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[Pandora] Vast Volatility Treasure TroveINTRODUCTION: Volatility enthusiasts, prepare for VICTORY on this day of July 4th, 2024! This is my "Vast Volatility Treasure Trove," intended mostly for educational purposes, yet these functions will also exhibit versatility when combined with other algorithms to garner statistical excellence. Once again, I am now ripping the lid off of Pandora's box... of volatility. Inside this script is a 'vast' collection of volatility estimators, reflecting the indicators name. Whether you are a seasoned trader destined to navigate financial strife or an eagerly curious learner, this script offers a comprehensive toolkit for a broad spectrum of volatility analysis. Enjoy your journey through the realm of market volatility with this code! WHAT IS MARKET VOLATILITY?: Market volatility refers to various fluctuations in the value of a financial market or asset over a period of time, often characterized by occasional rapid and significant deviations in price. During periods of greater market volatility, evolving conditions of prices can move rapidly in either direction, creating uncertainty for investors with results of sharp declines as well as rapid gains. However, market volatility is a typical aspect expected in financial markets that can also present opportunities for informed decision-making and potential benefits from the price flux. SCRIPT INTENTION: Volatility is assuredly omnipresent, waxing and waning in magnitude, and some readers have every intention of studying and/or measuring it. This script serves as an all-in-one armada of volatility estimators for TradingView members. I set out to provide a diverse set of tools to analyze and interpret market volatility, offering volatile insights, and aid with the development of robust trading indicators and strategies. In today's fast-paced financial markets, understanding and quantifying volatility is informative for both seasoned traders and novice investors. This script is designed to empower users by equipping them with a comprehensive suite of volatility estimators. Each function within this script has been meticulously crafted to address various aspects of volatility, from traditional methods like Garman-Klass and Parkinson to more advanced techniques like Yang-Zhang and my custom experimental algorithms. Ultimately, this script is more than just a collection of functions. It is a gateway to a deeper understanding of market volatility and a valuable resource for anyone committed to mastering the complexities of financial markets. SCRIPT CONTENTS: This script includes a variety of functions designed to measure and analyze market volatility. Where applicable, an input checkbox option provides an unbiased/biased estimate. Below is a brief description of each function in the original order they appear as code upon first publish: Parkinson Volatility - Estimates volatility emphasizing the high and low range movements. Alternate Parkinson Volatility - Simpler version of the original Parkinson Volatility that I realized. Garman-Klass Volatility - Estimates volatility based on high, low, open, and close prices using a formula that adjusts for biases in price dynamics. Rogers-Satchell-Yoon Volatility #1 - Estimates volatility based on logarithmic differences between high, low, open, and close values. Rogers-Satchell-Yoon Volatility #2 - Similar estimate to Rogers-Satchell with the same result via an alternate formulation of volatility. Yang-Zhang Volatility - An advanced volatility estimate combining both strengths of the Garman-Klass and Rogers-Satchell estimators, with weights determined by an alpha parameter. Yang-Zhang (Modified) Volatility - My experimental modification slightly different from the Yang-Zhang formula with improved computational efficiency. Selectable Volatility - Basic customizable volatility calculation based on the logarithmic difference between selected numerator and denominator prices (e.g., open, high, low, close). Close-to-Close Volatility - Estimates volatility using the logarithmic difference between consecutive closing prices. Specifically applicable to data sources without open, high, and low prices. Open-to-Close Volatility - (Overnight Volatility): Estimates volatility based on the logarithmic difference between the opening price and the last closing price emphasizing overnight gaps. Hilo Volatility - Estimates volatility using a method similar to Parkinson's method, which considers the logarithm of the high and low prices. Vantage Volatility - My experimental custom 'vantage' method to estimate volatility similar to Yang-Zhang, which incorporates various factors (Alpha, Beta, Gamma) to generate a weighted logarithmic calculation. This may be a volatility advantage or disadvantage, hence it's name. Schwert Volatility - Estimates volatility