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5-Period Average of Returns (Close)This indicator calculates the 5-period average of returns of the closing price, providing a detrended, zero-centered oscillator ideal for cycle analysis and timing. Key Features: Detrended: Centers around zero to clearly reveal cyclical patterns. Cycle-friendly: Highlights peaks and troughs for measuring dominant cycles. Flexible: Can be applied to multiple timeframes (daily, weekly, intraday). Zero Line Reference: Quickly identify directional shifts in average returns. Foundation for Advanced Analysis: Can be combined with RSI, statistical bands, or multi-timeframe studies. Use this indicator to: Identify dominant cycles and their phase Measure cycle length and rhythm Assist in entry and exit timing based on average-return oscillations Detrend price data for more precise technical and cyclical analysis
Pine Script®指標
由aroramunish1992提供
Bitcoin Value Capture HeatmapBTC Value Capture Heatmap answers a question originally posed by Willy Woo: "How much pressure on Bitcoin's market cap does one dollar of purchasing power exert?" The higher the print, the more market cap grows per dollar invested -- adjusted for global M2 growth. Bitcoin Value Capture Heatmap = ( market cap / global M2 ) / realized cap A NOVEL INGREDIENT REVEALS A UNIQUE USE CASE Adjusting bitcoin's market cap for global M2 growth sharpens a legacy metric with a normalizing factor that 'stabilizes' its view across cycles. The metric peaked at identical levels (4.2), three bitcoin bull markets in a row. On the same day bitcoin price volatility peaked for the cycle, every time. One might naturally expect this to coincide with cycle tops. But it doesn't. It precede's cycle's tops: in a consistent, very specific way, that predisposing a unique use case. BITCOIN'S VOLATILTY TOP The metric's true use case only comes into clear focus when paired with an unrelated insight: Whether in distribution (in Spring 2021) or a parabolic blow off top (2017 & 2013), each of the last 3 bitcoin cycle tops shows tight consistent adherence to the Wykoff Distribution Schematic. "But Wykoff schematics apply to distribution tops, not to blow off tops." A closer look at the last 15-20 years of parabolic blow off tops, across all asset classes , viewed through a Wykoff lens, reveals recurring tight adherence to Wykoff's Distribution Schematic. Including (and especially) BTC's parabolic top in Dec 2017; BTC's parabolic top in 2013; and ETH's blow off top in Jan 2018. In our age of automation, this makes sense. Wykoff's schematics mirror the timeless archetypal goal of his 'Composite Operator': max pain for all other market participants. A process that lends itself to automation, optimized a bit more each passing year. Peak cycle volatility maps directly to the Wykoff Distribution Schematic's 'Buying Climax'. An event that preceded parabolic cycle tops, by about 2 weeks. Future BTC parabolas (should they recur) would come at exponentially higher market caps, so they may take longer to unfold -- I don't take the 2 week pattern too seriously. But Parabolic Distribution as an emergent archetypal market structure is likely encoded. PUTTING IT ALL TOGETHER Bitcoin Value Capture Heatmap signals peak cycle volatility, on a daily close of 4.2 on the metric's Y axis. It has never reached that level twice in the same cycle. Awareness that: (a) peak volatility for the cycle has likely been reached, and (b) peak volatility has a history of tightly preceding bitcoin cycle tops, can (c) empowers traders with a data-driven 'guide post' to their likely exactly location in an increasingly archetypal topping process. SPECIFIC USES IN AN EXIT STRATEGY When the Heatmap's signal level is reached, one might (for instance): * Hedge, since bitcoin is likely closing in on its cycle top, OR * Start to DCA out, over a pre-planned time period OR * Rotate up the risk curve, since BTC probably doesn't have much upside left, OR * Wait for acceptance one leg higher, which (consistent with Wykoff logic) is the likeliest place to expect an actual cycle top. Though the ratio (in the past) touched 4.2 each cycle, a closer look shows subtly lower peaks per cycle, like most other on-chain cycle oscillators. Extrapolating out, one might expect bitcoin's next top on volatility to print on any touch of 4.0 or higher. Or one might give it more room to run, consistent with record institutiional flows this cycle. Alerts are enabled for both options. The metric works on any timeframe, but should only be used on the 1D chart.
Pine Script®指標
由AlchemicalIntel提供
4
Cycle Trend SROverview: This indicator draws resistance and support lines calculated by market cycles. By default, blue dots are resistance and red dots are support. How to calculate market cycle?: It use sine wave indicator by John Ehlers to calculate the market cycle. How to determine support and resistance levels?: There are two conditions for the depiction of a resistance and support lines. The sine wave indicator has two lines(sine and lead sine). The first condition is the crossing of these two lines. The second condition is a new high or low. - In the case of a resistance, it is a move below the low of the previous candle. - In the case of a support, it is a move above the high of the previous candle. When the two conditions are fulfilled, the highest or lowest price of the past few candles is used. The default setting is three, but this can be changed in parameter settings. Cycle changes and market reversals do not always occur at the same time. This is because price movements are not created by cycles, but by the results of trades. This allows us to understand the true support or resistance line, not the theoretical one. How to use?: This indicator assumes that price movements are formed by cycles and trends. The first step is to determine whether the cycle or the trend is stronger. - When the price is above support or below resistance, the Cycle dominates the market. - When the price is below support or above resistance, the Trend dominates the market. In a cycle-dominated situation, enter the market at the time when support or resistance lines is depicted. It is better to target only those cycles that match the upper time frame. In a trend-dominated situation, think about riding the trend. The timing to go outside of support and resistance is a trigger. PB(pullback) will be drawn only in case of a strong trend. A strong uptrend is when the price goes above a resistance line and the next support line depicted is above the resistance line. There is a threshold for this, which is twice as high as the price of ATR for period 14. A strong downtrend is the opposite of this. At the end of the cycle after the PB, the END is described. This can be used as a sign of a market reversal. If PB and END are not needed, hide them in the settings.
