall growth or increasing adoption, or changes in the market structure (more exchanges, changes to the regulatory environment, improved liquidity, more fiat on-ramps etc). Base Model Regression Diagnostics Below is a summary of the model generated by the OLS function OLS Regression Results \============================================================================== Dep. Variable: logprice R-squared: 0.999 Model: OLS Adj. R-squared: 0.999 Method: Least Squares F-statistic: 2.041e+06 Date: Fri, 28 Apr 2023 Prob (F-statistic): 0.00 Time: 11:06:58 Log-Likelihood: 3001.6 No. Observations: 2182 AIC: -5997. Df Residuals: 2179 BIC: -5980. Df Model: 2 Covariance Type: nonrobust \============================================================================== coef std err t P>|t| \ \------------------------------------------------------------------------------ const 0.0292 0.009 3.081 0.002 0.011 0.048 logprice 0.9969 0.001 1012.724 0.000 0.995 0.999 phaseplus -0.0004 0.000 -2.239 0.025 -0.001 -5.3e-05 \============================================================================== Omnibus: 674.771 Durbin-Watson: 1.901 Prob(Omnibus): 0.000 Jarque-Bera (JB): 24937.353 Skew: -0.765 Prob(JB): 0.00 Kurtosis: 19.491 Cond. No. 255. \============================================================================== Below we see some regression diagnostics along with the regression itself. Diagnostics: We can see that the residuals are looking a little skewed and there is some heteroskedasticity within the residuals. The coefficient of determination, or r2 is very high, but that is to be expected given the momentum term. A better r2 is manually calculated by the sum square of the difference of the model to the untrained data. This can be achieved by the following code: \# Calculate the out-of-sample R-squared oos\_mask = df\ >= 2 oos\_actual = df.loc\ oos\_predicted = df.loc\ residuals\_oos = oos\_actual - oos\_predicted SSR = np.sum(residuals\_oos \*\* 2) SST = np.sum((oos\_actual - oos\_actual.mean()) \*\* 2) R2\_oos = 1 - SSR/SST print("Out-of-sample R-squared:", R2\_oos) The result is: 0.84, which indicates a very close fit to the out of sample data for the base model, which goes some way to proving our fundamental assumption around subjective value and sound money to be accurate. Step 2: Adding the Damping Function Next, we incorporated a damping function to capture the cyclical nature of bull and bear markets. The optimal parameters for the damping function were determined by regressing on the residuals from the base model. The damping function enhances the model’s ability to identify and predict bull and bear cycles in the Bitcoin market. The addition of the damping function to the base model is expressed as the full model equation. This brings me to the question — why? Why add the damping function to the base model, which is arguably already performing extremely well out of sample and providing valuable insights into the exchange rate movements of Bitcoin. Fundamental reasoning behind the addition of a damping function: Subjective Theory of Value: The cyclical component of the damping function, represented by the cosine function, can be thought of as capturing the periodic fluctuations in market sentiment. These fluctuations may arise from various factors, such as changes in investor risk appetite, macroeconomic conditions, or technological advancements. Mathematically, the cyclical component represents the frequency of these fluctuations, while the phase shift (α and β) allows for adjustments in the alignment of these cycles with historical data. This flexibility enables the damping function to account for the heterogeneity in market participants’ preferences and expectations, which is a key aspect of the subjective theory of value. Time Preference and Market Cycles: The exponential decay component of the damping function, represented by the term e^(-0.0004t), can be linked to the concept of time preference and its impact on market dynamics. In financial markets, the discounting of future cash flows is a common practice, reflecting the time value of money and the inherent uncertainty of future events. The exponential decay in the damping function serves a similar purpose, diminishing the influence of past market cycles as time progresses. This decay term introduces a time-dependent weight to the cyclical component, capturing the dynamic nature of the Bitcoin market and the changing relevance of past events. Interactions between Cyclical and Exponential Decay Components: The interplay between the cyclical and exponential decay components in the damping function captures the complex dynamics of the Bitcoin market. The damping function effectively models the attenuation of past cycles while also accounting for their periodic nature. This allows the model to adapt to changing market conditions and to provide accurate predictions even in the face of significant volatility or structural shifts. Now we have the fundamental reasoning for the addition of the function, we can explore the actual implementation and look to other analogies for guidance — Financial and physical analogies to the damping function: Mathematical Aspects: The exponential decay component, e^(-0.0004t), attenuates the amplitude of the cyclical component over time. This attenuation factor is crucial in modelling the diminishing influence of past market cycles. The cyclical component, represented by the cosine function, accounts for the periodic nature of market cycles, with α determining the frequency of these cycles and β representing the phase shift. The constant term (+3) ensures that the function remains positive, which is important for practical applications, as the damping function is added to the rest of the model to obtain the final predictions. Analogies to Existing Damping Functions: The damping function in the BAERM is similar to damped harmonic oscillators found in physics. In a damped harmonic oscillator, an object in motion experiences a restoring force proportional to its displacement from equilibrium and a damping force proportional to its velocity. The equation of motion for a damped harmonic oscillator is: x’’(t) + 2γx’(t) + ω₀²x(t) = 0 where x(t) is the displacement, ω₀ is the natural frequency, and γ is the damping coefficient. The damping function in the BAERM shares similarities with the solution to this equation, which is typically a product of an exponential decay term and a sinusoidal term. The exponential decay term in the BAERM captures the attenuation of past market cycles, while the cosine term represents the periodic nature of these cycles. Comparisons with Financial Models: In finance, damped oscillatory models have been applied to model interest rates, stock prices, and exchange rates. The famous Black-Scholes option pricing model, for instance, assumes that stock prices follow a geometric Brownian motion, which can exhibit oscillatory behavior under certain conditions. In fixed income markets, the Cox-Ingersoll-Ross (CIR) model for interest rates also incorporates mean reversion and stochastic volatility, leading to damped oscillatory dynamics. By drawing on these analogies, we can better understand the technical aspects of the damping function in the BAERM and appreciate its effectiveness in modelling the complex dynamics of the Bitcoin market. The damping function captures both the periodic nature of market cycles and the attenuation of past events’ influence. Conclusion In this article, we explored the Bitcoin Auto-correlation Exchange Rate Model (BAERM), a novel 2-step linear regression model for understanding the Bitcoin USD exchange rate. We discussed the model’s components, their interpretations, and the fundamental insights they provide about Bitcoin exchange rate dynamics. The BAERM’s ability to capture the fundamental properties of Bitcoin is particularly interesting. The framework underlying the model emphasises the importance of individuals’ subjective valuations and preferences in determining prices. The momentum term, which accounts for auto-correlation, is a testament to this idea, as it shows that historical price trends influence market participants’ expectations and valuations. This observation is consistent with the notion that the price of Bitcoin is determined by individuals’ preferences based on past information. Furthermore, the BAERM incorporates the impact of Bitcoin’s supply dynamics on its price through the halving epoch terms. By acknowledging the significance of supply-side factors, the model reflects the principles of sound money. A limited supply of money, such as that of Bitcoin, maintains its value and purchasing power over time. The halving events, which reduce the block reward, play a crucial role in making Bitcoin increasingly scarce, thus reinforcing its attractiveness as a store of value and a medium of exchange. The constant term in the model serves as the baseline for the model’s predictions and can be interpreted as an inherent value attributed to Bitcoin. This value emphasizes the significance of the underlying technology, network effects, and Bitcoin’s role as a medium of exchange, store of value, and unit of account. These aspects are all essential for a sound form of money, and the model’s ability to account for them further showcases its strength in capturing the fundamental properties of Bitcoin. The BAERM offers a potential robust and well-founded methodology for understanding the Bitcoin USD exchange rate, taking into account the key factors that drive it from both supply and demand perspectives. In conclusion, the Bitcoin Auto-correlation Exchange Rate Model provides a comprehensive fundamentally grounded and hopefully useful framework for understanding the Bitcoin USD exchange rate.