based on arithmetic returns. Historical Volatility - Estimates volatility considering logarithmic returns. Annualized Historical Volatility - Estimates annualized volatility using logarithmic returns, adjusted for the number of trading days in a year. If I omitted any other known varieties, detailed requests for future consideration can be made below for their inclusion into this script within future versions... BONUS ALGORITHMS: This script also includes several experimental and bonus functions that push the boundaries of volatility analysis as I understand it. These functions are designed to provide additional insights and also are my ideal notions for traders looking to explore other methods of volatility measurement. VOLATILITY APPLICATIONS: Volatility estimators serve a common role across various facets of trading and financial analysis, offering insights into market behavior. These tools are already in instrumental with enhancing risk management practices by providing a deeper understanding of market dynamics and the inherent uncertainty in asset prices. With volatility estimators, traders can effectively quantifying market risk and adjust their strategies accordingly, optimizing portfolio performance and mitigating potential losses. Additionally, volatility estimations may serve as indication for detecting overbought or oversold market conditions, offering probabilistic insights that could inform strategic decisions at turning points. This script distinctly offers a variety of volatility estimators to navigate intricate financial terrains with informed judgment to address challenges of strategic planning. CODE REUSE: You don't have to ask for my permission to use/reuse these functions in your published scripts, simply because I have better things to do than answer requests for the reuse of these functions. Notice: Unfortunately, I will not provide any integration support into member's projects at all. I have my own projects that require way too much of my day already.
Indicatore Pine Script®
di ImmortalFreedom
13
Endpointed SSA of Price [Loxx]The Endpointed SSA of Price: A Comprehensive Tool for Market Analysis and Decision-Making The financial markets present sophisticated challenges for traders and investors as they navigate the complexities of market behavior. To effectively interpret and capitalize on these complexities, it is crucial to employ powerful analytical tools that can reveal hidden patterns and trends. One such tool is the Endpointed SSA of Price, which combines the strengths of Caterpillar Singular Spectrum Analysis, a sophisticated time series decomposition method, with insights from the fields of economics, artificial intelligence, and machine learning. The Endpointed SSA of Price has its roots in the interdisciplinary fusion of mathematical techniques, economic understanding, and advancements in artificial intelligence. This unique combination allows for a versatile and reliable tool that can aid traders and investors in making informed decisions based on comprehensive market analysis. The Endpointed SSA of Price is not only valuable for experienced traders but also serves as a useful resource for those new to the financial markets. By providing a deeper understanding of market forces, this innovative indicator equips users with the knowledge and confidence to better assess risks and opportunities in their financial pursuits. █ Exploring Caterpillar SSA: Applications in AI, Machine Learning, and Finance Caterpillar SSA (Singular Spectrum Analysis) is a non-parametric method for time series analysis and signal processing. It is based on a combination of principles from classical time series analysis, multivariate statistics, and the theory of random processes. The method was initially developed in the early 1990s by a group of Russian mathematicians, including Golyandina, Nekrutkin, and Zhigljavsky. Background Information: SSA is an advanced technique for decomposing time series data into a sum of interpretable components, such as trend, seasonality, and noise. This decomposition allows for a better understanding of the underlying structure of the data and facilitates forecasting, smoothing, and anomaly detection. Caterpillar SSA is a particular implementation of SSA that has proven to be computationally efficient and effective for handling large datasets. Uses in AI and Machine Learning: In recent years, Caterpillar SSA has found applications in various fields of artificial intelligence (AI) and machine learning. Some of these applications include: 1. Feature extraction: Caterpillar SSA can be used to extract meaningful features from time series data, which can then serve as inputs for machine learning models. These features can help improve the performance of various models, such as regression, classification, and clustering algorithms. 