Pine Script®指標
由jonico提供
1
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
cd_Quarterly_cycles_SSMT_TPD_CxGeneral This indicator is designed in line with the Quarterly Theory to display each cycle on the chart, either boxed and/or in candlestick form. Additionally, it performs inter-cycle divergence analysis ( SSMT ) with the correlated symbol, Terminus Price Divergence ( TPD ), Precision Swing Point ( PSP ) analysis, and potential Power of Three ( PO3 ) analysis. Special thanks to @HandlesHandled for his great indicator, which I used while preparing the cycles content. Details & Usage: Optional cycles available: Weekly, Daily, 90m, and Micro cycles. Displaying/removing cycles can be controlled from the menu (cycles / candles / labels). All selected cycles can be shown, or you can limit the number of displayed cycles (min: 2, max: 4). The summary table can be toggled on/off and repositioned. What’s in the summary table? • Below the header, the correlated symbol used in the analysis is displayed (e.g., SSMT → US500). • If available, live and previous bar results of the SSMT analysis are shown. • Under the PSP & TPD section, results are displayed when conditions are met. • Under Alerts, the real-time status of conditions defined in the menu is shown. • Under Potential AMD, possible PO3 analysis results are displayed. Analysis & Symbol Selection: To run analyses, a correlated symbol must first be defined with the main symbol. Default pairs are preloaded (see below), but users should adjust them according to their exchange and instruments. If no correlated pair is defined, cycles are displayed only as boxes/candles. Once defined pairs are opened on the chart, analyses load automatically. Pairs listed on the same row in the menu are automatically linked, so no need to re-enter them across rows. SSMT Analysis: Based on the chart’s timeframe, divergences are searched across Weekly, Daily, 90m, and Micro cycles. The code will not produce results for smaller cycles than the current timeframe. (Example: On H1, Micro cycles will not be displayed.) Results are obtained by comparing the highs and lows of consecutive cycles in the same period. If one pair makes a new high/low while the other does not, this divergence is added to SSMT results. The difference from classic SMT is that cycles are used instead of bars. PSP & TPD Analysis: A correlated symbol must be defined. For PSP, timeframe options are added to the menu. Users toggle timeframes on/off by checking/unchecking boxes. In selected timeframes, PSP & TPD analysis is performed. • PSP: If candlesticks differ in color (bullish/bearish) between symbols and the bar is at a high/low of the timeframe (and higher/lower than the bars before/after it), it is identified as a PSP. Divergences between pairs are interpreted as potential reversal signals. • TPD: Once a PSP occurs, the closing price of the previous bar and the opening price of the next bar are compared. If one symbol shows continuation while the other does not, it is marked as a divergence. Example: Let’s assume Pair 1 and Pair 2 are selected in the menu with the H4 timeframe, and our cycle is Weekly (Box). For Pair 1, the H4 candle at the Weekly high level: • Is positioned at the Weekly high, • Its high is above both the previous and the next candle, • It closed bearish (open > close). For Pair 2, the same H4 candle closed bullish (close > open). → PSP conditions are met. For TPD, we now check the candles before and after this PSP (H4) candle on both pairs. Comparing the previous candle’s close with the next candle’s open, we see that: • In Pair 1, the next open is lower than the previous close, • In Pair 2, the next open is higher than the previous close. Pair 1 → close > open Pair 2 → close < open Since they are not aligned in the same direction, this is interpreted as a divergence — a potential reversal signal. While TPD results are displayed in the summary table, whenever the conditions are met in the selected timeframes, the signals are also plotted directly on the chart. (🚦, X) • Higher timeframe TPD example: • Current timeframe TPD example: Alerts: The indicator can be conditioned based on aligned timeframes defined within the concept. Example (assuming random active rows in the screenshot): • Weekly Bullish SSMT → Tf2 (menu-selected) Bullish TPD → Daily Bullish SSMT. Selecting “none” in the menu means that condition is not required. When an alert is triggered, it will be displayed in the corresponding row of the table. • Example with only condition 3 enabled: Potential PO3 Analysis: According to Quarterly Theory, price moves in cycles, and the same structures are assumed to continue in smaller timeframes. From classical PO3 knowledge: before the main move, price first manipulates in the opposite direction to trap buyers/sellers, then makes its true move. The cyclical sequence is: (A)ccumulation → (M)anipulation → (D)istribution → (R)eversal / Continuation. Within cycle candles, the first letter of each phase is displayed. So how does the analysis work? If the active cycle is in (M)anipulation or (D)istribution phase, and it sweeps the previous cycle’s high or low but then pulls back inside, this is flagged in the summary table as a possible PO3 signal. In other words, it reflects the alignment of theoretical sequence with real-time price action. Confluence with SSMT and TPD conditions further strengthens the expectation. Final Note: No single marking or alert carries meaning on its own — it must always be evaluated in the context of your concept knowledge. Instead of trading purely on expectations, align bias + trend + entry confirmations to improve your success rate. Feedback and suggestions are welcome. Happy trading!
Pine Script®指標
由cdikici71提供
77
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
Z-Score Momentum Dashboard Z-Score Momentum Dashboard: A Comprehensive Technical Analysis Framework Understanding the Z-Score Momentum Dashboard The Z-Score Momentum Dashboard represents a sophisticated evolution in technical analysis indicators, designed to synthesize multiple analytical frameworks into a singular, coherent probabilistic assessment of market conditions. At its core, this indicator is a multi-dimensional analytical engine that processes price action, volume dynamics, cyclical patterns, and statistical anomalies to generate standardized z-scores that measure how far current market behavior deviates from established norms. Unlike traditional single-metric indicators that examine price through one lens, this dashboard constructs a comprehensive probabilistic model by weighting and combining six distinct analytical domains: Ehlers bandpass filtering for cycle detection, momentum calculations across multiple timeframes, mean reversion tendencies, trend strength measurements, volatility regime analysis, and volume confirmation signals. The indicator operates by first calculating individual scores across each of these six domains, normalizing them into comparable z-score formats, then applying user-configurable weights to create a composite probability score that estimates the likelihood of upward price movement. This probability undergoes statistical transformation through hyperbolic tangent functions to ensure bounded outputs between zero and one, which are then compared against historical baselines to generate the final z-score reading. The z-score itself becomes the primary signal, indicating not just direction but the statistical significance of the current market state relative to recent history. When the z-score exceeds predefined thresholds, it suggests the market has entered a regime that statistically differs from the baseline, implying either strong momentum continuation or potential exhaustion depending on accompanying contextual indicators. The dashboard visualization provides traders with immediate access to critical information through a comprehensive table display that shows historical z-scores over the past five days, current probability assessments, trend classification, momentum measurements, acceleration metrics, and distance from moving averages. This multi-temporal perspective allows traders to observe not just the current state but the trajectory of change, identifying whether momentum is building, plateauing, or reversing. The indicator also generates regime classifications such as "PARABOLIC EXT," "OVERSOLD," "STRONG MOM," and "NEUTRAL," which combine z-score readings with price extension metrics to characterize the current market environment. These classifications directly inform suggested actions, ranging from "Ride trend w/ stops" during strong momentum periods to "Watch for reversal" during oversold conditions with increasing momentum, providing traders with contextually appropriate strategic guidance. The Special Nature of This Analytical Approach What distinguishes the Z-Score Momentum Dashboard from conventional technical indicators is its fundamental philosophical approach to market analysis, which embraces probabilistic thinking rather than deterministic prediction. Most traditional indicators generate binary signals or directional recommendations based on threshold crossovers or pattern recognition, implicitly suggesting certainty about future price movement. This dashboard, in contrast, explicitly models uncertainty by generating probability distributions and measuring statistical significance, acknowledging that markets are stochastic systems where edge comes from systematic bias rather than predictive certainty. By converting diverse technical signals into standardized z-scores, the indicator creates a common language for comparing fundamentally different types of market information, whether that information comes from price momentum, volume patterns, or cyclical oscillations. The pseudo-machine learning architecture embedded within the indicator represents another distinctive feature that elevates it beyond standard technical analysis tools. While Pine Script limitations prevent the implementation of actual neural networks or gradient-boosted decision trees, the indicator approximates ensemble learning principles