Bitcoin Power Law Oscillator [InvestorUnknown]The Bitcoin Power Law Oscillator is a specialized tool designed for long-term mean-reversion analysis of Bitcoin's price relative to a theoretical midline derived from the Bitcoin Power Law model (made by capriole_charles). This oscillator helps investors identify whether Bitcoin is currently overbought, oversold, or near its fair value according to this mathematical model.
Key Features:
Power Law Model Integration: The oscillator is based on the midline of the Bitcoin Power Law, which is calculated using regression coefficients (A and B) applied to the logarithm of the number of days since Bitcoin’s inception. This midline represents a theoretical fair value for Bitcoin over time.
Midline Distance Calculation: The distance between Bitcoin’s current price and the Power Law midline is computed as a percentage, indicating how far above or below the price is from this theoretical value.
float a = input.float (-16.98212206, 'Regression Coef. A', group = "Power Law Settings")
float b = input.float (5.83430649, 'Regression Coef. B', group = "Power Law Settings")
normalization_start_date = timestamp(2011,1,1)
calculation_start_date = time == timestamp(2010, 7, 19, 0, 0) // First BLX Bitcoin Date
int days_since = request.security('BNC:BLX', 'D', ta.barssince(calculation_start_date))
bar() =>
= request.security('BNC:BLX', 'D', bar())
int offset = 564 // days between 2009/1/1 and "calculation_start_date"
int days = days_since + offset
float e = a + b * math.log10(days)
float y = math.pow(10, e)
float midline_distance = math.round((y / btc_close - 1.0) * 100)
Oscillator Normalization: The raw distance is converted into a normalized oscillator, which fluctuates between -1 and 1. This normalization adjusts the oscillator to account for historical extremes, making it easier to compare current conditions with past market behavior.
float oscillator = -midline_distance
var float min = na
var float max = na
if (oscillator > max or na(max)) and time >= normalization_start_date
max := oscillator
if (min > oscillator or na(min)) and time >= normalization_start_date
min := oscillator
rescale(float value, float min, float max) =>
(2 * (value - min) / (max - min)) - 1
normalized_oscillator = rescale(oscillator, min, max)
Overbought/Oversold Identification: The oscillator provides a clear visual representation, where values near 1 suggest Bitcoin is overbought, and values near -1 indicate it is oversold. This can help identify potential reversal points or areas of significant market imbalance.
Optional Moving Average: Users can overlay a moving average (either SMA or EMA) on the oscillator to smooth out short-term fluctuations and focus on longer-term trends. This is particularly useful for confirming trend reversals or persistent overbought/oversold conditions.
This indicator is particularly useful for long-term Bitcoin investors who wish to gauge the market's mean-reversion tendencies based on a well-established theoretical model. By focusing on the Power Law’s midline, users can gain insights into whether Bitcoin’s current price deviates significantly from what historical trends would suggest as a fair value.
Wyszukaj w skryptach "oscillator"
Normalised T3 Oscillator [BackQuant]Normalised T3 Oscillator
The Normalised T3 Oscillator is an technical indicator designed to provide traders with a refined measure of market momentum by normalizing the T3 Moving Average. This tool was developed to enhance trading decisions by smoothing price data and reducing market noise, allowing for clearer trend recognition and potential signal generation. Below is a detailed breakdown of the Normalised T3 Oscillator, its methodology, and its application in trading scenarios.
1. Conceptual Foundation and Definition of T3
The T3 Moving Average, originally proposed by Tim Tillson, is renowned for its smoothness and responsiveness, achieved through a combination of multiple Exponential Moving Averages and a volume factor. The Normalised T3 Oscillator extends this concept by normalizing these values to oscillate around a central zero line, which aids in highlighting overbought and oversold conditions.
2. Normalization Process
Normalization in this context refers to the adjustment of the T3 values to ensure that the oscillator provides a standard range of output. This is accomplished by calculating the lowest and highest values of the T3 over a user-defined period and scaling the output between -0.5 to +0.5. This process not only aids in standardizing the indicator across different securities and time frames but also enhances comparative analysis.
3. Integration of the Oscillator and Moving Average
A unique feature of the Normalised T3 Oscillator is the inclusion of a secondary smoothing mechanism via a moving average of the oscillator itself, selectable from various types such as SMA, EMA, and more. This moving average acts as a signal line, providing potential buy or sell triggers when the oscillator crosses this line, thus offering dual layers of analysis—momentum and trend confirmation.
4. Visualization and User Interaction
The indicator is designed with user interaction in mind, featuring customizable parameters such as the length of the T3, normalization period, and type of moving average used for signals. Additionally, the oscillator is plotted with a color-coded scheme that visually represents different strength levels of the market conditions, enhancing readability and quick decision-making.
5. Practical Applications and Strategy Integration
Traders can leverage the Normalised T3 Oscillator in various trading strategies, including trend following, counter-trend plays, and as a component of a broader trading system. It is particularly useful in identifying turning points in the market or confirming ongoing trends. The clear visualization and customizable nature of the oscillator facilitate its adaptation to different trading styles and market environments.
6. Advanced Features and Customization
Further enhancing its utility, the indicator includes options such as painting candles according to the trend, showing static levels for quick reference, and alerts for crossover and crossunder events, which can be integrated into automated trading systems. These features allow for a high degree of personalization, enabling traders to mold the tool according to their specific trading preferences and risk management requirements.
7. Theoretical Justification and Empirical Usage
The use of the T3 smoothing mechanism combined with normalization is theoretically sound, aiming to reduce lag and false signals often associated with traditional moving averages. The practical effectiveness of the Normalised T3 Oscillator should be validated through rigorous backtesting and adjustment of parameters to match historical market conditions and volatility.
8. Conclusion and Utility in Market Analysis
Overall, the Normalised T3 Oscillator by BackQuant stands as a sophisticated tool for market analysis, providing traders with a dynamic and adaptable approach to gauging market momentum. Its development is rooted in the understanding of technical nuances and the demand for a more stable, responsive, and customizable trading indicator.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
Bilateral Stochastic Oscillator - For The Sake Of EfficiencyIntroduction
The stochastic oscillator is a feature scaling method commonly used in technical analysis, this method is the same as the running min-max normalization method except that the stochastic oscillator is in a range of (0,100) while min-max normalization is in a range of (0,1). The stochastic oscillator in itself is efficient since it tell's us when the price reached its highest/lowest or crossed this average, however there could be ways to further develop the stochastic oscillator, this is why i propose this new indicator that aim to show all the information a classical stochastic oscillator would give with some additional features.
Min-Max Derivation
The min-max normalization of the price is calculated as follow : (price - min)/(max - min) , this calculation is efficient but there is alternates forms such as :
price - (max - min) - min/(max - min)
This alternate form is the one i chosen to make the indicator except that both range (max - min) are smoothed with a simple moving average, there are also additional modifications that you can see on the code.
The Indicator
The indicator return two main lines, in blue the bull line who show the buying force and in red the bear line who show the selling force.
An orange line show the signal line who represent the moving average of the max(bull,bear), this line aim to show possible exit/reversals points for the current trend.
Length control the highest/lowest period as well as the smoothing amount, signal length control the moving average period of the signal line, the pre-filtering setting indicate which smoothing method will be used to smooth the input source before applying normalization.
The default pre-filtering method is the sma.
The ema method is slightly faster as you can see above.
The triangular moving average is the moving average of another moving average, the impulse response of this filter is a triangular function hence its name. This moving average is really smooth.
The lsma or least squares moving average is the fastest moving average used in this indicator, this filter try to best fit a linear function to the data in a certain window by using the least squares method.
No filtering will use the source price without prior smoothing for the indicator calculation.
Relationship With The Stochastic Oscillator
The crosses between the bull and bear line mean that the stochastic oscillator crossed the 50 level. When the Bull line is equal to 0 this mean that the stochastic oscillator is equal to 0 while a bear line equal to 0 mean a stochastic oscillator equal to 100.
The indicator and below a stochastic oscillator of both period 100
Using Levels
Unlike a stochastic oscillator who would clip at the 0 and 100 level the proposed indicator is not heavily constrained in a range like the stochastic oscillator, this mean that you can apply levels to trigger signals
Possible levels could be 1,2,3... even if the indicator rarely go over 3.
Its then possible to create strategies using such levels as support or resistance one.
Conclusion
I've showed a modified stochastic oscillator who aim to show additional information to the user while keeping all the information a classical stochastic oscillator would give. The proposed indicator is no longer constrained in an hard range and posses more liberty to exploit its scale which in return allow to create strategies based on levels.
For pinescript users what you can learn from this is that alternates forms of specific formulas can be extremely interesting to modify, changes can be really surprising so if you are feeling stuck, modifying alternates forms of know indicators can give great results, use tools such as sympy gamma to get alternates forms of formulas.