Pine Script®指標
由ciphernom提供
RSI3M3+ v.1.8RSI3M3+ v.1.8 Indicator This script is an advanced trading indicator based on Walter J. Bressert's cycle analysis methodology, combined with an RSI (Relative Strength Index) variation. Let me break it down and explain how it works. Core Concepts The RSI3M3+ indicator combines: A short-term RSI (3-period) A 3-period moving average to smooth the RSI Bressert's cycle analysis principles to identify optimal trading points RSI3M3+ Indicator VisualizationImage Walter J. Bressert's Cycle Analysis Concepts Walter Bressert was a pioneer in cycle analysis trading who believed markets move in cyclical patterns that can be measured and predicted. His key principles integrated into this indicator include: Trading Cycles: Markets move in cycles with measurable time spans from low to low Timing Bands: Projected periods when the next cyclical low or high is anticipated Oscillator Use: Using oscillators like RSI to confirm cycle position Entry/Exit Rules: Specific rules for trade entry and exit based on cycle position Key Parameters in the Script Basic RSI Parameters Required bars: Minimum number of bars needed (default: 20) Overbought region: RSI level considered overbought (default: 70) Oversold region: RSI level considered oversold (default: 30) Bressert-Specific Parameters Cycle Detection Length: Lookback period for cycle identification (default: 30) Minimum/Maximum Cycle Length: Expected cycle duration in days (default: 15-30) Buy Line: Lower threshold for buy signals (default: 40) Sell Line: Upper threshold for sell signals (default: 60) How the Indicator Works RSI3M3 Calculation: Calculates a 3-period RSI (sRSI) Smooths it with a 3-period moving average (sMA) Cycle Detection: Identifies bottoms: When the RSI is below the buy line (40) and starting to turn up Identifies tops: When the RSI is above the sell line (60) and starting to turn down Records these points to calculate cycle lengths Timing Bands: Projects when the next cycle bottom or top should occur Creates visual bands on the chart showing these expected time windows Signal Generation: Buy signals occur when the RSI turns up from below the oversold level (30) Sell signals occur when the RSI turns down from above the overbought level (70) Enhanced by Bressert's specific timing rules Bressert's Five Trading Rules (Implemented in the Script) Cycle Timing: The low must be 15-30 market days from the previous Trading Cycle bottom Prior Top Validation: A Trading Cycle high must have occurred with the oscillator above 60 Oscillator Behavior: The oscillator must drop below 40 and turn up Entry Trigger: Entry is triggered by a rise above the price high of the upturn day Protective Stop: Place stop slightly below the Trading Cycle low (implemented as 99% of bottom price) How to Use the Indicator Reading the Chart Main Plot Area: Green line: 3-period RSI Red line: 3-period moving average of the RSI Horizontal bands: Oversold (30) and Overbought (70) regions Dotted lines: Buy line (40) and Sell line (60) Yellow vertical bands: Projected timing windows for next cycle bottom Signals: Green up arrows: Buy signals Red down arrows: Sell signals Trading Strategy For Buy Signals: Wait for the RSI to drop below the buy line (40) Look for an upturn in the RSI from below this level Enter the trade when price rises above the high of the upturn day Place a protective stop at 99% of the Trading Cycle low For Sell Signals: Wait for the RSI to rise above the sell line (60) Look for a downturn in the RSI from above this level Consider exiting or taking profits when a sell signal appears Alternative exit: When price moves below the low of the downturn day Cycle Timing Enhancement: Pay attention to the yellow timing bands Signals occurring within these bands have higher probability of success Signals outside these bands may be less reliable Practical Tips for Using RSI3M3+ Timeframe Selection: The indicator works best on daily charts for intermediate-term trading Can be used on weekly charts for longer-term position trading On intraday charts, adjust cycle lengths accordingly Market Applicability: Works well in trending markets with clear cyclical behavior Less effective in choppy, non-trending markets Consider additional indicators for trend confirmation Parameter Adjustment: Different markets may have different natural cycle lengths You may need to adjust the min/max cycle length parameters Higher volatility markets may need wider overbought/oversold levels Trade Management: Enter trades when all Bressert's conditions are met Use the protective stop as defined (99% of cycle low) Consider taking partial profits at the projected cycle high timing Advanced Techniques Multiple Timeframe Analysis: Confirm signals with the same indicator on higher timeframes Enter in the direction of the larger cycle when smaller and larger cycles align Divergence Detection: Look for price making new lows while RSI makes higher lows (bullish) Look for price making new highs while RSI makes lower highs (bearish) Confluence with Price Action: Combine with support/resistance levels Use with candlestick patterns for confirmation Consider volume confirmation of cycle turns This RSI3M3+ indicator combines the responsiveness of a short-term RSI with the predictive power of Bressert's cycle analysis, offering traders a sophisticated tool for identifying high-probability trading opportunities based on market cycles and momentum shifts. THANK YOU FOR PREVIOUS CODER THAT EFFORT TO CREATE THE EARLIER VERSION THAT MAKE WALTER J BRESSERT CONCEPT IN TRADINGVIEW @ADutchTourist
Pine Script®指標
由kromut76提供
14
Quarterly Theory ICT 04 [TradingFinder] SSMT 4Quarter Divergence🔵 Introduction Sequential SMT Divergence is an advanced price-action-based analytical technique rooted in the ICT (Inner Circle Trader) methodology. Its primary objective is to identify early-stage divergences between correlated assets within precise time structures. This tool not only breaks down market structure but also enables traders to detect engineered liquidity traps before the market reacts. In simple terms, SMT (Smart Money Technique) occurs when two correlated assets—such as indices (ES and NQ), currency pairs (EURUSD and GBPUSD), or commodities (Gold and Silver)—exhibit different reactions at key price levels (swing highs or lows). This lack of alignment is often a sign of smart money manipulation and signals a lack of confirmation in the ongoing trend—hinting at an imminent reversal or at least a pause in momentum. In its Sequential form, SMT divergences are examined through a more granular temporal lens—between intraday quarters (Q1 through Q4). When SMT appears at the transition from one quarter to another (e.g., Q1 to Q2 or Q3 to Q4), the signal becomes significantly more powerful, often aligning with a critical phase in the Quarterly Theory—a framework that segments market behavior into four distinct phases: Accumulation, Manipulation, Distribution, and Reversal/Continuation. For instance, a Bullish SMT forms when one asset prints a new low while its correlated counterpart fails to break the corresponding low from the previous quarter. This usually indicates absorption of selling pressure and the beginning of accumulation by smart money. Conversely, a Bearish SMT arises when one asset makes a higher high, but the second asset fails to confirm, signaling distribution or a fake-out before a decline. However, SMT alone is not enough. To confirm a true Market Structure Break (MSB), the appearance of a Precision Swing Point (PSP) is essential—a specific candlestick formation on a lower timeframe (typically 5 to 15 minutes) that reveals the entry of institutional participants. The combination of SMT and PSP provides a more accurate entry point and better understanding of premium and discount zones. The Sequential SMT Indicator, introduced in this article, dynamically scans charts for such divergence patterns across multiple sessions. It is applicable to various markets including Forex, crypto, commodities, and indices, and shows particularly strong performance during mid-week sessions (Wednesdays and Thursdays)—when most weekly highs and lows tend to form. Bullish Sequential SMT : Bearish Sequential SMT : 🔵 How to Use The Sequential SMT (SSMT) indicator is designed to detect time and structure-based divergences between two correlated assets. This divergence occurs when both assets print a similar swing (high or low) in the previous quarter (e.g., Q3), but in the current quarter (e.g., Q4), only one asset manages to break that swing level—while the other fails to reach it. This temporal mismatch is precisely identified by the SSMT indicator and often signals smart money activity, a market phase transition, or even the presence of an engineered liquidity trap. The signal becomes especially powerful when paired with a Precision Swing Point (PSP)—a confirming candle on lower timeframes (5m–15m) that typically indicates a market structure break (MSB) and the entry of smart liquidity. 🟣 Bullish Sequential SMT In the previous quarter, both assets form a similar swing low. In the current quarter, one asset (e.g., EURUSD) breaks that low and trades below it. The other asset (e.g., GBPUSD) fails to reach the same low, preserving the structure. This time-based divergence reflects declining selling pressure, potential absorption, and often marks the end of a manipulation phase and the start of accumulation. If confirmed by a bullish PSP candle, it offers a strong long opportunity, with stop-losses defined just below the swing low. 🟣 Bearish Sequential SMT In the previous quarter, both assets form a similar swing high. In the current quarter, one asset (e.g., NQ) breaks above that high. The other asset (e.g., ES) fails to reach that high, remaining below it. This type of divergence signals weakening bullish momentum and the likelihood of distribution or a fake-out before a price drop. When followed by a bearish PSP candle, it sets up a strong shorting opportunity with targets in the discount zone and protective stops placed above the swing high. 🔵 Settings ⚙️ Logical Settings Quarterly Cycles Type : Select the time segmentation method for SMT analysis. Available modes include: Yearly, Monthly, Weekly, Daily, 90 Minute, and Micro. These define how the indicator divides market time into Q1–Q4 cycles. Symbol : Choose the secondary asset to compare with the main chart asset (e.g., XAUUSD, US100, GBPUSD). Pivot Period : Sets the sensitivity of the pivot detection algorithm. A smaller value increases responsiveness to price swings. Activate Max Pivot Back : When enabled, limits the maximum number of past pivots to be considered for divergence detection. Max Pivot Back Length : Defines how many past pivots can be used (if the above toggle is active). Pivot Sync Threshold : The maximum allowed difference (in bars) between pivots of the two assets for them to be compared. Validity Pivot Length : Defines the time window (in bars) during which a divergence remains valid before it's considered outdated. 🎨 Display Settings Show Cycle :Toggles the visual display of the current Quarter (Q1 to Q4) based on the selected time segmentation Show Cycle Label : Shows the name (e.g., "Q2") of each detected Quarter on the chart. Show Bullish SMT Line : Draws a line connecting the bullish divergence points. Show Bullish SMT Label : Displays a label on the chart when a bullish divergence is detected. Bullish Color : Sets the color for bullish SMT markers (label, shape, and line). Show Bearish SMT Line : Draws a line for bearish divergence. Show Bearish SMT Label : Displays a label when a bearish SMT divergence is found. Bearish Color : Sets the color for bearish SMT visual elements. 🔔 Alert Settings Alert Name : Custom name for the alert messages (used in TradingView’s alert system). Message Frequency : All: Every signal triggers an alert. Once Per Bar: Alerts once per bar regardless of how many signals occur. Per Bar Close: Only triggers when the bar closes and the signal still exists. Time Zone Display : Choose the time zone in which alert timestamps are displayed (e.g., UTC). Bullish SMT Divergence Alert : Enable/disable alerts specifically for bullish signals. Bearish SMT Divergence Alert : Enable/disable alerts specifically for bearish signals 🔵 Conclusion The Sequential SMT (SSMT) indicator is a powerful and precise tool for identifying structural divergences between correlated assets within a time-based framework. Unlike traditional divergence models that rely solely on sequential pivot comparisons, SSMT leverages Quarterly Theory, in combination with concepts like liquidity sweeps, market structure breaks (MSB) and precision swing points (PSP), to provide a deeper and more actionable view of market dynamics. By using SSMT, traders gain not only the ability to identify where divergence occurs, but also when it matters most within the market cycle. This empowers them to anticipate major moves or traps before they fully materialize, and position themselves accordingly in high-probability trade zones. Whether you're trading Forex, crypto, indices, or commodities, the true strength of this indicator is revealed when used in sync with the Accumulation, Manipulation, Distribution, and Reversal phases of the market. Integrated with other confluence tools and market models, SSMT can serve as a core component in a professional, rule-based, and highly personalized trading strategy.