2. Dimensionality reduction: Caterpillar SSA can be employed as a dimensionality reduction technique, similar to Principal Component Analysis (PCA). It helps identify the most significant components of a high-dimensional dataset, reducing the computational complexity and mitigating the "curse of dimensionality" in machine learning tasks. 3. Anomaly detection: The decomposition of a time series into interpretable components through Caterpillar SSA can help in identifying unusual patterns or outliers in the data. Machine learning models trained on these decomposed components can detect anomalies more effectively, as the noise component is separated from the signal. 4. Forecasting: Caterpillar SSA has been used in combination with machine learning techniques, such as neural networks, to improve forecasting accuracy. By decomposing a time series into its underlying components, machine learning models can better capture the trends and seasonality in the data, resulting in more accurate predictions. Application in Financial Markets and Economics: Caterpillar SSA has been employed in various domains within financial markets and economics. Some notable applications include: 1. Stock price analysis: Caterpillar SSA can be used to analyze and forecast stock prices by decomposing them into trend, seasonal, and noise components. This decomposition can help traders and investors better understand market dynamics, detect potential turning points, and make more informed decisions. 2. Economic indicators: Caterpillar SSA has been used to analyze and forecast economic indicators, such as GDP, inflation, and unemployment rates. By decomposing these time series, researchers can better understand the underlying factors driving economic fluctuations and develop more accurate forecasting models. 3. Portfolio optimization: By applying Caterpillar SSA to financial time series data, portfolio managers can better understand the relationships between different assets and make more informed decisions regarding asset allocation and risk management. Application in the Indicator: In the given indicator, Caterpillar SSA is applied to a financial time series (price data) to smooth the series and detect significant trends or turning points. The method is used to decompose the price data into a set number of components, which are then combined to generate a smoothed signal. This signal can help traders and investors identify potential entry and exit points for their trades. The indicator applies the Caterpillar SSA method by first constructing the trajectory matrix using the price data, then computing the singular value decomposition (SVD) of the matrix, and finally reconstructing the time series using a selected number of components. The reconstructed series serves as a smoothed version of the original price data, highlighting significant trends and turning points. The indicator can be customized by adjusting the lag, number of computations, and number of components used in the reconstruction process. By fine-tuning these parameters, traders and investors can optimize the indicator to better match their specific trading style and risk tolerance. Caterpillar SSA is versatile and can be applied to various types of financial instruments, such as stocks, bonds, commodities, and currencies. It can also be combined with other technical analysis tools or indicators to create a comprehensive trading system. For example, a trader might use Caterpillar SSA to identify the primary trend in a market and then employ additional indicators, such as moving averages or RSI, to confirm the trend and generate trading signals. In summary, Caterpillar SSA is a powerful time series analysis technique that has found applications in AI and machine learning, as well as financial markets and economics. By decomposing a time series into interpretable components, Caterpillar SSA enables better understanding of the underlying structure of the data, facilitating forecasting, smoothing, and anomaly detection. In the context of financial trading, the technique is used to analyze price data, detect significant trends or turning points, and inform trading decisions. █ Input Parameters This indicator takes several inputs that affect its signal output. These inputs can be classified into three categories: Basic Settings, UI Options, and Computation Parameters. Source: This input represents the source of price data, which is typically the closing price of an asset. The user can select other price data, such as opening price, high price, or low price. The selected price data is then utilized in the Caterpillar SSA calculation process. Lag: The lag input determines the window size used for the time series decomposition. A higher lag value implies that the SSA algorithm will consider a longer range of historical data when extracting the underlying trend and components. This parameter is crucial, as it directly