Thanks for reading !
If you are looking for something or just want to say thanks try to pm me :)
FEDFUNDS Rate Divergence Oscillator [BackQuant]FEDFUNDS Rate Divergence Oscillator
1. Concept and Rationale
The United States Federal Funds Rate is the anchor around which global dollar liquidity and risk-free yield expectations revolve. When the Fed hikes, borrowing costs rise, liquidity tightens and most risk assets encounter head-winds. When it cuts, liquidity expands, speculative appetite often recovers. Bitcoin, a 24-hour permissionless asset sometimes described as “digital gold with venture-capital-like convexity,” is particularly sensitive to macro-liquidity swings.
The FED Divergence Oscillator quantifies the behavioural gap between short-term monetary policy (proxied by the effective Fed Funds Rate) and Bitcoin’s own percentage price change. By converting each series into identical rate-of-change units, subtracting them, then optionally smoothing the result, the script produces a single bounded-yet-dynamic line that tells you, at a glance, whether Bitcoin is outperforming or underperforming the policy backdrop—and by how much.
2. Data Pipeline
• Fed Funds Rate – Pulled directly from the FRED database via the ticker “FRED:FEDFUNDS,” sampled at daily frequency to synchronise with crypto closes.
• Bitcoin Price – By default the script forces a daily timeframe so that both series share time alignment, although you can disable that and plot the oscillator on intraday charts if you prefer.
• User Source Flexibility – The BTC series is not hard-wired; you can select any exchange-specific symbol or even swap BTC for another crypto or risk asset whose interaction with the Fed rate you wish to study.
3. Math under the Hood
(1) Rate of Change (ROC) – Both the Fed rate and BTC close are converted to percent return over a user-chosen lookback (default 30 bars). This means a cut from 5.25 percent to 5.00 percent feeds in as –4.76 percent, while a climb from 25 000 to 30 000 USD in BTC over the same window converts to +20 percent.
(2) Divergence Construction – The script subtracts the Fed ROC from the BTC ROC. Positive values show BTC appreciating faster than policy is tightening (or falling slower than the rate is cutting); negative values show the opposite.
(3) Optional Smoothing – Macro series are noisy. Toggle “Apply Smoothing” to calm the line with your preferred moving-average flavour: SMA, EMA, DEMA, TEMA, RMA, WMA or Hull. The default EMA-25 removes day-to-day whips while keeping turning points alive.
(4) Dynamic Colour Mapping – Rather than using a single hue, the oscillator line employs a gradient where deep greens represent strong bullish divergence and dark reds flag sharp bearish divergence. This heat-map approach lets you gauge intensity without squinting at numbers.
(5) Threshold Grid – Five horizontal guides create a structured regime map:
• Lower Extreme (–50 pct) and Upper Extreme (+50 pct) identify panic capitulations and euphoria blow-offs.
• Oversold (–20 pct) and Overbought (+20 pct) act as early warning alarms.
• Zero Line demarcates neutral alignment.
4. Chart Furniture and User Interface
• Oscillator fill with a secondary DEMA-30 “shader” offers depth perception: fat ribbons often precede high-volatility macro shifts.
• Optional bar-colouring paints candles green when the oscillator is above zero and red below, handy for visual correlation.
• Background tints when the line breaches extreme zones, making macro inflection weeks pop out in the replay bar.
• Everything—line width, thresholds, colours—can be customised so the indicator blends into any template.
5. Interpretation Guide
Macro Liquidity Pulse
• When the oscillator spends weeks above +20 while the Fed is still raising rates, Bitcoin is signalling liquidity tolerance or an anticipatory pivot view. That condition often marks the embryonic phase of major bull cycles (e.g., March 2020 rebound).
• Sustained prints below –20 while the Fed is already dovish indicate risk aversion or idiosyncratic crypto stress—think exchange scandals or broad flight to safety.
Regime Transition Signals
• Bullish cross through zero after a long sub-zero stint shows Bitcoin regaining upward escape velocity versus policy.
• Bearish cross under zero during a hiking cycle tells you monetary tightening has finally started to bite.
Momentum Exhaustion and Mean-Reversion
• Touches of +50 (or –50) come rarely; they are statistically stretched events. Fade strategies either taking profits or hedging have historically enjoyed positive expectancy.
• Inside-bar candlestick patterns or lower-timeframe bearish engulfings simultaneously with an extreme overbought print make high-probability short scalp setups, especially near weekly resistance. The same logic mirrors for oversold.
Pair Trading / Relative Value
• Combine the oscillator with spreads like BTC versus Nasdaq 100. When both the FED Divergence oscillator and the BTC–NDQ relative-strength line roll south together, the cross-asset confirmation amplifies conviction in a mean-reversion short.
• Swap BTC for miners, altcoins or high-beta equities to test who is the divergence leader.
Event-Driven Tactics
• FOMC days: plot the oscillator on an hourly chart (disable ‘Force Daily TF’). Watch for micro-structural spikes that resolve in the first hour after the statement; rapid flips across zero can front-run post-FOMC swings.
• CPI and NFP prints: extremes reached into the release often mean positioning is one-sided. A reversion toward neutral in the first 24 hours is common.
6. Alerts Suite
Pre-bundled conditions let you automate workflows:
• Bullish / Bearish zero crosses – queue spot or futures entries.
• Standard OB / OS – notify for first contact with actionable zones.
• Extreme OB / OS – prime time to review hedges, take profits or build contrarian swing positions.
7. Parameter Playground
• Shorten ROC Lookback to 14 for tactical traders; lengthen to 90 for macro investors.
• Raise extreme thresholds (for example ±80) when plotting on altcoins that exhibit higher volatility than BTC.
• Try HMA smoothing for responsive yet smooth curves on intraday charts.
• Colour-blind users can easily swap bull and bear palette selections for preferred contrasts.
8. Limitations and Best Practices
• The Fed Funds series is step-wise; it only changes on meeting days. Rapid BTC oscillations in between may dominate the calculation. Keep that perspective when interpreting very high-frequency signals.
• Divergence does not equal causation. Crypto-native catalysts (ETF approvals, hack headlines) can overwhelm macro links temporarily.
• Use in conjunction with classical confirmation tools—order-flow footprints, market-profile ledges, or simple price action to avoid “pure-indicator” traps.
9. Final Thoughts
The FEDFUNDS Rate Divergence Oscillator distills an entire macro narrative monetary policy versus risk sentiment into a single colourful heartbeat. It will not magically predict every pivot, yet it excels at framing market context, spotting stretches and timing regime changes. Treat it as a strategic compass rather than a tactical sniper scope, combine it with sound risk management and multi-factor confirmation, and you will possess a robust edge anchored in the world’s most influential interest-rate benchmark.
Trade consciously, stay adaptive, and let the policy-price tension guide your roadmap.
Chaikin Oscillator Multi-Timeframe BiasOverview
Chaikin Oscillator Multi-Timeframe Bias is an indicator designed to help traders align with institutional buying and selling activity by analyzing Chaikin Oscillator signals across two timeframes—a higher timeframe (HTF) for trend bias and a lower timeframe (LTF) for timing. This dual-confirmation model helps traders avoid false breakouts and trade in sync with market momentum and accumulation or distribution dynamics.
Core Concepts
The Chaikin Oscillator measures the momentum of accumulation and distribution based on price and volume. Institutional traders typically accumulate slowly and steadily, and the Chaikin Oscillator helps reveal this pattern. Multi-timeframe analysis confirms whether short-term price action supports the longer-term trend. This indicator applies a smoothing EMA to each Chaikin Oscillator to help confirm direction and reduce noise.
How to Use the Indicator
Start by selecting your timeframes. The higher timeframe, set by default to Daily, establishes the broader directional bias. The lower timeframe, defaulted to 30 minutes, identifies short-term momentum confirmation. The indicator displays one of five labels: CALL Bias, CALL Wait, PUT Bias, PUT Wait, or NEUTRAL. CALL Bias means both HTF and LTF are bullish, signaling a potential opportunity for long or call trades. CALL Wait indicates that the HTF is bullish, but the LTF hasn’t confirmed yet. PUT Bias signals bearish alignment in both HTF and LTF, while PUT Wait indicates HTF is bearish and LTF has not yet confirmed. NEUTRAL means there is no alignment between timeframes and directional trades are not advised.
Interpretation
When the Chaikin Oscillator is above zero and also above its EMA, this indicates bullish momentum and accumulation. When the oscillator is below zero and below its EMA, it suggests bearish momentum and distribution. Bias labels identify when both timeframes are aligned for a higher-probability directional setup. When a “Wait” label appears, it means one timeframe has confirmed bias but the other has not, suggesting the trader should monitor closely but delay entry.