by treating each analytical domain as a separate "model" whose outputs are weighted and combined. Users can adjust these weights based on their market beliefs or through backtesting optimization, effectively training the indicator to emphasize whichever analytical dimensions prove most predictive in their specific trading context. This flexibility means the same indicator can be configured for mean-reversion trading in range-bound markets by increasing mean reversion weights, or for momentum trading in trending markets by emphasizing trend and momentum components, making it adaptable across varying market regimes without requiring entirely different analytical tools. The integration of John Ehlers' digital signal processing concepts, particularly the bandpass filtering and super smoother functions, introduces engineering-grade analytical precision to financial market analysis. Ehlers' work translates aerospace and telecommunications signal processing mathematics into trading applications, allowing the indicator to isolate specific cyclical frequencies within price action while filtering out noise. This is fundamentally different from simple moving averages or oscillators that indiscriminately smooth price data; bandpass filters can extract the 10-day cycle component separately from the 20-day cycle component, identifying when multiple cycles align or diverge. The inclusion of these sophisticated filters alongside more conventional tools creates a hybrid analytical framework that combines the mathematical rigor of quantitative finance with the practical market wisdom embedded in traditional technical analysis. The dashboard's temporal analysis capabilities provide another layer of analytical depth rarely found in standalone indicators. By displaying five days of historical z-scores alongside current readings, the interface enables pattern recognition at the signal level rather than just the price level. Traders can observe whether z-scores are trending, oscillating, or demonstrating divergent behavior relative to price action. For instance, if price continues making new highs while z-scores decline, this suggests deteriorating statistical support for the advance despite superficial price strength, providing early warning of potential reversals. Similarly, rising z-scores during price consolidation indicate building statistical pressure that may soon manifest as directional movement. This meta-analytical capability transforms the indicator from a simple signal generator into a comprehensive framework for understanding the statistical character of market behavior. Algorithmic Superiority and Technical Advantages The algorithmic architecture of the Z-Score Momentum Dashboard demonstrates several technical advantages that contribute to its analytical power and practical utility. The normalization of disparate technical indicators into standardized z-scores solves a fundamental problem in multi-factor analysis: how to combine indicators with different scales and units into a coherent composite signal. A momentum reading measured in price points cannot be directly compared to an RSI reading measured on a 0-100 scale, nor to a volume ratio measured as a multiplier. By converting each measure into a z-score representing standard deviations from its respective mean, the indicator creates dimensional consistency, ensuring that each component contributes proportionally to the final composite score based on its statistical deviation rather than its nominal value. The use of adaptive baselines through rolling statistical windows provides robustness against regime changes and non-stationary market behavior. Rather than comparing current readings against fixed historical values or statically defined overbought/oversold levels, the indicator continuously recalculates mean and standard deviation estimates over the user-defined baseline period. This approach automatically adjusts to changing volatility regimes, market cycles, and structural shifts in price behavior. During high-volatility periods, the standard deviation increases, requiring larger absolute deviations to generate extreme z-scores, appropriately raising the bar for signal generation. Conversely, during low-volatility periods, smaller absolute movements can generate significant z-scores, maintaining signal sensitivity across diverse market conditions. The composite probability calculation employs mathematically sound transformation functions rather than arbitrary scaling. After weighting and combining individual z-scores into a composite score, the indicator applies hyperbolic tangent transformation to convert the unbounded composite score into a bounded probability estimate between zero and one. The tanh function was chosen specifically because its sigmoid-shaped curve smoothly compresses extreme values while maintaining sensitivity around the center, preventing outlier distortion while preserving information about moderate deviations. This is superior to linear scaling or simple threshold clamping, which can create artificial discontinuities or lose information about the magnitude of extreme readings. The subsequent z-score calculation on this probability distribution creates a second-order statistical metric that measures not just "is probability high?" but "is probability statistically significantly higher than typical?" This layered statistical approach provides more nuanced information than single-stage calculations. The incorporation of acceleration metrics alongside momentum measurements adds a crucial dimension to the analytical framework. While momentum measures the first derivative of the z-score (rate of change), acceleration measures the second derivative (rate of change of the rate of change), identifying inflection points where momentum itself shifts. Markets often reverse not when momentum reaches zero but when acceleration reverses, as this indicates the rate of momentum decay is accelerating even while momentum remains positive. By explicitly calculating and displaying acceleration, the indicator provides early warning of potential trend exhaustion before momentum fully dissipates. This mathematical sophistication mirrors concepts from physics and calculus, applying them to financial market dynamics in ways that enhance predictive capability. The multi-timeframe momentum analysis embedded within the indicator examines price changes over five, ten, and twenty periods, capturing different temporal scales of market behavior. Short-term momentum captures immediate price action and trading range dynamics, while longer-term momentum reflects sustained directional bias and major trend development. By combining these timeframes into a weighted average before calculating z-scores, the indicator synthesizes information across temporal scales, avoiding the myopia of single-timeframe analysis. This approach recognizes that market structure exists simultaneously at multiple frequencies, and robust signals often emerge when momentum aligns across timeframes, while divergences between timeframes can signal pending reversals or consolidations. Predictive Power Through Cyclical Analysis The integration of cyclical analysis into the Z-Score Momentum Dashboard represents one of its most powerful predictive capabilities, leveraging the empirical observation that financial markets exhibit periodic behavior driven by fundamental economic cycles, seasonal patterns, trader psychology, and technical feedback loops. The Ehlers bandpass filters implemented in the indicator specifically isolate cyclical components at 10, 15, and 20-day periods, frequencies that correspond to common trading cycles including bi-weekly, monthly, and quarterly rhythms in market activity. By extracting these specific frequency bands and measuring their slope, the indicator identifies when cycles are aligned in the same directional phase versus when they are diverging, with aligned cycles providing stronger predictive signals than single-frequency readings. Cyclical analysis offers predictive power because cycles, by definition, have characteristic wavelengths that enable forecasting of future turning points based on the current phase. If the indicator detects that the 10-day cycle is in a trough phase while the 20-day cycle is also declining, it can anticipate that the shorter cycle should begin turning upward before the longer cycle, potentially creating a bullish divergence or early reversal signal. Conversely, when a shorter cycle reaches a peak while longer cycles continue rising, this suggests the current rally may consolidate before the longer-cycle momentum can drive new highs. This phase relationship analysis transforms cyclical information from descriptive to predictive, allowing traders to position ahead of probable turning points rather than merely reacting to them. The bandpass filtering approach is particularly valuable because it separates signal from noise more effectively than conventional smoothing techniques. Traditional moving averages suppress both high-frequency noise and the actual signal being measured, creating lag and reducing responsiveness. Bandpass filters, in contrast, selectively attenuate frequencies outside the target band while preserving amplitude and phase information within the band, maintaining the timing and magnitude of the actual cyclical component. This means when the bandpass output changes, it reflects genuine change in the underlying cycle rather than random noise or smoothing artifacts. The z-score normalization of bandpass slopes then measures whether the current cyclical momentum is statistically unusual relative to recent history, identifying periods when cyclical forces are particularly strong or weak. The integration of Fisher Transform calculations further enhances cyclical predictive power by converting price oscillations into a nearly Gaussian probability distribution. Financial price data typically exhibits non-normal distributions with fat tails and skewness, which violate the assumptions underlying many statistical techniques. The Fisher Transform specifically addresses this by mapping the price data