Pine Script®指標
由TFlab提供
7
Hurst Future Lines of Demarcation StrategyJ. M. Hurst introduced a concept in technical analysis known as the Future Line of Demarcation (FLD), which serves as a forward-looking tool by incorporating a simple yet profound line into future projections on a financial chart. Specifically, the FLD is constructed by offsetting the price half a cycle ahead into the future on the time axis, relative to the Hurst Cycle of interest. For instance, in the context of a 40 Day Cycle, the FLD would be represented by shifting the current price data 20 days forward on the chart, offering an idea of future price movement anticipations. The utility of FLDs extends into three critical areas of insight, which form the backbone of the FLD Trading Strategy: A price crossing the FLD signifies the confirmation of either a peak or trough formation, indicating pivotal moments in price action. Such crossings also help determine precise price targets for the upcoming peak or trough, aligned with the cycle of examination. Additionally, the occurrence of a peak in the FLD itself signals a probable zone where the price might experience a trough, helping to anticipate of future price movements. These insights by Hurst in his "Cycles Trading Course" during the 1970s, are instrumental for traders aiming to determine entry and exit points, and to forecast potential price movements within the market. To use the FLD Trading Strategy, for example when focusing on the 40 Day Cycle, a trader should primarily concentrate on the interplay between three Hurst Cycles: The 20 Day FLD (Signal) - Half the length of the Trade Cycle The 40 Day FLD (Trade) - The Cycle you want to trade The 80 Day FLD (Trend) - Twice the length of the Trade Cycle Traders can gauge trend or consolidation by watching for two critical patterns: Cascading patterns, characterized by several FLDs running parallel with a consistent separation, typically emerge during pronounced market trends, indicating strong directional momentum. Consolidation patterns, on the other hand, occur when multiple FLDs intersect and navigate within the same price bandwidth, often reversing direction to traverse this range multiple times. This tangled scenario results in the formation of Pause Zones, areas where price momentum is likely to temporarily stall or where the emergence of a significant trend might be delayed. This simple FLD indicator provides 3 FLDs with optional source input and smoothing, A-through-H FLD interaction background, adjustable “Close the Trade” triggers, and a simple strategy for backtesting it all. The A-through-H FLD interactions are a framework designed to classify the different types of price movements as they intersect with or diverge from the Future Line of Demarcation (FLD). Each interaction (designated A through H by color) represents a specific phase or characteristic within the cycle, and understanding these can help traders anticipate future price movements and make informed decisions. The adjustable “Close the Trade” triggers are for setting the crossover/under that determines the trade exits. The options include: Price, Signal FLD, Trade FLD, or Trend FLD. For example, a trader may want to exit trades only when price finally crosses the Trade FLD line. Shoutouts & Credits for all the raw code, helpful information, ideas & collaboration, conversations together, introductions, indicator feedback, and genuine/selfless help: 🏆 @TerryPascoe 🏅 @Hpotter 👏 @parisboy
Pine Script®策略
由BarefootJoey提供
4
PA-Adaptive Hull Parabolic [Loxx]The PA-Adaptive Hull Parabolic is not your typical trading indicator. It synthesizes the computational brilliance of two famed technicians: John Ehlers and John Hull. Let's demystify its sophistication. █ Ehlers' Phase Accumulation John Ehlers is well-known in the trading community for his digital signal processing approach to market data. One of his standout techniques is phase accumulation. This method identifies the dominant cycle in the market by accumulating the phases of individual cycles. By doing so, it "adapts" to real-time market conditions. Here's the brilliance of phase accumulation in this code The indicator doesn't merely use a static look-back period. Instead, it dynamically determines the dominant market cycle through phase accumulation. The calcComp function, rooted in Ehlers' methodology, provides a complex computation using a digital signal processing approach to filter out market noise and pinpoint the current cycle's frequency. By measuring and adapting to the instantaneous period of the market, it ensures that the indicator remains relevant, especially in non-stationary market conditions. Hull's Moving Average John Hull introduced the Hull Moving Average (HMA) aiming to reduce lag and improve smoothing. The HMA's essence lies in its weighted average computation, prioritizing more recent prices. This code takes an adaptive twist on the HMA Instead of a fixed period, the HMA uses the dominant cycle length derived from Ehlers' phase accumulation. This makes the HMA not just fast and smooth, but also adaptive to the dominant market rhythm. The intricate iLwmp function in the script provides this adaptive HMA computation. It's a weighted moving average, but its length isn't static; it's based on the previously determined dominant market cycle. █ Trading Insights The indicator paints the bars to represent the immediate trend: green for bullish and red for bearish. Entry points, both long ("L") and short ("S"), are presented visually. These are derived from crossovers of the adaptive HMA, a clear indication of a potential shift in the trend. Additionally, alert conditions are set, ready to notify a trader when these crossovers occur, ensuring real-time actionable insights. █ Conclusion The PA-Adaptive Hull Parabolic is a masterclass in advanced technical indicator design. By marrying John Ehlers' adaptive phase accumulation with John Hull's HMA, it creates a dynamic, responsive, and precise tool for traders. It's not just about capturing the trend; it's about understanding the very rhythm of the market.
Pine Script®指標
由loxx提供
10
[GYTS-CE] Market Regime Detector🧊 Market Regime Detector (Community Edition) 🌸 Part of GoemonYae Trading System (GYTS) 🌸 🌸 --------- INTRODUCTION --------- 🌸 💮 What is the Market Regime Detector? The Market Regime Detector is an advanced, consensus-based indicator that identifies the current market state to increase the probability of profitable trades. By distinguishing between trending (bullish or bearish) and cyclic (range-bound) market conditions, this detector helps you select appropriate tactics for different environments. Instead of forcing a single strategy across all market conditions, our detector allows you to adapt your approach based on real-time market behaviour. 💮 The Importance of Market Regimes Markets constantly shift between different behavioural states or "regimes": • Bullish trending markets - characterised by sustained upward price movement • Bearish trending markets - characterised by sustained downward price movement • Cyclic markets - characterised by range-bound, oscillating behaviour Each regime requires fundamentally different trading approaches. Trend-following strategies excel in trending markets but fail in cyclic ones, while mean-reversion strategies shine in cyclic markets but underperform in trending conditions. Detecting these regimes is essential for successful trading, which is why we've developed the Market Regime Detector to accurately identify market states using complementary detection methods. 🌸 --------- KEY FEATURES --------- 🌸 💮 Consensus-Based Detection Rather than relying on a single method, our detector employs two complementary detection methodologies that analyse different aspects of market behaviour: • Dominant Cycle Average (DCA) - analyzes price movement relative to its lookback period, a proxy for the dominant cycle • Volatility Channel - examines price behaviour within adaptive volatility bands These diverse perspectives are synthesised into a robust consensus that minimises false signals while maintaining responsiveness to genuine regime changes. 💮 Dominant Cycle Framework The Market Regime Detector uses the concept of dominant cycles to establish a reference framework. You can input the dominant cycle period that best represents the natural rhythm of your market, providing a stable foundation for regime detection across different timeframes. 💮 Intuitive Parameter System We've distilled complex technical parameters into intuitive controls that traders can easily understand: • Adaptability - how quickly the detector responds to changing market conditions • Sensitivity - how readily the detector identifies transitions between regimes • Consensus requirement - how much agreement is needed among detection methods This approach makes the detector accessible to traders of all experience levels while preserving the power of the underlying algorithms. 💮 Visual Market Feedback The detector provides clear visual feedback about the current market regime through: • Colour-coded chart backgrounds (purple shades for bullish, pink for bearish, yellow for cyclic) • Colour-coded price bars • Strength indicators showing the degree of consensus • Customizable colour schemes to match your preferences or trading system 💮 Integration in the GYTS suite The Market Regime Detector is compatible with the GYTS Suite , i.e. it passes the regime into the 🎼 Order Orchestrator where you can set how to trade the trending and cyclic regime. 🌸 --------- CONFIGURATION SETTINGS --------- 🌸 💮 Adaptability Controls how quickly the Market Regime detector adapts to changing market conditions. You can see it as a low-frequency, long-term change parameter: Very Low: Very slow adaptation, most stable but may miss regime changes Low: Slower adaptation, more stability but less responsiveness Normal: Balanced between stability and responsiveness High: Faster adaptation, more responsive but less stable Very High: Very fast adaptation, highly responsive but may generate false signals This setting affects lookback periods and filter parameters across all detection methods. 💮 Sensitivity Controls how sensitive the detector is to market regime transitions. This acts as a high-frequency, short-term change parameter: Very Low: Requires substantial evidence to identify a regime change Low: Less sensitive, reduces false signals but may miss some transitions Normal: Balanced sensitivity suitable for most markets High: More sensitive, detects subtle regime changes but may have more noise Very High: Very sensitive, detects minor fluctuations but may produce frequent changes This setting affects thresholds for regime detection across all methods. 💮 Dominant Cycle Period This parameter allows you to specify the market's natural rhythm in bars. This represents a complete market cycle (up and down movement). Finding the right value for your specific market and timeframe might require some experimentation, but it's a crucial parameter that helps the detector accurately identify regime changes. Most of the times the cycle is between 20 and 40 bars. 💮 Consensus Mode Determines how the signals from both detection methods are combined to produce the final market regime: • Any Method (OR) : Signals bullish/bearish if either method detects that regime. If methods conflict (one bullish, one bearish), the stronger signal wins. More sensitive, catches more regime changes but may produce more false signals. • All Methods (AND) : Signals only when both methods agree on the regime. More conservative, reduces false signals but might miss some legitimate regime changes. • Weighted Decision : Balances both methods with equal weighting. Provides a middle ground between sensitivity and stability. Each mode also calculates a continuous regime strength value that's used for colour intensity in the 'unconstrained' display mode. 💮 Display Mode Choose how to display the market regime colours: • Unconstrained regime: Shows the regime strength as a continuous gradient. This provides more nuanced visualisation where the intensity of the colour indicates the strength of the trend. • Consensus only: Shows only the final consensus regime with fixed colours based on the detected regime type. The background and bar colours will change to indicate the current market regime: • Purple shades: Bullish trending market (darker purple indicates stronger bullish trend) • Pink shades: Bearish trending market (darker pink indicates stronger bearish trend) • Yellow: Cyclic (range-bound) market 💮 Custom Colour Options The Market Regime Detector allows you to customize the colour scheme to match your personal preferences or to coordinate with other indicators: • Use custom colours: Toggle to enable your own colour choices instead of the default scheme • Transparency: Adjust the transparency level of all regime colours • Bullish colours: Define custom colours for strong, medium, weak, and very weak bullish trends • Bearish colours: Define custom colours for strong, medium, weak, and very weak bearish trends • Cyclic colour: Define a custom colour for cyclic (range-bound) market conditions 🌸 --------- DETECTION METHODS --------- 🌸 💮 Dominant Cycle Average (DCA) The Dominant Cycle Average method forms a key part of our detection system: 1. Theoretical Foundation : The DCA method builds on cycle analysis and the observation that in trending markets, price consistently remains on one side of a moving average calculated using the dominant cycle period. In contrast, during cyclic markets, price oscillates around this average. 2. Calculation Process : • We calculate a Simple Moving Average (SMA) using the specified lookback period - a proxy for the dominant cycle period • We then analyse the proportion of time that price spends above or below this SMA over a lookback window. The theory is that the price should cross the SMA each half cycle, assuming that the dominant cycle period is correct and price follows a sinusoid. • This lookback window is adaptive, scaling with the dominant cycle period (controlled by the Adaptability setting) • The different values are standardised and normalised to possess more resolving power and to be more robust to noise. 3. Regime Classification : • When the normalised proportion exceeds a positive threshold (determined by Sensitivity setting), the market is classified as bullish trending • When it falls below a negative threshold, the market is classified as bearish trending • When the proportion remains between these thresholds, the market is classified as cyclic 💮 Volatility Channel The Volatility Channel method complements the DCA method by focusing on price movement relative to adaptive volatility bands: 1. Theoretical Foundation : This method is based on the observation that trending markets tend to sustain movement outside of normal volatility ranges, while cyclic markets tend to remain contained within these ranges. By creating adaptive bands that adjust to current market volatility, we can detect when price behaviour indicates a trending or cyclic regime. 