Notes
This indicator includes alerts for both CALL and PUT bias confirmation when both timeframes are aligned. It works on all asset classes, including stocks, ETFs, cryptocurrencies, and futures. Timeframes are fully customizable, and users may explore combinations such as 1D and 1H, or 4H and 15M depending on their strategy. For best results, consider pairing this tool with volume, volatility, or price action analysis.
Regime Classifier Oscillator (AiBitcoinTrend)The Regime Classifier Oscillator (AiBitcoinTrend) is an advanced tool for understanding market structure and detecting dynamic price regimes. By combining filtered price trends, clustering algorithms, and an adaptive oscillator, it provides traders with detailed insights into market phases, including accumulation, distribution, advancement, and decline.
This innovative tool simplifies market regime classification, enabling traders to align their strategies with evolving market conditions effectively.
👽 What is a Regime Classifier, and Why is it Useful?
A Regime Classifier is a concept in financial analysis that identifies distinct market conditions or "regimes" based on price behavior and volatility. These regimes often correspond to specific phases of the market, such as trends, consolidations, or periods of high or low volatility. By classifying these regimes, traders and analysts can better understand the underlying market dynamics, allowing them to adapt their strategies to suit prevailing conditions.
👽 Common Uses in Finance
Risk Management: Identifying high-volatility regimes helps traders adjust position sizes or hedge risks.
Strategy Optimization: Traders tailor their approaches—trend-following strategies in trending regimes, mean-reversion strategies in consolidations.
Forecasting: Understanding the current regime aids in predicting potential transitions, such as a shift from accumulation to an upward breakout.
Portfolio Allocation: Investors allocate assets differently based on market regimes, such as increasing cash positions in high-volatility environments.
👽 Why It’s Important
Markets behave differently under varying conditions. A regime classifier provides a structured way to analyze these changes, offering a systematic approach to decision-making. This improves both accuracy and confidence in navigating diverse market scenarios.
👽 How We Implemented the Regime Classifier in This Indicator
The Regime Classifier Oscillator takes the foundational concept of market regime classification and enhances it with advanced computational techniques, making it highly adaptive.
👾 Median Filtering: We smooth price data using a custom median filter to identify significant trends while eliminating noise. This establishes a baseline for price movement analysis.
👾 Clustering Model: Using clustering techniques, the indicator classifies volatility and price trends into distinct regimes:
Advance: Strong upward trends with low volatility.
Decline: Downward trends marked by high volatility.
Accumulation: Consolidation phases with subdued volatility.
Distribution: Topping or bottoming patterns with elevated volatility.
This classification leverages historical price data to refine cluster boundaries dynamically, ensuring adaptive and accurate detection of market states.
Volatility Classification: Price volatility is analyzed through rolling windows, separating data into high and low volatility clusters using distance-based assignments.
Price Trends: The interaction of price levels with the filtered trendline and volatility clusters determines whether the market is advancing, declining, accumulating, or distributing.
👽 Dynamic Cycle Oscillator (DCO):
Captures cyclic behavior and overlays it with smoothed oscillations, providing real-time feedback on price momentum and potential reversals.
Regime Visualization:
Regimes are displayed with intuitive labels and background colors, offering clear, actionable insights directly on the chart.
👽 Why This Implementation Stands Out
Dynamic and Adaptive: The clustering and refit mechanisms adapt to changing market conditions, ensuring relevance across different asset classes and timeframes.
Comprehensive Insights: By combining price trends, volatility, and cyclic behaviors, the indicator provides a holistic view of the market.
This implementation bridges the gap between theoretical regime classification and practical trading needs, making it a powerful tool for both novice and experienced traders.
👽 Applications
👾 Regime-Based Trading Strategies
Traders can use the regime classifications to adapt their strategies effectively:
Advance & Accumulation: Favorable for entering or holding long positions.
Decline & Distribution: Opportunities for short positions or risk management.
👾 Oscillator Insights for Trend Analysis
Overbought/oversold conditions: Early warning of potential reversals.
Dynamic trends: Highlights the strength of price momentum.
👽 Indicator Settings
👾 Filter and Classification Settings
Filter Window Size: Controls trend detection sensitivity.
ATR Lookback: Adjusts the threshold for regime classification.
Clustering Window & Refit Interval: Fine-tunes regime accuracy.
👾 Oscillator Settings
Dynamic Cycle Oscillator Lookback: Defines the sensitivity of cycle detection.
Smoothing Factor: Balances responsiveness and stability.
Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
Exposure Oscillator (Cumulative 0 to ±100%)
Exposure Oscillator (Cumulative 0 to ±100%)
This Pine Script indicator plots an "Exposure Oscillator" on the chart, which tracks the cumulative market exposure from a range of technical buy and sell signals. The exposure is measured on a scale from -100% (maximum short exposure) to +100% (maximum long exposure), helping traders assess the strength of their position in the market. It provides an intuitive visual cue to aid decision-making for trend-following strategies.
Buy Signals (Increase Exposure Score by +10%)
Buy Signal 1 (Cross Above 21 EMA):
This signal is triggered when the price crosses above the 21-period Exponential Moving Average (EMA), where the current bar closes above the EMA21, and the previous bar closed below the EMA21. This indicates a potential upward price movement as the market shifts into a bullish trend.
buySignal1 = ta.crossover(close, ema21)
Buy Signal 2 (Trending Above 21 EMA):
This signal is triggered when the price closes above the 21-period EMA for each of the last 5 bars, indicating a sustained bullish trend. It confirms that the price is consistently above the EMA21 for a significant period.
buySignal2 = ta.barssince(close <= ema21) > 5
Buy Signal 3 (Living Above 21 EMA):
This signal is triggered when the price has closed above the 21-period EMA for each of the last 15 bars, demonstrating a strong, prolonged uptrend.
buySignal3 = ta.barssince(close <= ema21) > 15
Buy Signal 4 (Cross Above 50 SMA):
This signal is triggered when the price crosses above the 50-period Simple Moving Average (SMA), where the current bar closes above the 50 SMA, and the previous bar closed below it. It indicates a shift toward bullish momentum.
buySignal4 = ta.crossover(close, sma50)
Buy Signal 5 (Cross Above 200 SMA):
This signal is triggered when the price crosses above the 200-period Simple Moving Average (SMA), where the current bar closes above the 200 SMA, and the previous bar closed below it. This suggests a long-term bullish trend.
buySignal5 = ta.crossover(close, sma200)
Buy Signal 6 (Low Above 50 SMA):
This signal is true when the lowest price of the current bar is above the 50-period SMA, indicating strong bullish pressure as the price maintains itself above the moving average.
buySignal6 = low > sma50
Buy Signal 7 (Accumulation Day):
An accumulation day occurs when the closing price is in the upper half of the daily range (greater than 50%) and the volume is larger than the previous bar's volume, suggesting buying pressure and accumulation.
buySignal7 = (close - low) / (high - low) > 0.5 and volume > volume
Buy Signal 8 (Higher High):
This signal occurs when the current bar’s high exceeds the highest high of the previous 14 bars, indicating a breakout or strong upward momentum.
buySignal8 = high > ta.highest(high, 14)
Buy Signal 9 (Key Reversal Bar):
This signal is generated when the stock opens below the low of the previous bar but rallies to close above the previous bar’s high, signaling a potential reversal from bearish to bullish.
buySignal9 = open < low and close > high
Buy Signal 10 (Distribution Day Fall Off):
This signal is triggered when a distribution day (a day with high volume and a close near the low of the range) "falls off" the rolling 25-bar period, indicating the end of a bearish trend or selling pressure.
buySignal10 = ta.barssince(close < sma50 and close < sma50) > 25
Sell Signals (Decrease Exposure Score by -10%)
Sell Signal 1 (Cross Below 21 EMA):
This signal is triggered when the price crosses below the 21-period Exponential Moving Average (EMA), where the current bar closes below the EMA21, and the previous bar closed above it. It suggests that the market may be shifting from a bullish trend to a bearish trend.
sellSignal1 = ta.crossunder(close, ema21)
Sell Signal 2 (Trending Below 21 EMA):
This signal is triggered when the price closes below the 21-period EMA for each of the last 5 bars, indicating a sustained bearish trend.
sellSignal2 = ta.barssince(close >= ema21) > 5
Sell Signal 3 (Living Below 21 EMA):
This signal is triggered when the price has closed below the 21-period EMA for each of the last 15 bars, suggesting a strong downtrend.
sellSignal3 = ta.barssince(close >= ema21) > 15
Sell Signal 4 (Cross Below 50 SMA):
This signal is triggered when the price crosses below the 50-period Simple Moving Average (SMA), where the current bar closes below the 50 SMA, and the previous bar closed above it. It indicates the start of a bearish trend.