onto a normal distribution where standard statistical inference tools work more reliably. When applied to cyclical data, this transformation makes it possible to accurately assess the statistical significance of cycle phases and turning points, distinguishing between normal cyclical oscillation and statistically significant deviations that may precede major price movements. The Schaff Trend Cycle component adds another dimension to cyclical analysis by combining MACD calculations with stochastic smoothing to identify trending phases within broader cyclical structures. Markets often exhibit fractal behavior where trends exist within cycles which exist within larger trends. The Schaff indicator specifically addresses this nested structure by detecting when shorter-term trends are emerging within the dominant cycle, providing early identification of trend changes before they become apparent in price action. When the Schaff reading aligns with bandpass filter signals and overall z-score direction, it confirms that multiple analytical perspectives agree on current cyclical phase, increasing confidence in directional predictions. The Detrended Price Oscillator (DPO) calculation removes trend components to isolate pure cyclical behavior, addressing a common challenge in cyclical analysis where strong trends can mask underlying cycles. By comparing current price to a centered moving average, the DPO reveals cyclical patterns that persist regardless of trend direction, allowing the indicator to maintain cyclical awareness in both trending and ranging markets. This is particularly valuable because cycles often continue operating during trends but become invisible to trend-following indicators, yet these cycles can predict pullbacks, consolidations, and acceleration phases within the larger trend. The incorporation of DPO signals into the composite z-score calculation ensures that cyclical information contributes to the final reading even when dominated by strong directional momentum. Practical Trading Application and Strategic Implementation Implementing the Z-Score Momentum Dashboard in practical trading requires understanding both its signal generation logic and the appropriate strategic frameworks for acting on its outputs. The primary trading signal comes from the overall z-score reading relative to the trigger and extreme thresholds, which by default are set at 1.25 and 2.0 respectively. When the z-score exceeds the trigger threshold, it indicates that current market behavior is more than 1.25 standard deviations above the recent baseline, suggesting statistically significant bullish momentum. Traders can interpret this as a regime shift from neutral to bullish conditions, warranting either initiation of long positions or continuation of existing long exposure with trailing stops. The strength of this signal increases when the z-score crosses the extreme threshold, indicating the market has entered a parabolic phase that, while statistically unusual, may represent either climactic buying or unsustainable conditions prone to mean reversion. The regime classifications provide contextual interpretation that modifies how traders should approach z-score signals. A z-score above the trigger threshold combined with moderate price extension from the 20-period moving average generates a "STRONG MOM" regime classification with the recommended action "Ride trend w/ stops," suggesting that traders should maintain directional exposure while using trailing stop-loss orders to protect profits if momentum reverses. In contrast, a z-score above the trigger threshold but with extreme price extension generates a "PARABOLIC EXT" classification with the action "Mean rev UP expected," warning that despite strong statistical momentum, the price has deviated too far from its moving average and may soon consolidate or reverse toward the mean. This nuanced interpretation prevents traders from blindly chasing extended moves even when z-scores remain elevated. The trend classification system—identifying RISING, FALLING, BOTTOMING, and TOPPING patterns—provides crucial information about the trajectory of statistical momentum rather than just its current level. A RISING classification indicates that not only is the z-score positive, but it has been consistently increasing over recent periods, suggesting accelerating momentum and increasing statistical support for directional movement. Traders can use this to distinguish between stable momentum that may continue and deteriorating momentum that may reverse, informing position sizing and stop-loss placement decisions. BOTTOMING and TOPPING classifications specifically identify potential inflection points where the direction of z-score movement is changing, generating early reversal signals before z-scores cross back through neutral territory. For mean reversion traders, the indicator provides exceptional value when z-scores reach extreme negative levels (below -2.0) while showing BOTTOMING trend patterns and positive acceleration. This combination suggests that statistical momentum has reached an extreme oversold condition and is beginning to reverse, creating favorable risk-reward opportunities for counter-trend long positions. The extension metric provides additional confirmation, as extreme negative extension from the moving average creates mechanical pull toward the mean independent of momentum considerations. Traders can enter positions when these factors align, using the moving average as an initial profit target and the z-score returning to neutral as a signal for position closure or transition to trend-following mode. For trend-following traders, the indicator is most valuable when z-scores remain elevated above the trigger threshold for extended periods with RISING or stable trend patterns and positive momentum readings. This indicates persistent statistical support for the trend rather than a temporary spike, justifying larger position sizes and wider stop-loss placement. The momentum and acceleration metrics help trend followers distinguish between healthy trends with sustained momentum and exhausted trends where momentum is decelerating, allowing for timely exit before reversals occur. When momentum and acceleration both turn negative while z-scores remain positive, it signals that the statistical foundation of the trend is eroding even though the trend nominally persists, prompting trend followers to tighten stops or take partial profits. The component scores displayed in the dashboard enable advanced traders to perform qualitative analysis of what factors are driving the composite z-score reading. If the composite z-score is positive but the breakdown shows that bandpass and momentum scores are negative while mean reversion scores are strongly positive, this indicates that the bullish reading is driven primarily by oversold mean reversion potential rather than directional momentum. Traders can use this information to adjust their trading approach, perhaps favoring short-term reversal trades over longer-term trend follows. Conversely, if all components show aligned readings, it suggests broad-based agreement across analytical dimensions, increasing confidence in the signal and potentially warranting larger position sizes or longer holding periods. Integration with broader trading systems can enhance the indicator's effectiveness. Traders might use the z-score as a filter for other strategies, taking long signals from separate systems only when the z-score is positive or trading reversal patterns only when z-scores are extreme. Alternatively, the indicator can serve as a portfolio allocation tool, increasing equity exposure when z-scores are positive and reducing exposure or shifting to defensive positions when z-scores turn negative. The probability estimates can be directly incorporated into Kelly Criterion or other position sizing formulas, scaling position sizes proportionally to the estimated probability of upward movement adjusted for risk-reward ratios of specific trade setups. Alert conditions built into the indicator provide automated monitoring capabilities, notifying traders when z-scores cross critical thresholds or when trend patterns change from FALLING to BOTTOMING or RISING to TOPPING. These alerts enable traders to monitor multiple instruments without constant chart watching, maintaining awareness of regime changes across a diversified portfolio. The alerts for extreme z-scores specifically warn of potential climactic conditions that may require immediate attention, whether to take profits on existing positions or to prepare for reversal opportunities. The customization options allow traders to optimize the indicator for specific instruments and market conditions. The baseline period parameter controls the lookback window for calculating statistical norms, with shorter periods making the indicator more responsive to recent conditions at the cost of increased noise, while longer periods provide stability but slower adaptation to regime changes. The weight parameters enable traders to emphasize whichever analytical dimensions prove most predictive in their specific markets, potentially increasing trend weights for strongly trending instruments like technology stocks while increasing mean reversion weights for range-bound commodities or currencies. Through systematic backtesting and forward validation, traders can develop instrument-specific configurations that maximize the indicator's predictive accuracy. Ultimately, the Z-Score Momentum Dashboard functions most effectively as a comprehensive analytical framework rather than a standalone trading system, providing rich statistical context that enhances decision-making across diverse trading approaches. Whether used for discretionary trade timing, systematic signal generation, risk management, or portfolio allocation, the indicator's multi-dimensional analysis, cyclical awareness, and probabilistic framework offer traders a sophisticated tool for understanding and responding to statistical patterns in market behavior that persist across timeframes, instruments, and market regimes.