2. Calculation Process : • We first calculate a smooth base channel center using a low pass filter, creating a noise-reduced centreline for price • True Range (TR) is used to measure market volatility, which is then smoothed and scaled by the deviation factor (controlled by Sensitivity) • Upper and lower bands are created by adding and subtracting this scaled volatility from the centreline • Price is smoothed using an adaptive A2RMA filter, which has a very flat and stable behaviour, to reduce noise while preserving trend characteristics • The position of this smoothed price relative to the bands is continuously monitored 3. Regime Classification : • When smoothed price moves above the upper band, the market is classified as bullish trending • When smoothed price moves below the lower band, the market is classified as bearish trending • When price remains between the bands, the market is classified as cyclic • The magnitude of price's excursion beyond the bands is used to determine trend strength 4. Adaptive Behaviour : • The smoothing periods and deviation calculations automatically adjust based on the Adaptability setting • The measured volatility is calculated over a period proportional to the dominant cycle, ensuring the detector works across different timeframes • Both the center line and the bands adapt dynamically to changing market conditions, making the detector responsive yet stable This method provides a unique perspective that complements the DCA approach, with the consensus mechanism synthesising insights from both methods. 🌸 --------- USAGE GUIDE --------- 🌸 💮 Starting with Default Settings The default settings (Normal for Adaptability and Sensitivity, Weighted Decision for Consensus Mode) provide a balanced starting point suitable for most markets and timeframes. Begin by observing how these settings identify regimes in your preferred instruments. 💮 Finding the Optimal Dominant Cycle The dominant cycle period is a critical parameter. Here are some approaches to finding an appropriate value: • Start with typical values, usually something around 25 works well • Visually identify the average distance between significant peaks and troughs • Experiment with different values and observe which provides the most stable regime identification • Consider using cycle-finding indicators to help identify the natural rhythm of your market 💮 Adjusting Parameters • If you notice too many regime changes → Decrease Sensitivity or increase Consensus requirement • If regime changes seem delayed → Increase Adaptability • If a trending regime is not detected, the market is automatically assigned to be in a cyclic state • If you want to see more nuanced regime transitions → Try the "unconstrained" display mode (note that this will not affect the output to other indicators) 💮 Trading Applications Regime-Specific Strategies: • Bullish Trending Regime - Use trend-following strategies, trail stops wider, focus on breakouts, consider holding positions longer, and emphasize buying dips • Bearish Trending Regime - Consider shorts, tighter stops, focus on breakdown points, sell rallies, implement downside protection, and reduce position sizes • Cyclic Regime - Apply mean-reversion strategies, trade range boundaries, apply oscillators, target definable support/resistance levels, and use profit-taking at extremes Strategy Switching: Create a set of rules for each market regime and switch between them based on the detector's signal. This approach can significantly improve performance compared to applying a single strategy across all market conditions. GYTS Suite Integration: • In the GYTS 🎼 Order Orchestrator, select the '🔗 STREAM-int 🧊 Market Regime' as the market regime source • Note that the consensus output (i.e. not the "unconstrained" display) will be used in this stream • Create different strategies for trending (bullish/bearish) and cyclic regimes. The GYTS 🎼 Order Orchestrator is specifically made for this. • The output stream is actually very simple, and can possibly be used in indicators and strategies as well. It outputs 1 for bullish, -1 for bearish and 0 for cyclic regime. 🌸 --------- FINAL NOTES --------- 🌸 💮 Development Philosophy The Market Regime Detector has been developed with several key principles in mind: 1. Robustness - The detection methods have been rigorously tested across diverse markets and timeframes to ensure reliable performance. 2. Adaptability - The detector automatically adjusts to changing market conditions, requiring minimal manual intervention. 3. Complementarity - Each detection method provides a unique perspective, with the collective consensus being more reliable than any individual method. 4. Intuitiveness - Complex technical parameters have been abstracted into easily understood controls. 💮 Ongoing Refinement The Market Regime Detector is under continuous development. We regularly: • Fine-tune parameters based on expanded market data • Research and integrate new detection methodologies • Optimise computational efficiency for real-time analysis Your feedback and suggestions are very important in this ongoing refinement process!
Pine Script®指標
由GoemonYae提供
Adaptive Qualitative Quantitative Estimation (QQE) [Loxx]Adaptive QQE is a fixed and cycle adaptive version of the popular Qualitative Quantitative Estimation (QQE) used by forex traders. This indicator includes varoius types of RSI caculations and adaptive cycle measurements to find tune your signal. Qualitative Quantitative Estimation (QQE): The Qualitative Quantitative Estimation (QQE) indicator works like a smoother version of the popular Relative Strength Index (RSI) indicator. QQE expands on RSI by adding two volatility based trailing stop lines. These trailing stop lines are composed of a fast and a slow moving Average True Range (ATR). There are many indicators for many purposes. Some of them are complex and some are comparatively easy to handle. The QQE indicator is a really useful analytical tool and one of the most accurate indicators. It offers numerous strategies for using the buy and sell signals. Essentially, it can help detect trend reversal and enter the trade at the most optimal positions. Wilders' RSI: The Relative Strength Index ( RSI ) is a well versed momentum based oscillator which is used to measure the speed (velocity) as well as the change (magnitude) of directional price movements. Essentially RSI , when graphed, provides a visual mean to monitor both the current, as well as historical, strength and weakness of a particular market. The strength or weakness is based on closing prices over the duration of a specified trading period creating a reliable metric of price and momentum changes. Given the popularity of cash settled instruments (stock indexes) and leveraged financial products (the entire field of derivatives); RSI has proven to be a viable indicator of price movements. RSX RSI: RSI is a very popular technical indicator, because it takes into consideration market speed, direction and trend uniformity. However, the its widely criticized drawback is its noisy (jittery) appearance. The Jurk RSX retains all the useful features of RSI , but with one important exception: the noise is gone with no added lag. Rapid RSI: Rapid RSI Indicator, from Ian Copsey's article in the October 2006 issue of Stocks & Commodities magazine. RapidRSI resembles Wilder's RSI , but uses a SMA instead of a WilderMA for internal smoothing of price change accumulators. VHF Adaptive Cycle: Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length. Band-pass Adaptive Cycle: 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. Included: -Toggle on/off bar coloring -Customize RSI signal using fixed, VHF Adaptive, and Band-pass Adaptive calculations -Choose from three different RSI types Visuals: -Red/Green line is the moving average of RSI -Thin white line is the fast trend -Dotted yellow line is the slow trend Happy trading!
Pine Script®指標
由loxx提供
4
Bitcoin Polynomial Regression ModelThis is the main version of the script. Click here for the Oscillator part of the script. 💡Why this model was created: One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model). This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here . 📉The theory of diminishing returns: This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles. To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/ 🔧Creation of the model: For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section. This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function: ax^3 +bx^2 + cx + d. In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows: Top regression line (x, y) values: 113, 18.6 240, 1004 451, 19128 655, 65502 Bottom regression line (x, y) values: 103, 2.5 267, 211 471, 3193 676, 16255 The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found: Top regression line parameter values: a: 0.000202798 b: 0.0872922 c: -30.88805 d: 1827.14113 Bottom regression line parameter values: a: 0.000138314 b: -0.0768236 c: 13.90555 d: -765.8892 📊Polynomial Regression Oscillator: This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) . The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula: normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line) This transformation results in a price value between 0 and 1 between both the regression lines. The Oscillator version can be found here. 🔍Interpretation of the Model: In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom. The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom. Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below. 🔮Future Predictions: Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom: In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top. Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026. Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026. For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow: And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
Pine Script®指標
由Cryptonerds提供
1
Bitcoin Polynomial Regression OscillatorThis is the oscillator version of the script. Click here for the other part of the script. 💡Why this model was created: One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model). This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here . 📉The theory of diminishing returns: This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles. To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/ 🔧Creation of the model: For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section. This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function: ax^3 +bx^2 + cx + d. In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows: Top regression line (x, y) values: 113, 18.6 240, 1004 451, 19128 655, 65502 Bottom regression line (x, y) values: 103, 2.5 267, 211 471, 3193 676, 16255 The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found: Top regression line parameter values: a: 0.000202798 b: 0.0872922 c: -30.88805 d: 1827.14113 Bottom regression line parameter values: a: 0.000138314 b: -0.0768236 c: 13.90555 d: -765.8892 📊Polynomial Regression Oscillator: This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) . The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula: normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line) This transformation results in a price value between 0 and 1 between both the regression lines. 🔍Interpretation of the Model: In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom. The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom. Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below. 🔮Future Predictions: Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom: In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top. Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026. Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026. For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow: And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
Pine Script®指標
由Cryptonerds提供
Ehlers Autocorrelation Periodogram [Loxx]Ehlers Autocorrelation Periodogram contains two versions of Ehlers Autocorrelation Periodogram Algorithm. This indicator is meant to supplement adaptive cycle indicators that myself and others have published on Trading View, will continue to publish on Trading View. These are fast-loading, low-overhead, streamlined, exact replicas of Ehlers' work without any other adjustments or inputs. Versions: - 2013, Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers - 2016, TASC September, "Measuring Market Cycles" Description The Ehlers Autocorrelation study is a technical indicator used in the calculation of John F. Ehlers’s Autocorrelation Periodogram. Its main purpose is to eliminate noise from the price data, reduce effects of the “spectral dilation” phenomenon, and reveal dominant cycle periods. The spectral dilation has been discussed in several studies by John F. Ehlers; for more information on this, refer to sources in the "Further Reading" section. As the first step, Autocorrelation uses Mr. Ehlers’s previous installment, Ehlers Roofing Filter, in order to enhance the signal-to-noise ratio and neutralize the spectral dilation. This filter is based on aerospace analog filters and when applied to market data, it attempts to only pass spectral components whose periods are between 10 and 48 bars. Autocorrelation is then applied to the filtered data: as its name implies, this function correlates the data with itself a certain period back. As with other correlation techniques, the value of +1 would signify the perfect correlation and -1, the perfect anti-correlation. Using values of Autocorrelation in Thermo Mode may help you reveal the cycle periods within which the data is best correlated (or anti-correlated) with itself. Those periods are displayed in the extreme colors (orange) while areas of intermediate colors mark periods of less useful cycles. What is an adaptive cycle, and what is the Autocorrelation Periodogram Algorithm? From his Ehlers' book mentioned above, page 135: "Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator.This look-back period is commonly a fixed value. However, since the measured cycle period is changing, as we have seen in previous chapters, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics. The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal." How to use this indicator The point of the Ehlers Autocorrelation Periodogram Algorithm is to dynamically set a period between a minimum and a maximum period length. While I leave the exact explanation of the mechanic to Dr. Ehlers’s book, for all practical intents and purposes, in my opinion, the punchline of this method is to attempt to remove a massive source of overfitting from trading system creation–namely specifying a look-back period. SMA of 50 days? 100 days? 200 days? Well, theoretically, this algorithm takes that possibility of overfitting out of your hands. Simply, specify an upper and lower bound for your look-back, and it does the rest. In addition, this indicator tells you when its best to use adaptive cycle inputs for your other indicators. Usage Example 1 Let's say you're using "Adaptive Qualitative Quantitative Estimation (QQE) ". This indicator has the option of adaptive cycle inputs. When the "Ehlers Autocorrelation Periodogram " shows a period of high correlation that adaptive cycle inputs work best during that period. Usage Example 2 Check where the dominant cycle line lines, grab that output number and inject it into your other standard indicators for the length input.