sellSignal4 = ta.crossunder(close, sma50)
Sell Signal 5 (Cross Below 200 SMA):
This signal is triggered when the price crosses below the 200-period Simple Moving Average (SMA), where the current bar closes below the 200 SMA, and the previous bar closed above it. It indicates a long-term bearish trend.
sellSignal5 = ta.crossunder(close, sma200)
Sell Signal 6 (High Below 50 SMA):
This signal is true when the highest price of the current bar is below the 50-period SMA, indicating weak bullishness or a potential bearish reversal.
sellSignal6 = high < sma50
Sell Signal 7 (Distribution Day):
A distribution day is identified when the closing range of a bar is less than 50% and the volume is larger than the previous bar's volume, suggesting that selling pressure is increasing.
sellSignal7 = (close - low) / (high - low) < 0.5 and volume > volume
Sell Signal 8 (Lower Low):
This signal occurs when the current bar's low is less than the lowest low of the previous 14 bars, indicating a breakdown or strong downward momentum.
sellSignal8 = low < ta.lowest(low, 14)
Sell Signal 9 (Downside Reversal Bar):
A downside reversal bar occurs when the stock opens above the previous bar's high but falls to close below the previous bar’s low, signaling a reversal from bullish to bearish.
sellSignal9 = open > high and close < low
Sell Signal 10 (Distribution Cluster):
This signal is triggered when a distribution day occurs three times in the rolling 7-bar period, indicating significant selling pressure.
sellSignal10 = ta.valuewhen((close < low) and volume > volume , 1, 7) >= 3
Theme Mode:
Users can select the theme mode (Auto, Dark, or Light) to match the chart's background or to manually choose a light or dark theme for the oscillator's appearance.
Exposure Score Calculation: The script calculates a cumulative exposure score based on a series of buy and sell signals.
Buy signals increase the exposure score, while sell signals decrease it. Each signal impacts the score by ±10%.
Signal Conditions: The buy and sell signals are derived from multiple conditions, including crossovers with moving averages (EMA21, SMA50, SMA200), trend behavior, and price/volume analysis.
Oscillator Visualization: The exposure score is visualized as a line on the chart, changing color based on whether the exposure is positive (long position) or negative (short position). It is limited to the range of -100% to +100%.
Position Type: The indicator also indicates the position type based on the exposure score, labeling it as "Long," "Short," or "Neutral."
Horizontal Lines: Reference lines at 0%, 100%, and -100% visually mark neutral, increasing long, and increasing short exposure levels.
Exposure Table: A table displays the current exposure level (in percentage) and position type ("Long," "Short," or "Neutral"), updated dynamically based on the oscillator’s value.
Inputs:
Theme Mode: Choose "Auto" to use the default chart theme, or manually select "Dark" or "Light."
Usage:
This oscillator is designed to help traders track market sentiment, gauge exposure levels, and manage risk. It can be used for long-term trend-following strategies or short-term trades based on moving average crossovers and volume analysis.
The oscillator operates in conjunction with the chart’s price action and provides a visual representation of the market’s current trend strength and exposure.
Important Considerations:
Risk Management: While the exposure score provides valuable insight, it should be combined with other risk management tools and analysis for optimal trading decisions.
Signal Sensitivity: The accuracy and effectiveness of the signals depend on market conditions and may require adjustments based on the user’s trading strategy or timeframe.
Disclaimer:
This script is for educational purposes only. Trading involves significant risk, and users should carefully evaluate all market conditions and apply appropriate risk management strategies before using this tool in live trading environments.
S&P Short-Range Oscillator**SHOULD BE USED ON THE S&P 500 ONLY**
The S&P Short-Range Oscillator (SRO), inspired by the principles of Jim Cramer's oscillator, is a technical analysis tool designed to help traders identify potential buy and sell signals in the stock market, specifically for the S&P 500 index. The SRO combines several market indicators to provide a normalized measure of market sentiment, assisting traders in making informed decisions.
The SRO utilizes two simple moving averages (SMAs) of different lengths: a 5-day SMA and a 10-day SMA. It also incorporates the daily price change and market breadth (the net change of closing prices). The 5-day and 10-day SMAs are calculated based on the closing prices. The daily price change is determined by subtracting the opening price from the closing price. Market breadth is calculated as the difference between the current closing price and the previous closing price.
The raw value of the oscillator, referred to as SRO Raw, is the sum of the daily price change, the 5-day SMA, the 10-day SMA, and the market breadth. This raw value is then normalized using its mean and standard deviation over a 20-day period, ensuring that the oscillator is centered and maintains a consistent scale. Finally, the normalized value is scaled to fit within the range of -15 to 15.
When interpreting the SRO, a value below -5 indicates that the market is potentially oversold, suggesting it might be a good time to start buying stocks as the market could be poised for a rebound. Conversely, a value above 5 suggests that the market is potentially overbought. In this situation, it may be prudent to hold on to existing positions or consider selling if you have substantial gains.
The SRO is visually represented as a blue line on a chart, making it easy to track its movements. Red and green horizontal lines mark the overbought (5) and oversold (-5) levels, respectively. Additionally, the background color changes to light red when the oscillator is overbought and light green when it is oversold, providing a clear visual cue.
By incorporating the S&P Short-Range Oscillator into your trading strategy, you can gain valuable insights into market conditions and make more informed decisions about when to buy, sell, or hold your stocks. However, always consider other market factors and perform your own analysis before making any trading decisions.
The S&P Short-Range Oscillator is a powerful tool for traders looking to gain insights into market sentiment. It provides clear buy and sell signals through its combination of multiple indicators and normalization process. However, traders should be aware of its lagging nature and potential complexity, and use it in conjunction with other analysis methods for the best results.
Disclaimer
The S&P Short-Range Oscillator is for informational purposes only and should not be considered financial advice. Trading involves risk, and you should conduct your own research or consult a financial advisor before making investment decisions. The author is not responsible for any losses incurred from using this indicator. Use at your own risk.
Momentum & Squeeze Oscillator [UAlgo]The Momentum & Squeeze Oscillator is a technical analysis tool designed to help traders identify shifts in market momentum and potential squeeze conditions. This oscillator combines multiple timeframes and periods to provide a detailed view of market dynamics. It enhances the decision-making process for both short-term and long-term traders by visualizing momentum with customizable colors and alerts.
🔶 Key Features
Custom Timeframe Selection: Allows users to select a custom timeframe for oscillator calculations, providing flexibility in analyzing different market periods.
Recalculation Option: Enables or disables the recalculation of the indicator, offering more control over real-time data processing.
Squeeze Background Visualization: Highlights potential squeeze conditions with a background color, helping traders quickly spot consolidation periods.
Adjustable Squeeze Sensitivity: Users can modify the sensitivity of the squeeze detection, tailoring the indicator to their specific trading style and market conditions.
Bar Coloring Condition: Option to color the price bars based on momentum conditions, enhancing the visual representation of market trends.
Threshold Bands: Option to fill threshold bands for a clearer visualization of overbought and oversold levels.
Reference Lines: Display reference lines for overbought, oversold, and mid-levels, aiding in quick assessment of momentum extremes.
Multiple Output Modes: Offers different output visualization modes, including:
ALL: Displays all calculated momentum values (fast, medium, slow).
AVG: Shows the average momentum, providing a consolidated view.
STD: Displays the standard deviation of momentum, useful for understanding volatility.
Alerts: Configurable alerts for key momentum events such as crossovers and squeeze conditions, keeping traders informed of important market changes.
🔶 Usage
The Momentum & Squeeze Oscillator can be used for various trading purposes:
Trend Identification: Use the oscillator to determine the direction and strength of market trends. By analyzing the average, fast, medium, and slow momentum lines, traders can gain insights into short-term and long-term market movements.
Squeeze Detection: The indicator highlights periods of low volatility (squeeze conditions) which often precede significant price movements. Traders can use this information to anticipate and prepare for potential breakouts.
Overbought/Oversold Conditions: The oscillator helps identify overbought and oversold conditions, indicating potential reversal points. This is particularly useful for timing entry and exit points in the market.
Momentum Shifts: By monitoring the crossover of momentum lines with key levels (e.g., the 50 level), traders can spot shifts in market momentum, allowing them to adjust their positions accordingly.
🔶 Disclaimer:
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Persistent Homology Based Trend Strength OscillatorPersistent Homology Based Trend Strength Oscillator
The Persistent Homology Based Trend Strength Oscillator is a unique and powerful tool designed to measure the persistence of market trends over a specified rolling window. By applying the principles of persistent homology, this indicator provides traders with valuable insights into the strength and stability of uptrends and downtrends, helping to inform better trading decisions.
What Makes This Indicator Original?
This indicator's originality lies in its application of persistent homology , a method from topological data analysis, to financial markets. Persistent homology examines the shape and features of data across multiple scales, identifying patterns that persist as the scale changes. By adapting this concept, the oscillator tracks the persistence of uptrends and downtrends in price data, offering a novel approach to trend analysis.