impacts the resulting smoothed series and the quality of extracted components. Number of Computations: This input, denoted as 'ncomp,' specifies the number of eigencomponents to be considered in the reconstruction of the time series. A smaller value results in a smoother output signal, while a higher value retains more details in the series, potentially capturing short-term fluctuations. SSA Period Normalization: This input is used to normalize the SSA period, which adjusts the significance of each eigencomponent to the overall signal. It helps in making the algorithm adaptive to different timeframes and market conditions. Number of Bars: This input specifies the number of bars to be processed by the algorithm. It controls the range of data used for calculations and directly affects the computation time and the output signal. Number of Bars to Render: This input sets the number of bars to be plotted on the chart. A higher value slows down the computation but provides a more comprehensive view of the indicator's performance over a longer period. This value controls how far back the indicator is rendered. Color bars: This boolean input determines whether the bars should be colored according to the signal's direction. If set to true, the bars are colored using the defined colors, which visually indicate the trend direction. Show signals: This boolean input controls the display of buy and sell signals on the chart. If set to true, the indicator plots shapes (triangles) to represent long and short trade signals. Static Computation Parameters: The indicator also includes several internal parameters that affect the Caterpillar SSA algorithm, such as Maxncomp, MaxLag, and MaxArrayLength. These parameters set the maximum allowed values for the number of computations, the lag, and the array length, ensuring that the calculations remain within reasonable limits and do not consume excessive computational resources. █ A Note on Endpionted, Non-repainting Indicators An endpointed indicator is one that does not recalculate or repaint its past values based on new incoming data. In other words, the indicator's previous signals remain the same even as new price data is added. This is an important feature because it ensures that the signals generated by the indicator are reliable and accurate, even after the fact. When an indicator is non-repainting or endpointed, it means that the trader can have confidence in the signals being generated, knowing that they will not change as new data comes in. This allows traders to make informed decisions based on historical signals, without the fear of the signals being invalidated in the future. In the case of the Endpointed SSA of Price, this non-repainting property is particularly valuable because it allows traders to identify trend changes and reversals with a high degree of accuracy, which can be used to inform trading decisions. This can be especially important in volatile markets where quick decisions need to be made.
Indicatore Pine Script®
di loxx
4
Bogdan Ciocoiu - LitigatorDescription The Litigator is an indicator that encapsulates the value delivered by the Relative Strength Index, Ultimate Oscillator, Stochastic and Money Flow Index algorithms to produce signals enabling users to enter positions in ideal market conditions. The Litigator integrates the value delivered by the above four algorithms into one script. This indicator is handy when trading continuation/reversal divergence strategies in conjunction with price action. Uniqueness The Litigator's uniqueness stands from integrating the above algorithms into the same visual area and leveraging preconfigured parameters suitable for short term scalping (1-5 minutes). In addition, the Litigator allows configuring the above four algorithms in such a way to coordinate signals by colour-coding or shape thickness to aid the user with identifying any emerging patterns quicker. Furthermore, Moonshot's uniqueness is also reflected in the way it has standardised the outputs of each algorithm to look and feel the same, and in doing so, enabling users to plug them in/out as needed. This also includes ensuring the ratios of the shapes are similar (applicable to the same scale). Open-source The indicator uses the following open-source scripts/algorithms: www.tradingview.com www.tradingview.com www.tradingview.com www.tradingview.com
Indicatore Pine Script®
di BogdanCiocoiu
1