Pine Script®指標
由jaydesaigu提供
Retrograde Planets - CERetrograde Planets - Community Edition Welcome to the Retrograde Planets - Community Edition, a specialized tool designed to empower traders with W.D. Gann’s time-theory principles by highlighting planetary retrograde cycles. This feature-complete indicator allows traders to visualize critical retrograde periods for market analysis across equities, forex, commodities, and cryptocurrencies. As a seamless add-on to the Gann ToolBox, it enhances time-based trading strategies with precision and clarity. Overview The Retrograde Planets - Community Edition identifies and highlights retrograde cycles for Mercury, Venus, Mars, Jupiter, Saturn, Uranus, and Neptune, key periods often associated with market volatility and trend shifts in Gann’s methodology. By calculating ecliptic longitudes with high accuracy, the script plots historical and future retrograde periods (up to 365 days ahead) on your chart, complete with visual highlights and labels. This tool is ideal for traders leveraging astrological cycles to anticipate market turning points. Key Features Retrograde Cycle Highlighting : Displays retrograde periods for Mercury, Venus, Mars, Jupiter, Saturn, Uranus, and Neptune, marking start and end points with vertical lines and labels. Historical and Future Projections : Visualizes past retrograde cycles and projects future ones up to a year in advance with minute-level precision. Customizable Planet Selection : Choose from seven planets to focus on specific retrograde cycles relevant to your analysis. 24/7 Market Optimization : Enable extended future data for continuous markets like crypto, improving performance and projection accuracy. Styling Flexibility : Customize the highlight color for retrograde periods to enhance chart clarity and align with your visual preferences. Labeled Visuals : Includes labels (e.g., “℞ Starts,” “℞ Ends”) with tooltips for easy identification of retrograde cycle boundaries. How It Works Open Settings : Access the script’s settings to configure your preferences. Select a Planet : Choose from Mercury, Venus, Mars, Jupiter, Saturn, Uranus, or Neptune to analyze its retrograde cycles. Enable Retrograde Cycles : Check the box to activate retrograde highlighting for the selected planet. Customize Highlight Color : Adjust the color of the highlighted retrograde periods for better chart visibility. Enable 24/7 Mode (Optional) : For crypto or continuous markets, activate the 24/7 setting to include extended future data. Analyze and Trade : Use the highlighted retrograde periods and labeled lines to identify potential market volatility or trend changes, integrating Gann’s time-theory into your strategy. Get Started As a gift to the TradingView community and Gann traders, the Retrograde Planets - Community Edition is provided free of charge. With no features locked, this tool offers full access to retrograde cycle analysis for precise market timing. Trade wisely and harness the power of planetary cycles!
Pine Script®指標
由NasserHumood提供
BTC Mercenary ModelBitcoin Market Cycle Evaluation Using Subjective Z-Scores Introduction: I've crafted a unique indicator for Bitcoin that synthesizes multiple market indicators into a single, actionable Z-score, aiming to offer insights into the current market cycle phase. Here's the methodology: Methodology: Alpha Validation: Each component indicator has been tested for its predictive power (alpha) against Bitcoin's market cycle peaks and troughs from at least the last two cycles. This ensures each indicator contributes meaningfully to our model. Z-Score Synthesis: By converting each indicator's value into a Z-score, we normalize their contributions. The average of these Z-scores provides a refined signal, indicating whether Bitcoin is in an overbought or oversold state relative to historical norms. Features: Individual Indicator Customization: Users can tweak inputs to optimize each indicator's alpha, enhancing the model's predictive accuracy. Historical Averages: The script provides visibility into how both technical and fundamental indicators have scored in the past, offering a benchmark for current conditions. ROC Flexibility: Adjust the Rate of Change (ROC) period to suit your analysis timeframe, allowing for more personalized market cycle interpretation. Indicators Integrated: Fundamental: MVRV (Market Value to Realized Value) - Measures market sentiment vs. actual value. Bitcoin Thermocap - Relates Bitcoin's market cap to its transaction volume. NUPL (Net Unrealized Profit/Loss) - Indicates holder's profit or loss status. CVDD (Coin Days Destroyed) - Shows the movement of long-held coins. SOPR (Spent Output Profit Ratio) - Highlights whether coins are being spent at a profit or loss. Technical: RSI (Relative Strength Index) - Identifies overbought/oversold conditions. CCI (Commodity Channel Index) - Detects cyclical turns in Bitcoin's price. Multiple Moving Averages - For trend analysis over various time frames. Sharpe Ratio - Evaluates risk-adjusted return. Pi Cycle Indicator - Predicts cycle tops based on moving average crossovers. Hodrick-Prescott Filter - Separates trend from cycle in price data. VWAP (Volume Weighted Average Price) - Provides a trading benchmark. How It Works Together: This model uses a weighted average of Z-scores from these indicators to give a comprehensive view of Bitcoin's market cycle. The Z-scores are not just summed but considered in context; for example, when fundamental indicators like MVRV suggest an overvaluation while technical ones like RSI indicate a near-term correction, the model's output reflects this nuanced interaction. Future Developments: The next step is to include sentiment analysis, potentially from social media or news sentiment, to further refine our cycle predictions. Chart Example: Symbol/Timeframe: BTCUSD on a daily chart. Script Name: Bitcoin Cycle Z-Score Evaluator Feedback Encouraged: I'm eager to receive feedback on how this model could be further tailored or expanded for better market insights. -CM
Pine Script®指標
由QuantPablo提供
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
8