Pine Script®指標
由loxx提供
5
Bitcoin Macro Trend Map [Ox_kali] ## Introduction __________________________________________________________________________________ The “Bitcoin Macro Trend Map” script is designed to provide a comprehensive analysis of Bitcoin’s macroeconomic trends. By leveraging a unique combination of Bitcoin-specific macroeconomic indicators, this script helps traders identify potential market peaks and troughs with greater accuracy. It synthesizes data from multiple sources to offer a probabilistic view of market excesses, whether overbought or oversold conditions. This script offers significant value for the following reasons: 1. Holistic Market Analysis : It integrates a diverse set of indicators that cover various aspects of the Bitcoin market, from investor sentiment and market liquidity to mining profitability and network health. This multi-faceted approach provides a more complete picture of the market than relying on a single indicator. 2. Customization and Flexibility : Users can customize the script to suit their specific trading strategies and preferences. The script offers configurable parameters for each indicator, allowing traders to adjust settings based on their analysis needs. 3. Visual Clarity : The script plots all indicators on a single chart with clear visual cues. This includes color-coded indicators and background changes based on market conditions, making it easy for traders to quickly interpret complex data. 4. Proven Indicators : The script utilizes well-established indicators like the EMA, NUPL, PUELL Multiple, and Hash Ribbons, which are widely recognized in the trading community for their effectiveness in predicting market movements. 5. A New Comprehensive Indicator : By integrating background color changes based on the aggregate signals of various indicators, this script essentially creates a new, comprehensive indicator tailored specifically for Bitcoin. This visual representation provides an immediate overview of market conditions, enhancing the ability to spot potential market reversals. Optimal for use on timeframes ranging from 1 day to 1 week , the “Bitcoin Macro Trend Map” provides traders with actionable insights, enhancing their ability to make informed decisions in the highly volatile Bitcoin market. By combining these indicators, the script delivers a robust tool for identifying market extremes and potential reversal points. ## Key Indicators __________________________________________________________________________________ Macroeconomic Data: The script combines several relevant macroeconomic indicators for Bitcoin, such as the 10-month EMA, M2 money supply, CVDD, Pi Cycle, NUPL, PUELL, MRVR Z-Scores, and Hash Ribbons (Full description bellow). Open Source Sources: Most of the scripts used are sourced from open-source projects that I have modified to meet the specific needs of this script. Recommended Timeframes: For optimal performance, it is recommended to use this script on timeframes ranging from 1 day to 1 week. Objective: The primary goal is to provide a probabilistic solution to identify market excesses, whether overbought or oversold points. ## Originality and Purpose __________________________________________________________________________________ This script stands out by integrating multiple macroeconomic indicators into a single comprehensive tool. Each indicator is carefully selected and customized to provide insights into different aspects of the Bitcoin market. By combining these indicators, the script offers a holistic view of market conditions, helping traders identify potential tops and bottoms with greater accuracy. This is the first version of the script, and additional macroeconomic indicators will be added in the future based on user feedback and other inputs. ## How It Works __________________________________________________________________________________ The script works by plotting each macroeconomic indicator on a single chart, allowing users to visualize and interpret the data easily. Here’s a detailed look at how each indicator contributes to the analysis: EMA 10 Monthly: Uses an exponential moving average over 10 monthly periods to signal bullish and bearish trends. This indicator helps identify long-term trends in the Bitcoin market by smoothing out price fluctuations to reveal the underlying trend direction.Moving Averages w/ 18 day/week/month. Credit to @ryanman0 M2 Money Supply: Analyzes the evolution of global money supply, indicating market liquidity conditions. This indicator tracks the changes in the total amount of money available in the economy, which can impact Bitcoin’s value as a hedge against inflation or economic instability. Credit to @dylanleclair CVDD (Cumulative Value Days Destroyed): An indicator based on the cumulative value of days destroyed, useful for identifying market turning points. This metric helps assess the Bitcoin market’s health by evaluating the age and value of coins that are moved, indicating potential shifts in market sentiment. Credit to @Da_Prof Pi Cycle: Uses simple and exponential moving averages to detect potential sell points. This indicator aims to identify cyclical peaks in Bitcoin’s price, providing signals for potential market tops. Credit to @NoCreditsLeft NUPL (Net Unrealized Profit/Loss): Measures investors’ unrealized profit or loss to signal extreme market levels. This indicator shows the net profit or loss of Bitcoin holders as a percentage of the market cap, helping to identify periods of significant market optimism or pessimism. Credit to @Da_Prof PUELL Multiple: Assesses mining profitability relative to historical averages to indicate buying or selling opportunities. This indicator compares the daily issuance value of Bitcoin to its yearly average, providing insights into when the market is overbought or oversold based on miner behavior. Credit to @Da_Prof MRVR Z-Scores: Compares market value to realized value to identify overbought or oversold conditions. This metric helps gauge the overall market sentiment by comparing Bitcoin’s market value to its realized value, identifying potential reversal points. Credit to @Pinnacle_Investor Hash Ribbons: Uses hash rate variations to signal buying opportunities based on miner capitulation and recovery. This indicator tracks the health of the Bitcoin network by analyzing hash rate trends, helping to identify periods of miner capitulation and subsequent recoveries as potential buying opportunities. Credit to @ROBO_Trading ## Indicator Visualization and Interpretation __________________________________________________________________________________ For each horizontal line representing an indicator, a legend is displayed on the right side of the chart. If the conditions are positive for an indicator, it will turn green, indicating the end of a bearish trend. Conversely, if the conditions are negative, the indicator will turn red, signaling the end of a bullish trend. The background color of the chart changes based on the average of green or red indicators. This parameter is configurable, allowing adjustment of the threshold at which the background color changes, providing a clear visual indication of overall market conditions. ## Script Parameters __________________________________________________________________________________ The script includes several configurable parameters to customize the display and behavior of the indicators: Color Style: Normal: Default colors. Modern: Modern color style. Monochrome: Monochrome style. User: User-customized colors. Custom color settings for up trends (Up Trend Color), down trends (Down Trend Color), and NaN (NaN Color) Background Color Thresholds: Thresholds: Settings to define the thresholds for background color change. Low/High Red Threshold: Low and high thresholds for bearish trends. Low/High Green Threshold: Low and high thresholds for bullish trends. Indicator Display: Options to show or hide specific indicators such as EMA 10 Monthly, CVDD, Pi Cycle, M2 Money, NUPL, PUELL, MRVR Z-Scores, and Hash Ribbons. Specific Indicator Settings: EMA 10 Monthly: Options to customize the period for the exponential moving average calculation. M2 Money: Aggregation of global money supply data. CVDD: Adjustments for value normalization. Pi Cycle: Settings for simple and exponential moving averages. NUPL: Thresholds for unrealized profit/loss values. PUELL: Adjustments for mining profitability multiples. MRVR Z-Scores: Settings for overbought/oversold values. Hash Ribbons: Options for hash rate moving averages and capitulation/recovery signals. ## Conclusion __________________________________________________________________________________ The “Bitcoin Macro Trend Map” by Ox_kali is a tool designed to analyze the Bitcoin market. By combining several macroeconomic indicators, this script helps identify market peaks and troughs. It is recommended to use it on timeframes from 1 day to 1 week for optimal trend analysis. The scripts used are sourced from open-source projects, modified to suit the specific needs of this analysis. ## Notes __________________________________________________________________________________ This is the first version of the script and it is still in development. More indicators will likely be added in the future. Feedback and comments are welcome to improve this tool. ## Disclaimer: __________________________________________________________________________________ Please note that the Open Interest liquidation map is not a guarantee of future market performance and should be used in conjunction with proper risk management. Always ensure that you have a thorough understanding of the indicator’s methodology and its limitations before making any investment decisions. Additionally, past performance is not indicative of future results.