Bogdan Ciocoiu - MoonshotDescription Moonshot is an indicator that encapsulates the value delivered by the TSI, MACD, Awesome Oscillator and CCI algorithms to produce signals to enable users to enter positions in ideal market conditions. Moonshot integrates the value delivered by the above four algorithms into one script. This indicator is particularly useful when trading continuation/reversal divergence strategies. Uniqueness The Moonshot's uniqueness stands from integrating the above algorithms into the same visual area and leveraging preconfigured parameters suitable for 1-3 minute scalping techniques. In addition, Moonshot allows swapping or furthermore configuring the above four algorithms in such a way to align signals by colour-coding or shape thickness to aid the users with identifying any emerging patterns quicker. Furthermore, Moonshot's uniqueness is also reflected in the way it has standardised the outputs of each algorithm to look and feel the same (including the scale at which the shapes are shown) and, in doing so, enables users to plug them in/out as needed. Open-source The indicator leverages the following open-source scripts/algorithms: www.tradingview.com www.tradingview.com www.tradingview.com www.tradingview.com
Indicatore Pine Script®
di BogdanCiocoiu
LengthAdaptationCollection of dynamic length adaptation algorithms. Mostly from various Adaptive Moving Averages (they are usually just EMA otherwise). Now you can combine Adaptations with any other Moving Averages or Oscillators (see my other libraries), to get something like Deviation Scaled RSI or Fractal Adaptive VWMA. This collection is not encyclopaedic. Suggestions are welcome. chande(src, len, sdlen, smooth, power) Chande's Dynamic Length   Parameters:      src : Series to use      len : Reference lookback length      sdlen : Lookback length of Standard deviation      smooth : Smoothing length of Standard deviation      power : Exponent of the length adaptation (lower is smaller variation)   Returns: Calculated period Taken from Chande's Dynamic Momentum Index (CDMI or DYMOI), which is dynamic RSI with this length Original default power value is 1, but I use 0.5 A variant of this algorithm is also included, where volume is used instead of price vidya(src, len, dynLow) Variable Index Dynamic Average Indicator (VIDYA)   Parameters:      src : Series to use      len : Reference lookback length      dynLow : Lower bound for the dynamic length   Returns: Calculated period Standard VIDYA algorithm. The period oscillates from the Lower Bound up (slow) I took the adaptation part, as it is just an EMA otherwise vidyaRS(src, len, dynHigh) Relative Strength Dynamic Length - VIDYA RS   Parameters:      src : Series to use      len : Reference lookback length      dynHigh : Upper bound for the dynamic length   Returns: Calculated period Based on Vitali Apirine's modification (Stocks and Commodities, January 2022) of VIDYA algorithm. The period oscillates from the Upper Bound down (fast) I took the adaptation part, as it is just an EMA otherwise kaufman(src, len, dynLow, dynHigh) Kaufman Efficiency Scaling   Parameters:      src : Series to use      len : Reference lookback length      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length   Returns: Calculated period Based on Efficiency Ratio calculation orifinally used in Kaufman Adaptive Moving Average developed by Perry J. Kaufman I took the adaptation part, as it is just an EMA otherwise ds(src, len) Deviation Scaling   Parameters:      src : Series to use      len : Reference lookback length   Returns: Calculated period Based on Derivation Scaled Super Smoother (DSSS) by John F. Ehlers Originally used with Super Smoother RMS originally has 50 bar lookback. Changed to 4x length for better flexibility. Could be wrong. maa(src, len, threshold) Median Average Adaptation   Parameters:      src : Series to use      len : Reference lookback length      threshold : Adjustment threshold (lower is smaller length, default: 0.002, min: 0.0001)   Returns: Calculated period Based on Median Average Adaptive Filter by John F. Ehlers Discovered and implemented by @cheatcountry: I took the adaptation part, as it is just an EMA otherwise fra(len, fc, sc) Fractal Adaptation   Parameters:      len : Reference lookback length      fc : Fast constant (default: 1)      sc : Slow constant (default: 200)   Returns: Calculated period Based on FRAMA by John F. Ehlers Modified to allow lower and upper bounds by an unknown author I took the adaptation part, as it is just an EMA otherwise mama(src, dynLow, dynHigh) MESA Adaptation - MAMA Alpha   Parameters:      src : Series to use      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length   Returns: Calculated period Based on MESA Adaptive Moving Average by John F. Ehlers Introduced in the September 2001 issue of Stocks and Commodities Inspired by the @everget implementation: I took the adaptation part, as it is just an EMA otherwise doAdapt(type, src, len, dynLow, dynHigh, chandeSDLen, chandeSmooth, chandePower) Execute a particular Length Adaptation from the list   Parameters:      type : Length Adaptation type to use      src : Series to use      len : Reference lookback length      dynLow : Lower bound for the dynamic length      dynHigh : Upper bound for the dynamic length      chandeSDLen : Lookback length of Standard deviation for Chande's Dynamic Length      chandeSmooth : Smoothing length of Standard deviation for Chande's Dynamic Length      chandePower : Exponent of the length adaptation for Chande's Dynamic Length (lower is smaller variation)   Returns: Calculated period (float, not limited) doMA(type, src, len) MA wrapper on wrapper: if DSSS is selected, calculate it here   Parameters:      type : MA type to use      src : Series to use      len : Filtering length   Returns: Filtered series Demonstration of a combined indicator: Deviation Scaled Super Smoother