Jinny Gann ArJinny Gann AR is a comprehensive technical analysis indicator designed to empower traders with the tools to analyze market movements using Gann square of 9 theory. Developed by Magic_xD, this indicator integrates various features inspired by the legendary trader W.D. Gann's methods. The trading techniques by WD Gann are widely seen as innovative and are still studied and used by traders today. He used angles and various geometric constructions. Gann angles divide time and price into proportionate parts and are often used to predict areas of support and resistance, key tops and bottoms and future price moves. The method is based on the notion that markets rotate from angle to angle and when an angle is broken, price moves towards the next one. Several angles together make up a Gann Fan. - Jinny Gann AR Might accurately Shows you when and what price might be the end of the Cycle, -Gives The important pivot points - This Allows you to Detect Next Level of Resistance/Support And when a Possible Reversal might occur ahead so you can Catch a reversal in time. - Its Multi Language User interface English - Arabic. Ability to customize Every thing visually. Some Features Explained on USOIL Chart : Gann Square of 9 Levels for USOIL: Charts Shows and Up Cycle Started 4 May 2023 From bottom of 63.61 Indicating Important Levels and Expected End of 1 Cycle at 99.5 on 25 Sep 2024 Gann Star With Levels And Time Lines : Vertical Dashed Lines are The time lines Jinny Gann Grid Based on Shape Type not Static 45 Angle: Jinny Gann Grid + Levels : Jinny Gann Fan For Up Cycle: Jinny Gann Fan Reverse Same Cycle: Ability To Show Both Up/Reversal Fans on The chart: The Number of Fann Levels you need on the chart can be customized by changing Shape Type... But Price Will Respect it Pretty Well. Key Features: Direction Selection: Choose between "Up" or "Down" to specify the market direction you want to analyze. Automatic Settings Adjustment: Enable this option to allow the indicator to automatically adjust settings for optimal analysis. Original Gann Levels: Display original Gann theory levels Based on Gann Square of 9 Equations. Auto Detect Tops/Bottoms: Determine the number of previous candles used to automatically detect Top or Bottom in the market. Spacing Configuration: Adjust the spacing or offset between Gann levels to fine-tune your analysis. Manual Starting Point: Manually set the starting point for your analysis. Geometric Shape Selection: Choose from various geometric shapes including straight lines, triangles, quadrilaterals, and more... Custom Angle Selection: Define custom angles for geometric shapes . Time Interval Selection: Select time intervals such as 360 or 720 Etc... Cycle Analysis: Determine the number of cycles to analyze market movements effectively. Decimal Precision: Customize the number of decimal places displayed for accurate analysis. Automatic Spacing (Under Development): Future feature to automatically select spacing for enhanced user experience. Time Levels Display: Visualize time levels to gain insights into market timing. Gann Star Display: Show Gann stars to identify critical market points. Star Modification: Modify the appearance of Gann stars for better visualization. Gann Grid Display: Display Gann grids to identify key support and resistance levels. Grid Extension: Extend Gann grid lines for extended analysis. Gann Fan Display: Show Gann fans to analyze trend lines and potential reversals. Reverse Fan Display: Visualize Gann fans in reverse to explore alternative analysis perspectives. Additional Fan Options: Explore more options for Gann fan analysis. Time Line Adjustment: Move time lines to the right or left for flexible analysis. Star Line Extension: Extend Gann star lines for deeper insights. Fan Line Extension: Extend Gann fan lines for comprehensive trend analysis. Customizable Colors: Customize colors for various indicators to suit your preference. Width Adjustment: Adjust the width of trend lines for better visualization. Label Customization: Customize colors and positions of level and price labels for clarity.
Pine Script®指標
由Magic_xD提供
21
Leonid's Bitcoin Macro & Liquidity Regime Tracker🧠 Macro Overlay Score (Bitcoin Liquidity Regime Tracker) This indicator combines the most important macroeconomic and on-chain inputs into a single unified score to help investors identify Bitcoin’s long-term cycle phases. Each input is normalized into a 0–100 score and blended using configurable weights to generate a dynamic, forward-looking macro regime tracker. ✅ Best used on the **Bitcoin All Time History Index with Weekly resolution** (`INDEX:BTCUSD`) for maximum historical context and signal clarity. --- 📈 Why Macro? Macro liquidity conditions — interest rates, monetary expansion, dollar strength, credit risk — drive Bitcoin cycles . Risk assets like BTC thrive during periods of: Monetary easing Liquidity injections Expansionary central bank policy This overlay surfaces those periods *before* price follows. It captures cycle shifts in the business cycle, monetary policy, and investor sentiment — making it ideal for long-term allocators, macro-aligned investors, and cycle-focused BTC holders. 🔔 This is **not** designed for short-term or swing trading. It is optimized for **macro trend confirmation and regime awareness** — not fast entry/exit signals. --- 🔍 What It Tracks Macro Inputs: - 🏭 ISM 3M Trend (Business Cycle) - 💹 CPI YoY (Inverted Inflation) - 💵 M2 YoY + M2 Acceleration - 🇨🇳 China M2 (Global Liquidity) - 💱 DXY 3M Trend (USD Strength) - 🏦 TGA & RRP YoY (Treasury / MMF Flows) - 🏛 Fed Balance Sheet (WALCL) - 💳 High Yield Spread (Credit Conditions) - 💧 Net Liquidity Composite = WALCL – TGA – RRP On-Chain Inputs: - ⚠️ MVRV Ratio (Valuation Cycles) - 🚀 Mayer Multiple Acceleration (200DMA Momentum) --- 🧩 How It Works Each input is: Normalized to a 0–100 score Weighted by importance (fully configurable) Combined into a **composite Macro Score**, then normalized across history The chart will display: 🔷 A 0–100 **Macro Score Line** 🧭 **Cycle Phase classification**: Accumulation, Expansion, Distribution, Capitulation 📊 Optional **debug table** with all sub-scores --- 🧠 Interpreting the Signal | Signal Type | Meaning | |-------------------|---------------------------------------------| | Macro Score ↑ | Liquidity improving → Bullish regime forming | | Macro Score ↓ | Liquidity deteriorating → Caution warranted | | Score < 40 & Rising | 🔵 Accumulation cycle likely beginning | | Score > 70 & Falling | 🟡 Distribution / Macro exhaustion | | Net Liquidity ↑ | Strong driver of BTC upside historically | --- ❓ FAQ Q: Why did the Macro Score peak in March 2021, but Bitcoin topped in November? > The indicator reflects **macro liquidity**, not price momentum. M2 growth slowed, DXY bottomed, and the Fed stopped expanding WALCL by Q1 2021 — all signs of macro exhaustion. BTC continued on **residual momentum**, but the smart money began exiting months earlier. Q: What does the score range mean? - 0–25 : Tight liquidity, unfavorable conditions - 50 : Neutral environment - 75–100 : Strong easing, liquidity surge Q: Is this good for short-term signals? > No. This is a **macro-level overlay**, best used for 3–12 month context shifts, not day trades. Q: Can I adjust the weights? > Yes. You can tune the influence of each input to match your thesis (e.g., overweight on-chain, or global liquidity). Q: Do I need special data access? > No. All symbols are public TradingView datasets (FRED, CryptoCap, etc.). Just use this on a BTC chart like `BTCUSD`. --- ✅ How to Use - Load on **`INDEX:BTCUSD`**, set to **Weekly timeframe** - Confirm long-term bottoms when score is low and rising (Accumulation → Expansion) - Watch for tops when score is high and falling (Distribution → Capitulation) - Combine with price structure, realized profit/loss, and market sentiment --- 🚀 If you're serious about understanding Bitcoin's macro regime, this is your alpha map. Share it, clone it, and build on it.