Pine Script®指標
由Ox_kali提供
1
Ehlers Adaptive Relative Strength Index (RSI) [Loxx]Ehlers Adaptive Relative Strength Index (RSI) is an implementation of RSI using Ehlers Autocorrelation Periodogram Algorithm to derive the length input for RSI. Other implementations of Ehers Adaptive RSI rely on the inferior Hilbert Transformer derive the dominant cycle. In his book "Cycle Analytics for Traders Advanced Technical Trading Concepts", John F. Ehlers describes an implementation for Adaptive Relative Strength Index in order to solve for varying length inputs into the classic RSI equation. What is an adaptive cycle, and what is the Autocorrelation Periodogram Algorithm? From his Ehlers' book mentioned above, page 135: "Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average (KAMA) and Tushar Chande’s variable index dynamic average (VIDYA) adapt to changes in volatility. By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic, relative strength index (RSI), commodity channel index (CCI), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator.This look-back period is commonly a fixed value. However, since the measured cycle period is changing, as we have seen in previous chapters, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics. The dominant cycle period is measured using the autocorrelation periodogram algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal." What is Adaptive RSI? From his Ehlers' book mentioned above, page 137: "The adaptive RSI starts with the computation of the dominant cycle using the autocorrelation periodogram approach. Since the objective is to use only those frequency components passed by the roofing filter, the variable "filt" is used as a data input rather than closing prices. Rather than independently taking the averages of the numerator and denominator, I chose to perform smoothing on the ratio using the SuperSmoother filter. The coefficients for the SuperSmoother filters have previously been computed in the dominant cycle measurement part of the code." Happy trading!
Pine Script®指標
由loxx提供
1
Quarterly Theory ICT 05 [TradingFinder] Doubling Theory Signals🔵 Introduction Doubling Theory is an advanced approach to price action and market structure analysis that uniquely combines time-based analysis with key Smart Money concepts such as SMT (Smart Money Technique), SSMT (Sequential SMT), Liquidity Sweep, and the Quarterly Theory ICT. By leveraging fractal time structures and precisely identifying liquidity zones, this method aims to reveal institutional activity specifically smart money entry and exit points hidden within price movements. At its core, the market is divided into two structural phases: Doubling 1 and Doubling 2. Each phase contains four quarters (Q1 through Q4), which follow the logic of the Quarterly Theory: Accumulation, Manipulation (Judas Swing), Distribution, and Continuation/Reversal. These segments are anchored by the True Open, allowing for precise alignment with cyclical market behavior and providing a deeper structural interpretation of price action. During Doubling 1, a Sequential SMT (SSMT) Divergence typically forms between two correlated assets. This time-structured divergence occurs between two swing points positioned in separate quarters (e.g., Q1 and Q2), where one asset breaks a significant low or high, while the second asset fails to confirm it. This lack of confirmation—especially when aligned with the Manipulation and Accumulation phases—often signals early smart money involvement. Following this, the highest and lowest price points from Doubling 1 are designated as liquidity zones. As the market transitions into Doubling 2, it commonly returns to these zones in a calculated move known as a Liquidity Sweep—a sharp, engineered spike intended to trigger stop orders and pending positions. This sweep, often orchestrated by institutional players, facilitates entry into large positions with minimal slippage. Bullish : Bearish : 🔵 How to Use Applying Doubling Theory requires a simultaneous understanding of temporal structure and inter-asset behavioral divergence. The method unfolds over two main phases—Doubling 1 and Doubling 2—each divided into four quarters (Q1 to Q4). The first phase focuses on identifying a Sequential SMT (SSMT) divergence, which forms when two correlated assets (e.g., EURUSD and GBPUSD, or NQ and ES) react differently to key price levels across distinct quarters. For example, one asset may break a previous low while the other maintains structure. This misalignment—especially in Q2, the Manipulation phase—often indicates early smart money accumulation or distribution. Once this divergence is observed, the extreme highs and lows of Doubling 1 are marked as liquidity zones. In Doubling 2, the market gravitates back toward these zones, executing a Liquidity Sweep. This move is deliberate—designed to activate clustered stop-loss and pending orders and to exploit pockets of resting liquidity. These sweeps are typically driven by institutional forces looking to absorb liquidity and position themselves ahead of the next major price move. The key to execution lies in the fact that, during the sweep in Doubling 2, a classic SMT divergence should also appear between the two assets. This indicates a weakening of the previous trend and adds an extra layer of confirmation. 🟣 Bullish Doubling Theory In the bullish scenario, Doubling 1 begins with a bullish SSMT divergence, where one asset forms a lower low while the other maintains its structure. This divergence signals weakening bearish momentum and possible smart money accumulation. In Doubling 2, the market returns to the previous low and sweeps the liquidity zone—breaking below it on one asset, while the second fails to confirm, forming a bullish SMT divergence. f this move is followed by a bullish PSP and a clear market structure break (MSB), a long entry is triggered. The stop-loss is placed just below the swept liquidity zone, while the target is set in the premium zone, anticipating a move driven by institutional buyers. 🟣 Bearish Doubling Theory The bearish scenario follows the same structure in reverse. In Doubling 1, a bearish SSMT divergence occurs when one asset prints a higher high while the other fails to do so. This suggests distribution and weakening buying pressure. Then, in Doubling 2, the market returns to the previous high and executes a liquidity sweep, targeting trapped buyers. A bearish SMT divergence appears, confirming the move, followed by a bearish PSP on the lower timeframe. A short position is initiated after a confirmed MSB, with the stop-loss placed 🔵 Settings ⚙️ Logical Settings Quarterly Cycles Type : Select the time segmentation method for SMT analysis. Available modes include : Yearly, Monthly, Weekly, Daily, 90 Minute, and Micro. These define how the indicator divides market time into Q1–Q4 cycles. Symbol : Choose the secondary asset to compare with the main chart asset (e.g., XAUUSD, US100, GBPUSD). Pivot Period : Sets the sensitivity of the pivot detection algorithm. A smaller value increases responsiveness to price swings. Pivot Sync Threshold : The maximum allowed difference (in bars) between pivots of the two assets for them to be compared. Validity Pivot Length : Defines the time window (in bars) during which a divergence remains valid before it's considered outdated. 🎨 Display Settings Show Cycle :Toggles the visual display of the current Quarter (Q1 to Q4) based on the selected time segmentation Show Cycle Label : Shows the name (e.g., "Q2") of each detected Quarter on the chart. Show Labels : Displays dynamic labels (e.g., “Q2”, “Bullish SMT”, “Sweep”) at relevant points. Show Lines : Draws connection lines between key pivot or divergence points. Color Settings : Allows customization of colors for bullish and bearish elements (lines, labels, and shapes) 🔔 Alert Settings Alert Name : Custom name for the alert messages (used in TradingView’s alert system). Message Frequenc y: All : Every signal triggers an alert. Once Per Bar : Alerts once per bar regardless of how many signals occur. Per Bar Close : Only triggers when the bar closes and the signal still exists. Time Zone Display : Choose the time zone in which alert timestamps are displayed (e.g., UTC). Bullish SMT Divergence Alert : Enable/disable alerts specifically for bullish signals. Bearish SMT Divergence Alert : Enable/disable alerts specifically for bearish signals 🔵 Conclusion Doubling Theory is a powerful and structured framework within the realm of Smart Money Concepts and ICT methodology, enabling traders to detect high-probability reversal points with precision. By integrating SSMT, SMT, Liquidity Sweeps, and the Quarterly Theory into a unified system, this approach shifts the focus from reactive trading to anticipatory analysis—anchored in time, structure, and liquidity. What makes Doubling Theory stand out is its logical synergy of time cycles, behavioral divergence, liquidity targeting, and institutional confirmation. In both bullish and bearish scenarios, it provides clearly defined entry and exit strategies, allowing traders to engage the market with confidence, controlled risk, and deeper insight into the mechanics of price manipulation and smart money footprints.
Pine Script®指標
由TFlab提供
2
Detrended Rhythm Oscillator (DRO)How to detect the current "market beat" or market cycle? A common way to capture the current dominant cycle length is to detrend the price and look for common rhythms in the detrended series. A common approach is to use a Detrended Price Oscillator (DPO). This is done in order to identify and isolate short-term cycles. A basic DPO description can be found here: www.tradingview.com Improvements to the standard DPO The main purpose of the standard DPO is to analyze historical data in order to observe cycle's in a market's movement. DPO can give the technical analyst a better sense of a cycle's typical high/low range as well as its duration. However, you need to manually try to "see" tops and bottoms on the detrended price and measure manually the distance from low-low or high-high in order to derive a possible cycle length. Therefore, I added the following improvements: 1) Using a DPO to detrend the price 2) Indicate the turns of the detrended price with a ZigZag lines to better see the tops/bottoms 3) Detrend the ZigZag to remove price amplitude between turns to even better see the cyclic turns ("rhythm") 4) Measure the distance from last detrended zigzag pivot (high-high / low-low) and plot the distance in bars above/below the turn Now, you can clearly see the rhythm of the dataset indicated by the Detrended Rhythm Oscillator including the exact length between the turns. This makes the procedure to "spot" turns and "measure" distance more simple for the trader. How to use this information The purpose is to check if there is a common rhythm or beat in the underlying dataset. To check that, look for recurring pattern in the numbers. E.g. if you often see the same measured distance, you can conclude that there is a major dominant cycle in this market. Also watch for harmonic relations between the numbers. So in the example above you see the highlighted cluster of detected length of around 40,80 and 120. There three numbers all have a harmonic relation to 40. Once you have this cyclic information, you can use this number to optimize or tune technical indicators based on the current dominant cycle length. E.g. set the length parameter of a technical indicator to the detected harmonic length with the DRO indicator. Example Use-Case You can use this information to set the input for the following free public open-source script: Disclaimer This is not meant to be a technical indicator on its own and the derived cyclic length should not be used to forecast the next turn per se. The indicator should give you an indication of the current market beat or dominant beats which can be use to further optimize other oscillator or trading related settings. Options & settings The indicator allows to plot different versions. It allows to plot the original DPO, the DRO with ZigZag lines, the DRO with detrended ZigZag lines and length labels on/off. You can turn on or off these version in the indicator settings. So you can tweak it visually to your own needs.