Concepts Underlying the Calculations:
Persistent Homology: This method identifies features such as clusters, holes, and voids that persist as the scale changes. In the context of this indicator, it tracks the duration and stability of price trends.
Rolling Window Analysis: The oscillator uses a specified window size to calculate the average length of uptrends and downtrends, providing a dynamic view of trend persistence over time.
Threshold-Based Trend Identification: It differentiates between uptrends and downtrends based on specified thresholds for price changes, ensuring precision in trend detection.
How It Works:
The oscillator monitors consecutive changes in closing prices to identify uptrends and downtrends.
An uptrend is detected when the closing price increase exceeds a specified positive threshold.
A downtrend is detected when the closing price decrease exceeds a specified negative threshold.
The lengths of these trends are recorded and averaged over the chosen window size.
The Trend Persistence Index is calculated as the difference between the average uptrend length and the average downtrend length, providing a measure of trend persistence.
How Traders Can Use It:
Identify Trend Strength: The Trend Persistence Index offers a clear measure of the strength and stability of uptrends and downtrends. A higher value indicates stronger and more persistent uptrends, while a lower value suggests stronger and more persistent downtrends.
Spot Trend Reversals: Significant shifts in the Trend Persistence Index can signal potential trend reversals. For instance, a transition from positive to negative values might indicate a shift from an uptrend to a downtrend.
Confirm Trends: Use the Trend Persistence Index alongside other technical indicators to confirm the strength and duration of trends, enhancing the accuracy of your trading signals.
Manage Risk: Understanding trend persistence can help traders manage risk by identifying periods of high trend stability versus periods of potential volatility. This can be crucial for timing entries and exits.
Example Usage:
Default Settings: Start with the default settings to get a feel for the oscillator’s behavior. Observe how the Trend Persistence Index reacts to different market conditions.
Adjust Thresholds: Fine-tune the positive and negative thresholds based on the asset's volatility to improve trend detection accuracy.
Combine with Other Indicators: Use the Persistent Homology Based Trend Strength Oscillator in conjunction with other technical indicators such as moving averages, RSI, or MACD for a comprehensive analysis.
Backtesting: Conduct backtesting to see how the oscillator would have performed in past market conditions, helping you to refine your trading strategy.
CME Gap Oscillator [CryptoSea]Introducing the CME Gap Oscillator , a pioneering tool designed to illuminate the significance of market gaps through the lens of the Chicago Mercantile Exchange (CME). By leveraging gap sizes in relation to the Average True Range (ATR), this indicator offers a unique perspective on market dynamics, particularly around the critical weekly close periods.
Key Features
Gap Measurement : At its core, the CME Oscillator quantifies the size of weekend gaps in the context of the market's volatility, using the ATR to standardize this measurement.
Dynamic Levels : Incorporating a dynamic extreme level calculation, the tool adapts to current market conditions, providing real-time insights into significant gap sizes and their implications.
Band Analysis : Through the introduction of upper and lower bands, based on standard deviations, traders can visually assess the oscillator's position relative to typical market ranges.
Enhanced Insights : A built-in table tracks the frequency of the oscillator's breaches beyond these bands within the latest CME week, offering a snapshot of recent market extremities.
Settings & Customisation
ATR-Based Measurement : Choose to measure gap sizes directly or in terms of ATR for a volatility-adjusted view.
Band Period Adjustability : Tailor the oscillator's sensitivity by modifying the band calculation period.
Dynamic Level Multipliers : Adjust the multiplier for dynamic levels to suit your analysis needs.
Visual Preferences : Customise the oscillator, bands, and table visuals, including color schemes and line styles.
In the example below, it demonstrates that the CME will want to return to the 0 value, this would be considered a reset or gap fill.
Application & Strategy
Deploy the CME Oscillator to enhance your market analysis
Market Sentiment : Gauge weekend market sentiment shifts through gap analysis, refining your strategy for the week ahead.
Volatility Insights : Use the oscillator's ATR-based measurements to understand the volatility context of gaps, aiding in risk management.
Trend Identification : Identify potential trend continuations or reversals based on the frequency and magnitude of gaps exceeding dynamic levels.
The CME Oscillator stands out as a strategic tool for traders focusing on gap analysis and volatility assessment. By offering a detailed breakdown of market gaps in relation to volatility, it empowers users with actionable insights, enabling more informed trading decisions across a range of markets and timeframes.
Composite Trend Oscillator [ChartPrime]CODE DUELLO:
Have you ever stopped to wonder what the underlying filters contained within complex algorithms are actually providing for you? Wouldn't it be nice to actually visually inspect for that? Those would require some kind of wild west styled quick draw duel or some comparison method as a proper 'code duello'. Then it can be determined which filter can 'draw' the quickest from it's computational holster with the least amount of lag and smoothness.
In Pine we can do so, discovering how beneficial that would be. This can be accomplished by quickly switching from one filter to another by input() back and forth, requiring visual memory. A better way could be done by placing two indicators added to the chart and then eventually placed into one indicator pane on top of each other.
By adding a filter() helper function that calls other moving average functions chosen for comparison, it can put to the test which moving average is the best drawing filter suited to our expected needs. PhiSmoother was formerly debuted and now it is utilized in a more complex environment in a multitude of ways along side other commonly utilized filters. Now, you the reader, get to judge for yourself...
FILTER VERSATILITY:
Having the capability to adjust between various smoothing methods such as PhiSmoother, TEMA, DEMA, WMA, EMA, and SMA on historical market data within the code provides an advantage. Each of these filter methods offers distinct advantages and hinderances. PhiSmoother stands out often by having superb noise rejection, while also being able to manipulate the fine-tuning of the phase or lag of the indicator, enhancing responsiveness to price movements.
The following are more well-known classic filters. TEMA (Triple Exponential Moving Average) and DEMA (Double Exponential Moving Average) offer reduced transient response times to price changes fluctuations. WMA (Weighted Moving Average) assigns more weight to recent data points, making it particularly useful for reduced lag. EMA (Exponential Moving Average) strikes a balance between responsiveness and computational efficiency, making it a popular choice. SMA (Simple Moving Average) provides a straightforward calculation based on the arithmetic mean of the data. VWMA and RMA have both been excluded for varying reasons, both being unworthy of having explanation here.
By allowing for adjustment refinements between these filter methods, traders may garner the flexibility to adapt their analysis to different market dynamics, optimizing their algorithms for improved decision-making and performance on demand.
INDICATOR INTRODUCTION:
ChartPrime's Composite Trend Oscillator operates as an oscillator based on the concept of a moving average ribbon. It utilizes up to 32 filters with progressively longer periods to assess trend direction and strength. Embedded within this indicator is an alternative view that utilizes the separation of the ribbon filaments to assess volatility. Both versions are excellent candidates for trend and momentum, both offering visualization of polarity, directional coloring, and filter crossings. Anyone who has former experience using RSI or stochastics may have ease of understanding applying this to their chart.
COMPOSITE CLUSTER MODES EXPLAINED:
In Trend Strength mode, the oscillator behavior signifies market direction and movement strength. When the oscillator is rising and above zero, the market is within a bullish phase, and visa versa. If the signal filter crosses the composite trend, this indicates a potential dynamic shift signaling a possible reversal. When the oscillator is teetering on its extremities, the market is more inclined to reverse later.
With Volatility mode, the oscillator undergoes a transformation, displaying an unbounded oscillator driven by market volatility. While it still employs the same scoring mechanism, it is now scaled according to the strength of the market move. This can aid with identification of ranging scenarios. However, one side effect is that the oscillator no longer has minimum or maximum boundaries. This can still be advantageous when considering divergences.
NOTEWORTHY SETTINGS FEATURES:
The following input settings described offer comprehensive control over the indicator's behavior and visualization.
Common Controls:
Price Source Selection - The indicator offers flexibility in choosing the price source for analysis. Traders can select from multiple options.
Composite Cluster Mode - Choose between "Trend Strength" and "Volatility" modes, providing insights into trend directionality or volatility weighting.
Cluster Filter and Length - Selects a filter for the cluster composition. This includes a length parameter adjustment.
Cluster Options:
Cluster Dispersion - Users can adjust the separation between moving averages in the cluster, influencing the sensitivity of the analysis.
Cluster Trimming - By modifying upper and lower trim parameters, traders can adjust the sensitivity of the moving averages within the cluster, enhancing its adaptability.
PostSmooth Filter and Length - Choose a filter to refine the composite cluster's post-smoothing with a length parameter adjustment.
Signal Filter and Length - Users can select a filter for the lagging signal plot, also having a length parameter adjustment.
Transition Easing - Sensitivity adjustment to influence the transition between bullish and bearish colors.