Pine Script®指標
由DurdensBitcoinLedger提供
8
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
11
SSMT [jam]🔷 Quarterly Theory SSMT based off of Daye's Quarterly Theory This indicator detects Sequential SMTs between the charted symbols and up to two other correlated symbols using a hierarchical, time-based cycle system. ❓ What It Does The script divides time into five nested cycles: Nano: ~5.625 min (double to ~11.25 min) – best on seconds charts Micro: ~22.5 min (double to ~45 min) – best on 1-minute charts 90m: 90 min (double to 180 min) – best on 5-minute charts Daily Session: 6-hour segments (double to 12-hour) – best on 15-minute charts Weekly: Day-based phases (Q1–Q5; doublable) – best on 60-minute charts Within each cycle, it tracks price extremes for all selected symbols. When a cycle ends, it checks if the main index and comparison indices moved in opposite directions from their prior-cycle extremes. Matching opposite moves trigger a divergence signal. Divergences appear as coloured extending lines connecting the prior and current extremes, with labels showing which symbols diverged. ⭐ Unique Aspects Covers five fractal levels from ultra-short nano to weekly Allows higher-timeframe divergences on lower charts (e.g., daily/weekly signals visible on 1m) Auto-adjusts so the charted symbol is always the primary reference Optional vertical dividers and fixed Q-labels for clear cycle timing 🎀 Extensive Customization Global controls: Toggle all divergences, dividers, cycle labels; universal label background, text colour, size, position (start/middle/end of line), and style (auto/up/down) Per-cycle settings (independent for Nano, Micro, 90m, Daily, Weekly): Show/hide the cycle's divergences Doubling option Separate bullish and bearish line colours Line width and style (solid/dashed/dotted) Divider colour, width, and style Toggle Q-labels Option to display this cycle's signals on lower timeframes 🔨 How to Use I personally apply it to NQ, ES, and YM (CFDs), but you can choose whichever symbols you trade/prefer. Divergence lines form at cycle boundaries: Bullish divergence (typically lower low on main index but higher on others) → potential support/rotation higher Bearish divergence (typically higher high on main but lower on others) → potential resistance/rotation lower Stronger signals occur when multiple cycle levels align. Always combine with your own analysis and risk management. A highly configurable tool for spotting intermarket relative strength/weakness across multiple time scales.
Pine Script®指標
由JeremyMasuda提供
8
[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
6
bitcoin Multi-Timeframe Trend Analysis Toolbitcoin Multi-Timeframe Trend Analysis Tool: A Comprehensive Guide for Market Cycle Identification Introduction The Multi-Timeframe Trend Analysis Tool is a sophisticated technical indicator designed to help traders identify critical market phases across different time horizons. This tool synthesizes multiple established technical analysis concepts into a unified framework, specifically optimized for high-volatility markets such as cryptocurrencies and alternative coins (altcoins). By integrating trend-following, momentum, and mean-reversion principles, it provides visual cues for strategic entry and exit points throughout market cycles. Core Philosophy and Integration Rationale The indicator's design philosophy centers on the principle that different market phases require different analytical approaches. Rather than relying on a single indicator, which often produces false signals during complex market conditions, this tool combines multiple technical components that complement each other's strengths and compensate for individual weaknesses. The integration follows a logical hierarchy: Trend Identification through multiple EMA periods establishes the market's primary direction Momentum Confirmation via multiple MACD configurations validates trend strength and potential reversals Multi-timeframe Alignment ensures signals are significant across both short-term and long-term perspectives This layered approach reduces the likelihood of whipsaws and increases the statistical significance of generated signals. Component Synergy and Operational Mechanics 1. EMA System: The Trend Foundation The tool employs six Exponential Moving Averages organized into two groups: Long-term EMA Group (200, 300, 700 periods): The 200-period EMA serves as the primary trend baseline The 300-period EMA provides confirmation of the longer-term direction The 700-period EMA represents the "macro trend" and helps identify major cycle shifts Medium-term EMA Group (18, 36, 63 periods): These shorter EMAs capture intermediate trend dynamics The relationship between these EMAs helps identify acceleration or deceleration in trend momentum The EMA system works by comparing relationships between different period lengths. For instance, when shorter EMAs are positioned below longer EMAs, it confirms a bearish trend structure, while the opposite configuration suggests bullish momentum. 2. Multi-Period MACD System: Momentum and Divergence Detection The tool implements three separate MACD configurations, each serving a distinct purpose: Bottom MACD (168/364/6 periods): Designed to capture long-term momentum shifts at potential market bottoms The extended periods (168 and 364) filter out short-term noise while highlighting significant trend changes Particularly effective at identifying oversold conditions during prolonged downtrends Top MACD (108/234/9 periods): Optimized for detecting momentum deterioration at potential market tops The period selection is based on historical analysis of bull market cycles Helps identify when bullish momentum is weakening before price action clearly reverses Local Top MACD (9/36/9 periods): Functions as an early warning system for short-term corrections Particularly useful for swing traders and risk management Can help identify profit-taking opportunities during ongoing trends The three MACDs operate independently but collectively provide a comprehensive view of momentum across different time horizons. When multiple MACDs simultaneously show confirming signals, the reliability of the indication increases significantly. 3. Signal Generation Logic: Conditional Framework Signals are generated only when multiple conditions align across different components: Accumulation Zone Conditions: Requires both trend alignment (200 EMA below 300 EMA) AND either: Price trading at a significant discount to the 200 EMA (suggesting oversold conditions), OR The 200 EMA itself declining sharply (confirming bearish momentum exhaustion) This dual requirement prevents false accumulation signals during healthy downtrends Strong Buy Zone Conditions: Includes all accumulation zone requirements PLUS: Sharp decline in the 36-period EMA (suggesting panic or capitulation) Accelerated decline in the 200 EMA (confirming bearish exhaustion) This represents a higher-conviction signal with multiple confirming factors Potential Bull Market Top Conditions: Requires the 700 EMA to be rising sharply (confirming extended bullish trend) AND Top MACD showing bearish divergence (momentum weakening) AND Short-term EMA alignment still bullish (indicating the top is forming amid strength) This combination helps distinguish between minor corrections and major trend reversals Local Top Warning Conditions: Triggered when the 700 EMA shows accelerated gains (potential euphoria phase) AND The Local Top MACD shows bearish momentum divergence Serves as a risk management tool rather than a direct reversal signal Practical Application and Usage Guidelines For Long-Term Investors: Monitor for "Accumulation Zone" signals during market downturns Consider initiating or adding to positions during "Strong Buy Zone" signals Use these signals for dollar-cost averaging strategies rather than timing exact bottoms Hold through intermediate fluctuations unless "Potential Bull Market Top" signals appear For Trend Traders: Use EMA alignments to confirm trend direction before entering positions Employ "Local Top Warnings" to secure profits on portions of positions Watch for alignment between medium-term EMA direction and MACD signals for entry timing Consider "Potential Bull Market Top" signals as reasons to reduce exposure or implement hedging strategies For Risk Managers: Use "Local Top Warnings" to tighten stop-losses or reduce position sizes Monitor the relationship between price and the 200 EMA for overall market health assessment Track multiple timeframes to distinguish between normal volatility and potential trend changes Originality and Distinctive Features This tool represents a novel synthesis of existing technical concepts rather than a completely new indicator. Its originality stems from: Purpose-Specific MACD Configurations: Unlike standard MACD implementations, each of the three MACDs is optimized for a specific market condition, with period lengths derived from empirical analysis of market cycles. Multi-Layered Confirmation Framework: Signals require alignment across trend, momentum, and rate-of-change dimensions, reducing false positives common in single-indicator systems. Progressive Signal Hierarchy: The tool distinguishes between initial warning signals ("Local Top Warnings") and higher-conviction reversal signals ("Potential Bull Market Tops"), allowing for graduated responses. Combination of Absolute and Relative Conditions: The logic incorporates both absolute price relationships (price vs. EMA levels) and rate-of-change metrics (EMA acceleration/deceleration), capturing both state and momentum information. Limitations and Considerations Lagging Nature: Like all trend-following indicators, this tool reacts to established conditions rather than predicting future movements. Early trend phases may not generate signals. Parameter Sensitivity: The default parameters are optimized for daily cryptocurrency charts. Performance may vary across different asset classes or timeframes. Complementary Analysis Required: This tool should be used alongside fundamental analysis, volume confirmation, and market structure considerations. No Guarantee of Performance: Past success in identifying market phases does not ensure future accuracy. All trading involves risk, and no indicator provides certainty. Conclusion The Multi-Timeframe Trend Analysis Tool provides a structured approach to identifying significant market phases by integrating trend, momentum, and mean-reversion concepts across multiple time horizons. Its value lies not in predicting exact turning points but in identifying zones of increasing probability for trend changes, allowing traders to adjust their strategies accordingly. When used as part of a comprehensive trading plan with proper risk management, it can help traders navigate complex market environments with greater clarity and discipline. The tool is particularly suited to the extended trends and pronounced cycles characteristic of cryptocurrency markets, though its principles apply across various financial instruments. As with all technical tools, its effectiveness increases with user understanding of both its mechanisms and its limitations.