Pine Script®指標
由StockMarketCycles提供
78
Regime Classifier Oscillator (AiBitcoinTrend)The Regime Classifier Oscillator (AiBitcoinTrend) is an advanced tool for understanding market structure and detecting dynamic price regimes. By combining filtered price trends, clustering algorithms, and an adaptive oscillator, it provides traders with detailed insights into market phases, including accumulation, distribution, advancement, and decline. This innovative tool simplifies market regime classification, enabling traders to align their strategies with evolving market conditions effectively. 👽 What is a Regime Classifier, and Why is it Useful? A Regime Classifier is a concept in financial analysis that identifies distinct market conditions or "regimes" based on price behavior and volatility. These regimes often correspond to specific phases of the market, such as trends, consolidations, or periods of high or low volatility. By classifying these regimes, traders and analysts can better understand the underlying market dynamics, allowing them to adapt their strategies to suit prevailing conditions. 👽 Common Uses in Finance Risk Management: Identifying high-volatility regimes helps traders adjust position sizes or hedge risks. Strategy Optimization: Traders tailor their approaches—trend-following strategies in trending regimes, mean-reversion strategies in consolidations. Forecasting: Understanding the current regime aids in predicting potential transitions, such as a shift from accumulation to an upward breakout. Portfolio Allocation: Investors allocate assets differently based on market regimes, such as increasing cash positions in high-volatility environments. 👽 Why It’s Important Markets behave differently under varying conditions. A regime classifier provides a structured way to analyze these changes, offering a systematic approach to decision-making. This improves both accuracy and confidence in navigating diverse market scenarios. 👽 How We Implemented the Regime Classifier in This Indicator The Regime Classifier Oscillator takes the foundational concept of market regime classification and enhances it with advanced computational techniques, making it highly adaptive. 👾 Median Filtering: We smooth price data using a custom median filter to identify significant trends while eliminating noise. This establishes a baseline for price movement analysis. 👾 Clustering Model: Using clustering techniques, the indicator classifies volatility and price trends into distinct regimes: Advance: Strong upward trends with low volatility. Decline: Downward trends marked by high volatility. Accumulation: Consolidation phases with subdued volatility. Distribution: Topping or bottoming patterns with elevated volatility. This classification leverages historical price data to refine cluster boundaries dynamically, ensuring adaptive and accurate detection of market states. Volatility Classification: Price volatility is analyzed through rolling windows, separating data into high and low volatility clusters using distance-based assignments. Price Trends: The interaction of price levels with the filtered trendline and volatility clusters determines whether the market is advancing, declining, accumulating, or distributing. 👽 Dynamic Cycle Oscillator (DCO): Captures cyclic behavior and overlays it with smoothed oscillations, providing real-time feedback on price momentum and potential reversals. Regime Visualization: Regimes are displayed with intuitive labels and background colors, offering clear, actionable insights directly on the chart. 👽 Why This Implementation Stands Out Dynamic and Adaptive: The clustering and refit mechanisms adapt to changing market conditions, ensuring relevance across different asset classes and timeframes. Comprehensive Insights: By combining price trends, volatility, and cyclic behaviors, the indicator provides a holistic view of the market. This implementation bridges the gap between theoretical regime classification and practical trading needs, making it a powerful tool for both novice and experienced traders. 👽 Applications 👾 Regime-Based Trading Strategies Traders can use the regime classifications to adapt their strategies effectively: Advance & Accumulation: Favorable for entering or holding long positions. Decline & Distribution: Opportunities for short positions or risk management. 👾 Oscillator Insights for Trend Analysis Overbought/oversold conditions: Early warning of potential reversals. Dynamic trends: Highlights the strength of price momentum. 👽 Indicator Settings 👾 Filter and Classification Settings Filter Window Size: Controls trend detection sensitivity. ATR Lookback: Adjusts the threshold for regime classification. Clustering Window & Refit Interval: Fine-tunes regime accuracy. 👾 Oscillator Settings Dynamic Cycle Oscillator Lookback: Defines the sensitivity of cycle detection. Smoothing Factor: Balances responsiveness and stability. Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
Pine Script®指標
由aibitcointrend提供
1
Wall Street Cheat Sheet IndicatorThe Wall Street Cheat Sheet Indicator is a unique tool designed to help traders identify the psychological stages of the market cycle based on the well-known Wall Street Cheat Sheet. This indicator integrates moving averages and RSI to dynamically label market stages, providing clear visual cues on the chart. Key Features: Dynamic Stage Identification: The indicator automatically detects and labels market stages such as Disbelief, Hope, Optimism, Belief, Thrill, Euphoria, Complacency, Anxiety, Denial, Panic, Capitulation, Anger, and Depression. These stages are derived from the emotional phases of market participants, helping traders anticipate market movements. Technical Indicators: The script uses two key technical indicators: 200-day Simple Moving Average (SMA): Helps identify long-term market trends. 50-day Simple Moving Average (SMA): Aids in recognizing medium-term trends. Relative Strength Index (RSI): Assesses the momentum and potential reversal points based on overbought and oversold conditions. Clear Visual Labels: The current market stage is displayed directly on the chart, making it easy to spot trends and potential turning points. Usefulness: This indicator is not just a simple mashup of existing tools. It uniquely combines the concept of market psychology with practical technical analysis tools (moving averages and RSI). By labeling the psychological stages of the market cycle, it provides traders with a deeper understanding of market sentiment and potential future movements. How It Works: Disbelief: Detected when the price is below the 200-day SMA and RSI is in the oversold territory, indicating a potential bottom. Hope: Triggered when the price crosses above the 50-day SMA, with RSI starting to rise but still below 50, suggesting an early uptrend. Optimism: Occurs when the price is above the 50-day SMA and RSI is between 50 and 70, indicating a strengthening trend. Belief: When the price is well above the 50-day SMA and RSI is between 70 and 80, showing strong bullish momentum. Thrill and Euphoria: Identified when RSI exceeds 80, indicating overbought conditions and potential for a peak. Complacency to Depression: These stages are identified based on price corrections and drops relative to moving averages and declining RSI values. Best Practices: High-Time Frame Focus: This indicator works best on high-time frame charts, specifically the 1-week Bitcoin (BTCUSDT) chart. The longer time frame provides a clearer picture of the overall market cycle and reduces noise. Trend Confirmation: Use in conjunction with other technical analysis tools such as trendlines, Fibonacci retracement levels, and support/resistance zones for more robust trading strategies. How to Use: Add the Indicator: Apply the Wall Street Cheat Sheet Indicator to your TradingView chart. Analyze Market Stages: Observe the dynamic labels indicating the current stage of the market cycle. Make Informed Decisions: Use the insights from the indicator to time your entries and exits, aligning your trades with the market sentiment. This indicator is a valuable tool for traders looking to understand market psychology and make informed trading decisions based on the stages of the market cycle.
Pine Script®指標
由ladi_o提供
1
TASC 2022.11 Phasor Analysis█ OVERVIEW TASC's November 2022 edition Traders' Tips includes an article by John ​Ehlers titled "Recurring Phase Of Cycle Analysis". This is the code that implements the ​ phasor analysis indicator presented in this publication. █ CONCEPTS The article explores the use of phasor analysis to identify market trends. An ordinary rotating phasor diagram is a two-dimensional vector, anchored to the origin, whose rotation rate corresponds to the cycle period in the price data stream. Similarly, Ehlers' phasor is a representation of angular phase rotation along the course of time. Its angle reflects the current phase of the cycle. Angles -180, -90, +90 and +180 degrees correspond to the beginning, valley, peak and end of the cycle, respectively. If the observed cycle is very long, the market can be considered trending . In his article, John ​Ehlers defined trending behavior to occur when the derived instantaneous cycle period value is greater that 60 bars. The author also introduced guidelines for long and short entries in a trending state. Depending on the tuning of the indicator period input, a long entry position may occur when the phasor angle is around the approximate vicinity of −90 degrees, while a short entry position may occur when the phasor angle will be around the approximate vicinity of +90 degrees. Applying these definitive guidelines, the author proposed a state variable that is indicated by +1 for a trending long position, 0 for cycling, and −1 for a trending short position (or out). The phasor angle, the cycle period, and the state variable are made available with three selectable display modes provided for this TradingView indicator. █ CALCULATIONS The calculations are carried out as follows. First, the price data stream is correlated with cosine and sine of a fixed cycle period. This produces two new data streams that correspond to the projections of the frequency domain phasor diagram to the horizontal (so-called real ) and vertical (so-called imaginary ) axis respectively. The wavelength of the cycle period input should be set for the midrange vicinities of the phasor to coincide with the peaks and valleys of the charted price data. Secondly, the phase angle of the phasor is easily computed as the arctangent of the ratio of the imaginary component to the real component. The difference between the current phasor values and its last is then employed to calculate a derived instantaneous period and market state. This computation is then repeated successively for each individual bar over the entire duration of the data set.