Enjoy
CBO (Candle Bias Oscillator)The Candle Bias Oscillator (CBO) with volume and ATR scaling is a unique technical analysis tool designed to capture market sentiment through the analysis of candlestick patterns, volume momentum, and market volatility. This indicator is built on the foundation of assessing the bias within a candlestick's body and wicks, adjusted for market volatility using the Average True Range (ATR), and further refined by comparing the Rate of Change (ROC) in volume and the adjusted bias. The culmination of these calculations results in the CBO, a smoothed oscillator that highlights potential market turning points through divergence analysis.
Key Features:
Bias Calculations: Utilizes the relationship between the candle's body and wicks to determine the market's immediate bias, offering a nuanced view beyond simple price action. Have you ever wanted to quantify exactly how bullish or bearish a particular candle or candlestick pattern is? Whether it's dojis, hammers, engulfing, gravestones, evening morning star, three soldiers etc. you don't have to memorize 50 candlestick patterns anymore.
Volatility Adjustment: Employs the ATR to adjust the bias calculation, ensuring the oscillator remains relevant across varying market conditions by accounting for volatility.
Momentum and Divergence: Measures the momentum in volume and bias through ROC calculations, identifying divergence that may signal reversals or significant price movements.
Signal Line: A smoothed version of the CBO, derived from its own values, serving as a benchmark for identifying potential crossovers and divergences.
Utility and Application:
The CBO with Divergence Scaling is developed for traders who seek a deeper understanding of market dynamics beyond price movements alone. It is particularly useful for identifying potential reversals or continuation patterns early, by highlighting divergence between market sentiment (as expressed through candlestick bias) and actual volume movements. In this way, it aligns us retail traders with institutional traders and smart money. This indicator is versatile and can be applied across various time frames and market instruments, offering value to both short-term traders and long-term investors.
How to Use:
Trend Identification: The direction and value of the CBO provide insights into the prevailing market trend. A positive oscillator value may indicate bullish sentiment, while a negative value suggests bearish sentiment.
Signal Line Crossovers: Crossovers between the CBO and its signal line can be used as potential buy or sell signals. A crossover above the signal line might indicate a buying opportunity, whereas a crossover below could suggest a selling point.
Divergence: Discrepancies between the CBO and price action (especially when confirmed by volume ROC) can highlight potential reversals.
Customization and Parameters: This script allows users to adjust several parameters, including oscillator periods, signal line periods, ATR periods, and ROC periods for divergence, to best fit their trading strategy and the characteristics of the market they are analyzing.
Conclusion:
The Custom Bias Oscillator with Divergence Scaling is a comprehensive tool designed to offer traders a multi-faceted view of market conditions, combining elements of price action, volatility, and momentum. By integrating these aspects into a single indicator, it aims to provide a more rounded and actionable insight into market trends and potential turning points.
To comply with best practices and ensure clarity regarding the informational nature of the Custom Bias Oscillator (CBO) tool, it's crucial to include a disclaimer about the non-advisory nature of the script. Here's a suitable disclaimer that you can add to the end of your script description or publication:
Disclaimer:
The Custom Bias Oscillator (CBO) with Divergence Scaling and its accompanying analysis are provided as tools for educational and informational purposes only and should not be construed as financial advice. The creator of this indicator does not guarantee any specific outcomes or profit, and all users should be aware of the risks involved in trading and investing. Users should conduct their own research and consult with a professional financial advisor before making any investment decisions. The use of this indicator is at the user's own risk, and the creator bears no responsibility for any direct or consequential loss arising from any use of this tool or the information provided herein.
Pseudo-Entropy Oscillator with Standard Deviation (modified)Intuition: The Pseudo-Entropy Oscillator with Standard Deviation (PEO_SD) was created to provide traders with a way to analyze market momentum and potential reversals. It combines the concepts of entropy, standard deviation, and moving averages to offer insights into market behavior.The oscillator's core idea is to measure the pseudo-entropy of the market using standard deviation. Pseudo-entropy refers to the degree of disorder or randomness in the price data. By calculating the standard deviation of the closing prices over a specified period, the oscillator quantifies the market's volatility.To enhance the usefulness of the pseudo-entropy measurement, the oscillator incorporates moving averages. The entropy delta is calculated by applying momentum analysis to the pseudo-entropy values. This helps identify short-term changes in the entropy, indicating shifts in market sentiment or momentum.The oscillator further smoothes the pseudo-entropy values by calculating the simple moving average (SMA) over a specified length. This helps filter out noise and provides a clearer representation of the market's overall momentum.
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
The "Pseudo-Entropy Oscillator with Standard Deviation" (PEO_SD) is a custom indicator designed to help traders analyze market momentum and potential reversal points. It can be applied to various markets like stocks, commodities, forex, or cryptocurrencies. By using this indicator, you can gain insights into the market's behavior and make more informed trading decisions.
The PEO_SD indicator plots three lines on your chart: the fast pseudo-entropy line, the medium pseudo-entropy line, and the slow pseudo-entropy line. Each line represents the combined pseudo-entropy values, which are calculated using standard deviation and moving averages.
The lines are color-coded for easy identification. The fast line is represented by blue, the medium line by yellow, and the slow line by red. Additionally, three horizontal reference lines are plotted: the mid line (at 50), the lower bound (at 20), and the upper bound (at 80).
To use this indicator effectively, you can observe the interactions of the lines with the reference lines. For example, when any of the lines cross above the mid line, it might indicate a bullish signal, suggesting an upward price movement. Conversely, a crossover below the mid line could be a bearish signal, indicating a potential downward price movement. If the lines reach the upper bound, it might suggest that the market is overbought, and a reversal could be imminent. Conversely, reaching the lower bound may indicate that the market is oversold, possibly leading to a price reversal.
By applying the PEO_SD indicator and studying the lines' movements, you can gain valuable insights into market momentum, identify potential reversal points, and make more informed trading decisions.
Normalized Elastic Volume Oscillator (MTF)The Multi-Timeframe Normalized Elastic Volume Oscillator combines volume analysis with multiple timeframe analysis. It provides traders with valuable insights into volume dynamics across different timeframes, helping to identify trends, potential reversals, and overbought/oversold conditions.
When using the Multi-Timeframe Normalized Elastic Volume Oscillator, consider the following guidelines:
Understanding Input Parameters : The indicator offers customizable input parameters to suit your trading preferences. You can adjust the EMA length (emaLength), scaling factor (scalingFactor), volume weighting option (volumeWeighting), and select a higher timeframe for analysis (higherTF). Experiment with these parameters to optimize the indicator for your trading strategy.
Multiple Timeframe Analysis : The Multi-Timeframe Normalized Elastic Volume Oscillator allows you to analyze volume dynamics on both the current timeframe and a higher timeframe. By comparing volume behavior across different timeframes, you gain a broader perspective on market trends and the strength of volume deviations. The higher timeframe analysis provides additional confirmation and helps identify more significant market shifts.
Normalized Values : The indicator normalizes the volume deviations on both timeframes to a consistent scale between -0.25 and 0.75. This normalization makes it easier to compare and interpret the oscillator's readings across different assets and timeframes. Positive values indicate bullish volume behavior, while negative values suggest bearish volume behavior.
Interpreting the Indicator : Pay attention to the position of the Multi-Timeframe Normalized Elastic Volume Oscillator lines relative to the zero line on both timeframes. Positive values on either timeframe indicate a bullish bias, while negative values suggest a bearish bias. The distance of the oscillator from the zero line reflects the strength of the volume deviation. Extreme readings, both positive and negative, may indicate overbought or oversold conditions, potentially signaling a trend reversal or exhaustion.
Combining with Other Indicators : For more robust trading decisions, consider combining the Multi-Timeframe Normalized Elastic Volume Oscillator with other technical analysis tools. This could include trend indicators, support/resistance levels, or candlestick patterns. By incorporating multiple indicators, you gain additional confirmation and increase the reliability of your trading signals.
Remember that the Multi-Timeframe Normalized Elastic Volume Oscillator is a valuable tool, but it should not be used in isolation. Consider other factors such as price action, market context, and fundamental analysis to make well-informed trading decisions. Additionally, practice proper risk management and exercise caution when executing trades.
By utilizing the Multi-Timeframe Normalized Elastic Volume Oscillator, you gain a comprehensive view of volume dynamics across different timeframes. This knowledge can help you identify potential market trends, confirm trading signals, and improve the timing of your trades.
Take time to familiarize yourself with the indicator and conduct thorough testing on historical data. This will help you gain confidence in its effectiveness and align it with your trading strategy. With experience and continuous evaluation, you can harness the power of the Multi-Timeframe Normalized Elastic Volume Oscillator to make informed trading decisions.