Pine Script®指標
由neeson1987提供
Pressure Windows [Lite]PRESSURE WINDOWS — Lite is a stripped-down time-cycle instrument based on Jim Hurst’s Time-Cycles method. It lets you define a primary cycle using two major lows and project basic harmonic subdivisions forward in time. The Lite version is designed to expose coarse timing pressure, not full cycle resolution. This is a structural timing tool — not a prediction engine. What the Lite Version Shows : A primary time cycle defined by two anchor points Single-level harmonic subdivision only: -1/2 (halves) -1/3 (thirds) Forward projection of the cycle into the future Internal verification lines inside the base cycle to check fit What it does not do (by design): No chained subdivisions (no 1/2→1/2 or 1/3→1/2) No higher-resolution nesting No regime logic or shaded “windows” Those belong to the full instrument. How to Use (Lite Workflow) : 1. Define the Cycle : Add the indicator to your chart Select Time 1 at a major low Select Time 2 at the next major low (You can fine-tune both in the settings) 2. Choose a Subdivision : In settings: None → only the primary cycle 1/2 → midpoint timing pressure 1/3 → third-based rhythm 3. Verify Before Trusting : Look at the subdivisions inside the base cycle: Do intermediate lows align with halves or thirds? If they don’t, don’t force it The Lite version is intentionally coarse — its job is to answer: “Is the market vibrating in 2s or 3s?” Not to overfit timing. Best Practices : Works best on higher timeframes (Daily / Weekly) Use Log scale for long-term structure (time remains linear) Treat projections as zones of pressure, not turning points Important Note : Pressure Windows — Lite limits subdivision depth on purpose, if you need nested timing resolution and finer pressure mapping, that’s what the full version is built for. *This script doesn't constitute investment advice and isn't created solely for qualified investors.
Pine Script®指標
由ThePineScripter提供
Moon Phases & Declinations - Chronos Capital [BETA]High-Precision Lunar Cycles: Moon Phases & Declinations (Swiss Ephemeris) Overview This indicator provides institutional-grade astronomical data directly on your chart. Unlike standard scripts that use basic sine-wave approximations, this tool implements the **Swiss Ephemeris algorithm**, the gold standard for high-precision celestial calculations. By tracking the Moon’s phases and its **Maximum/Minimum Declinations**, traders can identify potential "turning points" or "energy shifts" in market volatility often associated with lunar cycles. --- Key Features Ultra-High Precision: Calculations are accurate to within *seconds* of time, ensuring that the visual plot aligns perfectly with astronomical reality. Moon Phase Tracking: Distinct markers for New Moon, Full Moon, and Quarters. Lunar Declination Peaks: Automatically identifies when the moon reaches its *Maximum North* and *Maximum South* points (Lunar Extremes). Customizable Visuals: Toggle between background highlights, vertical lines, or plot signals to suit your trading style. --- Technical Accuracy This script is built using a ported version of the Swiss Ephemeris Positional Accuracy: Within 0.1 arcseconds. Time Accuracy: Within **~1-2 seconds** of official JPL data. Algorithm: Integration of the *ELP2000-85* lunar theory for maximum reliability over decades of historical data. --- ### **How to Use** 1. **Reversal Zones:** Watch for the Moon’s *Max/Min Declination* points, which often coincide with local tops or bottoms in trending markets. 2. **Volatility Shifts:** Use the *New Moon* and *Full Moon* markers to anticipate periods of increased or decreased market liquidity and volume. 3. **Confluence:** Best used in combination with your existing price action or momentum indicators to add a "time-based" filter to your entries. *Disclaimer: This tool is for educational and analytical purposes only. Lunar cycles are a study of time-based correlation, not a guaranteed financial signal.*
Pine Script®指標
由ChronosCapital提供
GUSI ProGUSI — Adaptive Bitcoin Cycle Risk Model Most on-chain metrics published on TradingView — such as NUPL, MVRV, or Puell Multiple — were once reliable in past cycles but have lost accuracy. The reason is simple: their trigger levels are static, while Bitcoin’s market structure changes over time. Tops have formed lower each cycle, yet the traditional horizontal thresholds remain unchanged. What GUSI does differently: It introduces sloped trigger functions that decrease over time, adapting each metric to Bitcoin’s maturing market. It applies long-term normalization methods (smoothing and z-score lookups) to reduce distortion from short-term volatility and extreme outliers. It only includes signals that remain valid across all Bitcoin cycles since 2011, discarding dozens of popular on-chain ideas that fail even after adjustment. How GUSI is built: GUSI is not just a mashup of indicators. Each component is a proprietary, modified version of a known on-chain signal: Logarithmic MACD with declining trigger bands MVRV-Z Score Regression with cycle-aware slopes Net Unrealized Profit/Loss Ratio normalized with dynamic z-scores Puell Multiple with logarithmic decay Weekly RSI momentum filter for bottoms Optional Pi Cycle Top logic with sloped moving averages These are combined into a composite risk scoring system (0–100). Every signal contributes to the score according to user-defined weights, and each can be toggled on/off. The end result is a flexible model that adapts to long-term changes in Bitcoin’s cycles while staying transparent in its logic. How to use it: Scores near 97 indicate historically high-risk conditions (cycle tops). Scores near 2.5 highlight deep accumulation zones (cycle bottoms). Background colors and labels make the conditions clear, and built-in alerts let you automate your strategy. GUSI is designed for the INDEX:BTCUSD 1D chart and works best when viewed in that context. In short: GUSI makes classic on-chain indicators relevant again by adapting them to Bitcoin’s evolving market cycles. Instead of relying on static thresholds that stop working over time, it introduces dynamic slopes, normalization, and a weighted composite framework that traders can adjust themselves.
Pine Script®指標
由PanduPanduPandu提供