Pine Script®指標
由PineCodersTASC提供
8
Bitcoin Power Law Clock [LuxAlgo]The Bitcoin Power Law Clock is a unique representation of Bitcoin prices proposed by famous Bitcoin analyst and modeler Giovanni Santostasi. It displays a clock-like figure with the Bitcoin price and average lines as spirals, as well as the 12, 3, 6, and 9 hour marks as key points in the cycle. 🔶 USAGE Giovanni Santostasi, Ph.D., is the creator and discoverer of the Bitcoin Power Law Theory. He is passionate about Bitcoin and has 12 years of experience analyzing it and creating price models. As we can see in the above chart, the tool is super intuitive. It displays a clock-like figure with the current Bitcoin price at 10:20 on a 12-hour scale. This tool only works on the 1D INDEX:BTCUSD chart. The ticker and timeframe must be exact to ensure proper functionality. According to the Bitcoin Power Law Theory, the key cycle points are marked at the extremes of the clock: 12, 3, 6, and 9 hours. According to the theory, the current Bitcoin prices are in a frenzied bull market on their way to the top of the cycle. 🔹 Enable/Disable Elements All of the elements on the clock can be disabled. If you disable them all, only an empty space will remain. The different charts above show various combinations. Traders can customize the tool to their needs. 🔹 Auto scale The clock has an auto-scale feature that is enabled by default. Traders can adjust the size of the clock by disabling this feature and setting the size in the settings panel. The image above shows different configurations of this feature. 🔶 SETTINGS 🔹 Price Price: Enable/disable price spiral, select color, and enable/disable curved mode Average: Enable/disable average spiral, select color, and enable/disable curved mode 🔹 Style Auto scale: Enable/disable automatic scaling or set manual fixed scaling for the spirals Lines width: Width of each spiral line Text Size: Select text size for date tags and price scales Prices: Enable/disable price scales on the x-axis Handle: Enable/disable clock handle Halvings: Enable/disable Halvings Hours: Enable/disable hours and key cycle points 🔹 Time & Price Dashboard Show Time & Price: Enable/disable time & price dashboard Location: Dashboard location Size: Dashboard size
Pine Script®指標
由LuxAlgo提供
10
Clock&Flow MM+InfoThis script is an indicator that helps you visualize various moving averages directly on the price chart and gain some additional insights. Here's what it essentially does: Displays Different Moving Averages: You can choose to see groups of moving averages with different periods, set to nominal cyclical durations. You can also opt to configure them for instruments traded with classic or extended trading hours (great for Futures), and they'll adapt to your chosen timeframe. Colored Bands: It allows you to add colored bands to the background of the chart that change weekly or daily, helping you visualize time cycles. You can customize the band colors. Information Table: A small table appears in a corner of the chart, indicating which cycle the moving averages belong to (daily, weekly, monthly, etc.), corresponding to the timeframe you are using on the chart. Customization: You can easily enable or disable the various groups of moving averages or the colored bands through the indicator's settings. It's a useful tool for traders who use moving averages to identify trends and support/resistance levels, and who want a quick overview of market cycles. Questo script è un indicatore che aiuta a visualizzare diverse medie mobili direttamente sul grafico dei prezzi e a ottenere alcune informazioni aggiuntive. In pratica, fa queste cose: Mostra diverse medie mobili: Puoi scegliere di vedere gruppi di medie mobili con periodi diversi impostati sulle durate cicliche nominali. Puoi scegliere se impostarle per uno strumento quotato con orario di negoziazione classico o esteso (ottimo per i Futures) e si adattano al tuo timeframe). Bande colorate: Ti permette di aggiungere delle bande colorate sullo sfondo del grafico che cambiano ogni settimana o ogni giorno, per aiutarti a visualizzare i cicli temporali. Puoi scegliere il colore delle bande. Tabella informativa: In un angolo del grafico, compare una piccola tabella che indica a quale ciclo appartengono le medie mobili (giornaliero, settimanale, mensile, ecc.) e corrispondono in base al timeframe che stai usando sul grafico. Personalizzazione: Puoi facilmente attivare o disattivare i vari gruppi di medie mobili o le bande colorate tramite le impostazioni dell'indicatore. È uno strumento utile per i trader che usano le medie mobili per identificare trend e supporti/resistenze, e che vogliono avere un colpo d'occhio sui cicli di mercato.
Pine Script®指標
由JoMerende提供
Economic Seasons [Daveatt]Ever wondered what season your economy is in? Just like Mother Nature has her four seasons, the economy cycles through its own seasons! This indicator helps you visualize where we are in the economic cycle by tracking two key metrics: 📊 What We're Tracking: 1. Interest Rates (USIRYY) - The yearly change in interest rates 2. Inflation Rate (USINTR) - The rate at which prices are rising The magic happens when we normalize these values (fancy math that makes the numbers play nice together) and compare them to their recent averages. We use a lookback period to calculate the standard deviation and determine if we're seeing higher or lower than normal readings. 🔄 The Four Economic Seasons & Investment Strategy: 1. 🌸 Goldilocks (↑Growth, ↓Inflation) "Not too hot, not too cold" - The economy is growing steadily without overheating. BEST TIME TO: Buy growth stocks, technology, consumer discretionary WHY: Companies can grow earnings in this ideal environment of low rates and stable prices 2. 🌞 Reflation (↑Growth, ↑Inflation) "Party time... but watch your wallet!" - The economy is heating up. BEST TIME TO: Buy commodities, banking stocks, real estate WHY: These sectors thrive when inflation rises alongside growth 3. 🌡️ Inflation (↓Growth, ↑Inflation) "Ouch, my purchasing power!" - Growth slows while prices keep rising. BEST TIME TO: Rotate into value stocks, consumer staples, healthcare WHY: These defensive sectors maintain pricing power during inflationary periods 4. ❄️ Deflation (↓Growth, ↓Inflation) "Winter is here" - Both growth and inflation are falling. BEST TIME TO: Focus on quality bonds, cash positions, and dividend aristocrats WHY: Capital preservation becomes key; high-quality fixed income provides safety 🎯 Strategic Trading Points: - BUY AGGRESSIVELY: During late Deflation/early Goldilocks (the spring thaw) - HOLD & ACCUMULATE: Throughout Goldilocks and early Reflation - START TAKING PROFITS: During late Reflation/early Inflation - DEFENSIVE POSITIONING: Throughout Inflation and Deflation ⚠️ Warning Signs to Watch: - Goldilocks → Reflation: Time to reduce growth stock exposure - Reflation → Inflation: Begin rotating into defensive sectors - Inflation → Deflation: Quality becomes crucial - Deflation → Goldilocks: Start building new positions The blue dot shows you where we are right now in this cycle. The red arrows in the middle remind us that this is a continuous cycle - one season flows into the next, just like in nature! 💡 Pro Tip: The transitions between seasons often provide the best opportunities - but also the highest risks. Use additional indicators and fundamental analysis to confirm these shifts. Remember: Just like you wouldn't wear a winter coat in summer, you shouldn't use a Goldilocks strategy during Inflation! Time your trades with the seasons. 🎯 Happy Trading! 📈
Pine Script®指標
由Daveatt提供
WD Gann: Close Price X Bars Ago with Line or Candle PlotThis indicator is inspired by the principles of WD Gann, a legendary trader known for his groundbreaking methods in time and price analysis. It helps traders track the close price of a security from X bars ago, a technique that is often used to identify key price levels in relation to past price movements. This concept is essential for Gann’s market theories, which emphasize the relationship between time and price. WD Gann’s analysis often revolved around specific numbers that he considered significant, many of which correspond to squared numbers (e.g., 1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289, 324, 361, 400, 441, 484, 529, 576, 625, 676, 729, 784, 841, 900, 961, 1024, 1089, 1156, 1225, 1296, 1369, 1444, 1521, 1600, 1681, 1764, 1849, 1936). These numbers are believed to represent natural rhythms and cycles in the market. This indicator can help you explore how past price levels align with these significant numbers, potentially revealing key price zones that could act as support, resistance, or reversal points. Key Features: - Historical Close Price Calculation: The indicator calculates and displays the close price of a security from X bars ago (where X is customizable). This method aligns with Gann's focus on price relationships over specific time intervals, providing traders with valuable reference points to assess market conditions. - Customizable Plot Type: You can choose between two plot types for visualizing the historical close price: - Line Plot: A simple line that represents the close price from X bars ago, ideal for those who prefer a clean and continuous representation. - Candle Plot: Displays the close price as a candlestick chart, providing a more detailed view with open, high, low, and close prices from X bars ago. - Candle Color Coding: For the candle plot type, the script color-codes the candles. Green candles appear when the close price from X bars ago is higher than the open price, indicating bullish sentiment; red candles appear when the close is lower, indicating bearish sentiment. This color coding gives a quick visual cue to market sentiment. - Customizable Number of Bars: You can adjust the number of bars (X) to look back, providing flexibility for analyzing different timeframes. Whether you're conducting short-term or long-term analysis, this input can be fine-tuned to suit your trading strategy. - Gann Method Application: WD Gann's methods involved analyzing price action over specific time periods to predict future movements. This indicator offers traders a way to assess how the price of a security has behaved in the past in relation to a chosen time interval, a critical concept in Gann's theories. How to Use: 1. Input Settings: - Number of Bars (X): Choose the number of bars to look back (e.g., 100, 200, or any custom period). - Plot Type: Select whether to display the data as a Line or Candles. 2. Interpretation: - Using the Line plot, observe how the close price from X bars ago compares to the current market price. - Using the Candles plot, analyze the full price action of the chosen bar from X bars ago, noting how the close price relates to the open, high, and low of that bar. 3. Gann Analysis: Integrate this indicator into your broader Gann-based analysis. By looking at past price levels and their relationship to significant squared numbers, traders can uncover potential key levels of support and resistance or even potential reversal points. The historical close price can act as a benchmark for predicting future market movements. Suggestions on WD Gann's Emphasis in Trading: WD Gann’s trading methods were rooted in several key principles that emphasized the relationship between time and price. These principles are vital to understanding how the "Close Price X Bars Ago" indicator fits into his overall analysis: 1. Time Cycles: Gann believed that markets move in cyclical patterns. By studying price levels from specific time intervals, traders can spot these cycles and predict future market behavior. This indicator allows you to see how the close price from X bars ago relates to current market conditions, helping to spot cyclical highs and lows. 2. Price and Time Squaring: A core concept in Gann’s theory is that certain price levels and time periods align, often marking significant reversal points. The squared numbers (e.g., 1, 4, 9, 16, 25, etc.) serve as potential key levels where price and time might "square" to create support or resistance. This indicator helps traders spot these historical price levels and their potential relevance to future price action. 3. Geometric Angles: Gann used angles (like the 45-degree angle) to predict market movements, with the belief that prices move at specific geometric angles over time. This indicator gives traders a reference for past price levels, which could align with key angles, helping traders predict future price movement based on Gann's geometry. 4. Numerology and Key Intervals: Gann paid particular attention to numbers that held significance, including squared numbers and numbers related to the Fibonacci sequence. This indicator allows traders to analyze price levels based on these key numbers, which can help in identifying potential turning points in the market. 5. Support and Resistance Levels: Gann’s methods often involved identifying levels of support and resistance based on past price action. By tracking the close price from X bars ago, traders can identify past support and resistance levels that may become significant again in future market conditions. Perfect for: Traders using WD Gann’s methods, such as Gann angles, time cycles, and price theory. Analysts who focus on historical price levels to predict future price action. Those who rely on numerology and geometric principles in their trading strategies. By integrating this indicator into your trading strategy, you gain a powerful tool for analyzing market cycles and price movements in relation to key time intervals. The ability to track and compare the historical close price to significant numbers—like Gann’s squared numbers—can provide valuable insights into potential support, resistance, and reversal points. Disclaimer: This indicator is based on the methods and principles of WD Gann and is for educational purposes only. It is not intended as financial advice. Trading involves significant risk, and you should not trade with money that you cannot afford to lose. Past performance is not indicative of future results. The use of this indicator is at your own discretion and risk. Always do your own research and consider consulting a licensed financial advisor before making any investment decisions.
Pine Script®指標
由sam-austin提供