Kase Peak Oscillator w/ Divergences [Loxx]Kase Peak Oscillator is unique among first derivative or "rate-of-change" indicators in that it statistically evaluates over fifty trend lengths and automatically adapts to both cycle length and volatility. In addition, it replaces the crude linear mathematics of old with logarithmic and exponential models that better reflect the true nature of the market. Kase Peak Oscillator is unique in that it can be applied across multiple time frames and different commodities.
As a hybrid indicator, the Peak Oscillator also generates a trend signal via the crossing of the histogram through the zero line. In addition, the red/green histogram line indicates when the oscillator has reached an extreme condition. When the oscillator reaches this peak and then turns, it means that most of the time the market will turn either at the present extreme, or (more likely) at the following extreme.
This is both a reversal and breakout/breakdown indicator. Crosses above/below zero line can be used for breakouts/breakdowns, while the thick green/red bars can be used to detect reversals
The indicator consists of three indicators:
The PeakOscillator itself is rendered as a gray histogram.
Max is a red/green solid line within the histogram signifying a market extreme.
Yellow line is max peak value of two (by default, you can change this with the deviations input settings) standard deviations of the Peak Oscillator value
White line is the min peak value of two (by default, you can change this with the deviations input settings) standard deviations of the PeakOscillator value
The PeakOscillator is used two ways:
Divergence: Kase Peak Oscillator may be used to generate traditional divergence signals. The difference between it and traditional divergence indicators lies in its accuracy.
PeakOut: The second use is to look for a Peak Out. A Peak Out occurs when the histogram breaks beyond the PeakOut line and then pulls back. A Peak Out through the maximum line will be displayed magenta. A Peak Out, which only extends through the Peak Min line is called a local Peak Out, and is less significant than a normal Peak Out signal. These local Peak Outs are to be relied upon more heavily during sideways or corrective markets. Peak Outs may be based on either the maximum line or the minimum line. Maximum Peak Outs, however, are rarer and thus more significant than minimum Peak Outs. The magnitude of the price move may be greater following the maximum Peak Out, but the likelihood of the break in trend is essentially the same. Thus, our research indicates that we should react equally to a Peak Out in a trendy market and a Peak Min in a choppy or corrective market.
Included:
Bar coloring
Alerts
Combo Backtest 123 Reversal & Rainbow Oscillator This is combo strategies for get a cumulative signal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
Ever since the people concluded that stock market price movements are not
random or chaotic, but follow specific trends that can be forecasted, they
tried to develop different tools or procedures that could help them identify
those trends. And one of those financial indicators is the Rainbow Oscillator
Indicator. The Rainbow Oscillator Indicator is relatively new, originally
introduced in 1997, and it is used to forecast the changes of trend direction.
As market prices go up and down, the oscillator appears as a direction of the
trend, but also as the safety of the market and the depth of that trend. As
the rainbow grows in width, the current trend gives signs of continuity, and
if the value of the oscillator goes beyond 80, the market becomes more and more
unstable, being prone to a sudden reversal. When prices move towards the rainbow
and the oscillator becomes more and more flat, the market tends to remain more
stable and the bandwidth decreases. Still, if the oscillator value goes below 20,
the market is again, prone to sudden reversals. The safest bandwidth value where
the market is stable is between 20 and 80, in the Rainbow Oscillator indicator value.
The depth a certain price has on a chart and into the rainbow can be used to judge
the strength of the move.
WARNING:
- For purpose educate only
- This script to change bars colors.
[blackcat] L2 Ehlers Recursive Median OscillatorLevel: 2
Background
John F. Ehlers introuced Recursive Median Oscillator in Mar, 2018.
Function
In “Recursive Median Filters” in Mar, 2018, John Ehlers presented an approach for filtering out extreme price and volume data that could throw off typical averaging calculations. Dr. Ehlers’ line in digital signal processing extends to removing extreme spikes in financial data, but utilizing the median average value in the recursive filter calculations. By removing these extremities, the actual extremities that occur in the underlying data may be better determined. Ehlers goes on to present a novel oscillator using this technique, comparing its response to the well-known RSI. He notes that by being able to smooth the data with the least amount of lag, the recursive median oscillator may give the trader a better view of the bigger picture.
The recursive median filter (RMF) ignores the spiking-types of the price noise. RMF uses an exponential moving average of the five-period median of the source data to produce a smoothing of the signal while avoiding spikes. The recursive median oscillator (RMO) is an oscillator built along the same principles. The RMO has less lag and a faster response to the larger moves in the price data.
Key Signal
RMO --> Ehlers Recursive Median Oscillator fast line
RMO --> Ehlers Recursive Median Oscillator slow line
Pros and Cons
100% John F. Ehlers definition translation, even variable names are the same. This help readers who would like to use pine to read his book.
Remarks
The 89th script for Blackcat1402 John F. Ehlers Week publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
Dynamic Momentum Oscillator (Dynamo)Hi,
In July 1996 Futures magazine, E. Marshall Wall introduces the
Dynamic Momentum Oscillator (Dynamo). Please refer to this article
for interpretation.
The Dynamo oscillator is a normalizing function which adjusts the
values of a standard oscillator for trendiness by taking the difference
between the value of the oscillator and a moving average of the oscillator
and then subtracting that value from the oscillator midpoint.
Enhanced Std Dev Oscillator (Z-Score)Enhanced Std Dev Oscillator (Z-Score)
Overview
The Enhanced Std Dev Oscillator (ESDO) is a refined Z-Score indicator that normalizes price deviations from a moving mean using standard deviation, smoothed for clarity and equipped with divergence detection. This oscillator shines in identifying extreme overbought/oversold conditions and potential reversals, making it ideal for mean-reversion strategies in stocks, forex, or crypto. By highlighting when prices stray too far from the norm, it helps traders avoid chasing trends and focus on high-probability pullbacks.
Key Features
Customisable Mean & Deviation: Choose SMA or EMA for the mean (default: SMA, length 14); opt for Population or Sample standard deviation for precise statistical accuracy.
Smoothing for Clarity: Apply a simple moving average (default: 3) to the raw Z-Score, reducing noise without lagging signals excessively.
Zone Highlighting: Background colours flag extreme zones—red tint above +2 (overbought), green below -2 (oversold)—for quick visual scans.
Divergence Alerts: Automatically detects bullish (price lows lower, Z-Score higher) and bearish (price highs higher, Z-Score lower) divergences using pivot points (default length: 5), with labeled shapes for easy spotting.
Built-in Alerts: Notifications for Z-Score crossovers into OB/OS zones and divergence events to keep you informed without constant monitoring.
How It Works
Core Calculation: Computes the mean (SMA/EMA) over the specified length, then standard deviation (Population or adjusted Sample formula for N>1). Z-Score = (Source - Mean) / Std Dev, handling edge cases like zero deviation.
Smoothing: Averages the Z-Score with an SMA to create a cleaner plot oscillating around zero.
Levels & Zones: Plots horizontal lines at ±1 (orange dotted) and ±2 (red dashed) for reference; backgrounds activate in extreme zones.
Divergence Logic: Scans for pivot highs/lows in price and Z-Score; flags divergences when price extremes diverge from oscillator extremes (looking back 2 pivots for confirmation).
Visualisation: Blue line for the smoothed Z-Score; green/red labels for bull/bear divergences.
Usage Tips
Buy Signal: Z-Score crosses below -2 (oversold) or bullish divergence forms—pair with volume spike for confirmation.
Sell Signal: Z-Score crosses above +2 (overbought) or bearish divergence—watch for resistance alignment.
Customisation: Use EMA mean for trendier assets; enable Sample std dev for smaller datasets. Increase pivot length (7-10) in volatile markets to filter false signals.
Timeframes: Excels on daily/4H for swing trades; test smoothing on lower frames to avoid over-smoothing. Always combine with trend filters like a 200-period MA.
This open-source script is licensed under Mozilla Public License 2.0. Backtest thoroughly—past performance isn't indicative of future results. Trade with discipline! 📈
© HighlanderOne
Premier Stochastic Oscillator [LazyBear, V2]This script builds on the well-known Premier Stochastic Oscillator (PSO) originally introduced by LazyBear, and adds a Z-Score extension to provide statistical interpretation of momentum extremes.
Features
Premier Stochastic Core: A smoothed stochastic calculation that highlights bullish and bearish momentum phases.
Z-Score Mapping: The PSO values are standardized into Z-Scores (from –3 to +3), quantifying the degree of momentum stretch.
Positive / Negative Z-Scores:
Positive Z values suggest momentum strength that can align with accumulation or favorable buying conditions.
Negative Z values indicate stronger bearish pressure, often aligning with selling or distribution conditions.
On-Chart Label: The current Z-Score is displayed on the latest bar for quick reference.
How to Use
Momentum Confirmation: Use the oscillator to confirm whether bullish or bearish momentum is intensifying.
Overextended Conditions: Extreme Z-Scores (±2 or beyond) highlight statistically stretched conditions, often preceding reversions.
Strategic Integration: Best applied in confluence with trend tools or higher-timeframe filters; not a standalone trading signal.
Originality
Unlike the standard PSO, this version:
Adds a Z-Score framework for objective statistical scaling.
Provides real-time labeling of Z values for clarity.
Extends the classic oscillator into a tool for both momentum detection and mean-reversion context.
Hurst Momentum Oscillator | AlphaNattHurst Momentum Oscillator | AlphaNatt
An adaptive oscillator that combines the Hurst Exponent - which identifies whether markets are trending or mean-reverting - with momentum analysis to create signals that automatically adjust to market regime.
"The Hurst Exponent reveals a hidden truth: markets aren't always trending. This oscillator knows when to ride momentum and when to fade it."
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📐 THE MATHEMATICS
Hurst Exponent (H):
Measures the long-term memory of time series:
H > 0.5: Trending (persistent) behavior
H = 0.5: Random walk
H < 0.5: Mean-reverting behavior
Originally developed for analyzing Nile river flooding patterns, now used in:
Fractal market analysis
Network traffic prediction
Climate modeling
Financial markets
The Innovation:
This oscillator multiplies momentum by the Hurst coefficient:
When trending (H > 0.5): Momentum is amplified
When mean-reverting (H < 0.5): Momentum is reduced
Result: Adaptive signals based on market regime
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
💎 KEY ADVANTAGES
Regime Adaptive: Automatically adjusts to trending vs ranging markets
False Signal Reduction: Reduces momentum signals in mean-reverting markets
Trend Amplification: Stronger signals when trends are persistent
Mathematical Edge: Based on fractal dimension analysis
No Repainting: All calculations on historical data
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📊 TRADING SIGNALS
Visual Interpretation:
Cyan zones: Bullish momentum in trending market
Magenta zones: Bearish momentum or mean reversion
Background tint: Blue = trending, Pink = mean-reverting
Gradient intensity: Signal strength
Trading Strategies:
1. Trend Following:
Trade momentum signals when background is blue (trending)
2. Mean Reversion:
Fade extreme readings when background is pink
3. Regime Transition:
Watch for background color changes as early warning
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🎯 OPTIMAL USAGE
Best Conditions:
Strong trending markets (crypto bull runs)
Clear ranging markets (forex sessions)
Regime transitions
Multi-timeframe analysis
Market Applications:
Crypto: Excellent for identifying trend persistence
Forex: Detects when pairs are ranging
Stocks: Identifies momentum stocks
Commodities: Catches persistent trends
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Developed by AlphaNatt | Fractal Market Analysis
Version: 1.0
Classification: Adaptive Regime Oscillator
Not financial advice. Always DYOR.
SMC - Institutional Confidence Oscillator [PhenLabs]📊 Institutional Confidence Oscillator
Version: PineScript™v6
📌 Description
The Institutional Confidence Oscillator (ICO) revolutionizes market analysis by automatically detecting and evaluating institutional activity at key support and resistance levels using our own in-house detection system. This sophisticated indicator combines volume analysis, volatility measurements, and mathematical confidence algorithms to provide real-time readings of institutional sentiment and zone strength.
Using our advanced thin liquidity detection, the ICO identifies high-volume, narrow-range bars that signal institutional zone formation, then tracks how these zones perform under market pressure. The result is a dual-wave confidence oscillator that shows traders when institutions are actively defending price levels versus when they’re abandoning positions.
The indicator transforms complex institutional behavior patterns into clear, actionable confidence percentiles, helping traders align with smart money movements and avoid common retail trading pitfalls.
🚀 Points of Innovation
Automated thin liquidity zone detection using volume threshold multipliers and zone size filtering
Dual-sided confidence tracking for both support and resistance levels simultaneously
Sigmoid function processing for enhanced mathematical accuracy in confidence calculations
Real-time institutional defense pattern analysis through complete test cycles
Advanced visual smoothing options with multiple algorithmic methods (EMA, SMA, WMA, ALMA)
Integrated momentum indicators and gradient visualization for enhanced signal clarity
🔧 Core Components
Volume Threshold System: Analyzes volume ratios against baseline averages to identify institutional activity spikes
Zone Detection Algorithm: Automatically identifies thin liquidity zones based on customizable volume and size parameters
Confidence Lifecycle Engine: Tracks institutional defense patterns through complete observation windows
Mathematical Processing Core: Uses sigmoid functions to convert raw market data into normalized confidence percentiles
Visual Enhancement Suite: Provides multiple smoothing methods and customizable display options for optimal chart interpretation
🔥 Key Features
Auto-Detection Technology: Automatically scans for institutional zones without manual intervention, saving analysis time
Dual Confidence Tracking: Simultaneously monitors both support and resistance institutional activity for comprehensive market view
Smart Zone Validation: Evaluates zone strength through volume analysis, adverse excursion measurement, and defense success rates
Customizable Parameters: Extensive input options for volume thresholds, observation windows, and visual preferences
Real-Time Updates: Continuously processes market data to provide current institutional confidence readings
Enhanced Visualization: Features gradient fills, momentum indicators, and information panels for clear signal interpretation
🎨 Visualization
Dual Oscillator Lines: Support confidence (cyan) and resistance confidence (red) plotted as percentage values 0-100%
Gradient Fill Areas: Color-coded regions showing confidence dominance and strength levels
Reference Grid Lines: Horizontal markers at 25%, 50%, and 75% levels for easy interpretation
Information Panel: Real-time display of current confidence percentiles with color-coded dominance indicators
Momentum Indicators: Rate of change visualization for confidence trends
Background Highlights: Extreme confidence level alerts when readings exceed 80%
📖 Usage Guidelines
Auto-Detection Settings
Use Auto-Detection
Default: true
Description: Enables automatic thin liquidity zone identification based on volume and size criteria
Volume Threshold Multiplier
Default: 6.0, Range: 1.0+
Description: Controls sensitivity of volume spike detection for zone identification, higher values require more significant volume increases
Volume MA Length
Default: 15, Range: 1+
Description: Period for volume moving average baseline calculation, affects volume spike sensitivity
Max Zone Height %
Default: 0.5%, Range: 0.05%+
Description: Filters out wide price bars, keeping only thin liquidity zones as percentage of current price
Confidence Logic Settings
Test Observation Window
Default: 20 bars, Range: 2+
Description: Number of bars to monitor zone tests for confidence calculation, longer windows provide more stable readings
Clean Break Threshold
Default: 1.5 ATR, Range: 0.1+
Description: ATR multiple required for zone invalidation, higher values make zones more persistent
Visual Settings
Smoothing Method
Default: EMA, Options: SMA/EMA/WMA/ALMA
Description: Algorithm for signal smoothing, EMA responds faster while SMA provides more stability
Smoothing Length
Default: 5, Range: 1-50
Description: Period for smoothing calculation, higher values create smoother lines with more lag
✅ Best Use Cases
Trending market analysis where institutional zones provide reliable support/resistance levels
Breakout confirmation by validating zone strength before position entry
Divergence analysis when confidence shifts between support and resistance levels
Risk management through identification of high-confidence institutional backing
Market structure analysis for understanding institutional sentiment changes
⚠️ Limitations
Performs best in liquid markets with clear institutional participation
May produce false signals during low-volume or holiday trading periods
Requires sufficient price history for accurate confidence calculations
Confidence readings can fluctuate rapidly during high-impact news events
Manual fallback zones may not reflect actual institutional activity
💡 What Makes This Unique
Automated Detection: First Pine Script indicator to automatically identify thin liquidity zones using sophisticated volume analysis
Dual-Sided Analysis: Simultaneously tracks institutional confidence for both support and resistance levels
Mathematical Precision: Uses sigmoid functions for enhanced accuracy in confidence percentage calculations
Real-Time Processing: Continuously evaluates institutional defense patterns as market conditions change
Visual Innovation: Advanced smoothing options and gradient visualization for superior chart clarity
🔬 How It Works
1. Zone Identification Process:
Scans for high-volume bars that exceed the volume threshold multiplier
Filters bars by maximum zone height percentage to identify thin liquidity conditions
Stores qualified zones with proximity threshold filtering for relevance
2. Confidence Calculation Process:
Monitors price interaction with identified zones during observation windows
Measures volume ratios and adverse excursions during zone tests
Applies sigmoid function processing to normalize raw data into confidence percentiles
3. Real-Time Analysis Process:
Continuously updates confidence readings as new market data becomes available
Tracks institutional defense success rates and zone validation patterns
Provides visual and numerical feedback through the oscillator display
💡 Note:
The ICO works best when combined with traditional technical analysis and proper risk management. Higher confidence readings indicate stronger institutional backing but should be confirmed with price action and volume analysis. Consider using multiple timeframes for comprehensive market structure understanding.