Libreria Pine Script®
di lastguru
2
DMI + HMA - No Risk ManagementDMI (Directional Movement Index) and HMA (Hull Moving Average) The DMI and HMA make a great combination, The DMI will gauge the market direction, while the HMA will add confirmation to the trend strength. What is the DMI? The DMI is an indicator that was developed by J. Welles Wilder in 1978. The Indicator was designed to identify in which direction the price is moving. This is done by comparing previous highs and lows and drawing 2 lines. 1. A Positive movement line 2. A Negative movement line A third line can be added, which would be known as the ADX line or Average Directional Index. This can also be used to gauge the strength in which direction the market is moving. When the Positive movement line (DI+) is above the Negative movement line (DI-) there is more upward pressure. Ofcourse visa versa, when the DI- is above the DI+ that would indicate more downwards pressure. Want to know more about HMA? Check out one of our other published scripts What is this strategy doing? We are first waiting for the DMI to cross in our favoured direction, after that, we wait for the HMA to signal the entry. Without both conditions being true, no trade will be made. Long Entries 1. DI+ crosses above DI- 2. HMA line 1 is above HMA line 2 Short Entries 1. DI- Crosses above DI+ 2. HMA line 1 is below HMA lilne 2 Its as simple as that. Conclusion While this strategy does have its downsides, that can be reduced by adding some risk manegment into the script. In general the trade profitability is above average, And the max drawdown is at a minimum. The settings have been optimised to suite BTCUSDT PERP markets. Though with small adjustments it can be used on many assets!
Strategia Pine Script®
di Tuned_Official
1
SMU Stock ThermometerThis script shows various technical indicators in a stacked vertical candle called Market Termometer. It helps to see the price action in one single vertical column where the actual price moves up or down. So you can see the price change based on your custom setting levels. I've been studying ALGO for over a year and made many live experiment trades long and shorts. So, I'm trying to find a way to see what is ALGos next move. If it sounds far-fetch, then you should see my other published scripts. Here is example of how ALGo dance around old indicators, which is why I started creating a bunch of new indicators that ALGO doesn't know Example: Impact-driven-algorithm= Large volume masked as small volume to keep the price at desired level. So, your chart says overbought but market doesn't drop for days Cost-driven-algorithm= Hedge fund buy every time at lower price and prevent others to buy low, moving up fast. Is like a clock with millisecond timing and ALGO owners know when to buy low and when to sell high If you have a good idea, let me know so i can include it the future versions. Enjoy and think outside the box, the only way to beat the ALGO
Indicatore Pine Script®
di stockmarketupdate
10
[Pandora][Swarm] Rapid Exponential Moving AverageENVISIONING POSSIBILITY What is the theoretical pinnacle of possibility? The current state of algorithmic affairs falls far short of my aspirations for achievable feasibility. I'm lifting the lid off of Pandora's box once again, very publicly this time, as a brute force challenge to conventional 'wisdom'. The unfolding series of time mandates a transcendental systemic alteration... THE MOVING AVERAGE ZOO: The realm of digital signal processing for trading is filled with familiar antiquated filtering tools. Two families of filtration, being 'infinite impulse response' (EMA, RMA, etc.) and 'finite impulse response' (WMA, SMA, etc.), are prevalently employed without question. These filter types are the mules and donkeys of data analysis, broadly accepted for use in finance. At first glance, they appear sufficient for most tasks, offering a basic straightforward way to reduce noise and highlight trends. Yet, beneath their simplistic facade lies a constellation of limitations and impediments, each having its own finicky quirks. Upon closer inspection, identifiable drawbacks render them far from ideal for many real-world applications in today's volatile markets. KNOWN FUNDAMENTAL FLAWS: Despite commonplace moving average (MA) popularity, these conventional filters suffer from an assortment of fundamental flaws. Most of them don't genuinely address core challenges of how to preserve the true dynamics of a signal while suppressing noise and retaining cutoff frequency compliance. Their simple cookie cutter structures make them ill-suited in actuality for dynamic market environments. In reality, they often trade one problem for another dilemma, forsaking analytics to choose between distortion and delay. A deeper seeded issue remains within frequency compliance, how adequately a filter respects (or disrespects) the underlying signal’s spectral properties according to it's assigned periodic parameter. Traditional MAs habitually distort phase relationships, causing delayed reactions with surplus lag or exaggerations with excessive undershoot/overshoot. For applications requiring timely resilience, such as algorithmic trading, these shortcomings are often functionally unacceptable. What’s needed is vigorous filters that can more accurately retain signal behaviors while minimizing lag without sacrificing smoothness and uniformity. Until then, the public MA zoo remains as a collection of corny compromises, rather than a favorable toolbelt of solutions. P.S.: In PSv7+, in my opinion, many of these geriatric MAs deserve no future with ease of access for the naive, simply not knowing these filters are most likely creating bigger problems than solving any. R.E.M.A. What is this? I prefer to think of it as the "radical EMA", definitely along my lines of a retire everything morte algorithm. This isn't your run of the mill average from the petting zoo. I would categorize it as a paradigm shifting rampant economic masochistic annihilator, sufficiently good enough to begin ruthlessly executing moving averages left and right. Um, yeah... that kind of moving average destructor as you may soon recognize with a few 'Filters+' settings adjustments, realizing ordinary EMA has been doing us an injustice all this time. Does it possess the capability to relentlessly exterminate most averaging filters in existence? Well, it's about time we find out, by uncaging it on the loose into the greater economic wilderness. Only then can we truly find out if it is indeed a radical exponential market accelerant whose time has come. If it is, then it may eventually become a reality erasing monolithic anomaly destined for greatness, ultimately changing the entire landscape of trading in perpetuity. UNLEASHING NEXT-GEN: This lone next generation exoweapon algorithm is intended to initiate the transformative beginning stages of mass filtration deprecation. However, it won't be the only one, just the first arrival of it's alien kind from me. Welcome to notion #1 of my future filtration frontier, on this episode of the algorithmic twilight zone. Where reality takes a twisting turn one dimension beyond practical logic, after persistent models of mindset disintegrate into insignificance, followed by illusory perception confronted into cognitive dissonance. An evolutionary path to genuine advancement resides outside the prison of preconceptions, manifesting only after divergence from persistent binding restrictions of dogmatic doctrines. Such a genesis in transformative thinking will catalyze unbounded cognitive potential, plowing the way for the cultivation of total redesigns of thought. Futuristic innovative breakthroughs demand the surrender of legacy and outmoded understandings. Now that the world's largest assembly of investors has been ensembled, there are additional tasks left to perform. I'm compelled to deploy this mathematical-weapon of mass financial creation into it's rightful destined hands, to "WE THE PEOPLE" of TV. SCRIPT INTENTION: Deprecate anything and everything as any non-commercial member sees desirably fit. This includes your existing code formulations already in working functional modes of operation AND/OR future projects in the works. Swapping is nearly as simple as copying and pasting with meager modifications, after you have identified comparable likeness in this indicators settings with a visual assessment. Results may become eye opening, but only if you dare to look and test. Where you may suspect a ta.filter() is lacking sufficient luster or may be flat out majorly deficient, employing rema, drema, trema, or qrema configurations may be a more suitable replacement. That's up to you to discern. My code satire already identifies likely bottom of the barrel suspects that either belong in the extinction record or have already been marked for deprecation. They are ordered more towards the bottom by rank where they belong. SuperSmoother is a masterpiece here to stay, being my original go-to reference filter. Everything you see here is already deprecated, including REMA... REMA CHARACTERISTICS - VERY low lag - No overshoot - Frequency compliant - Proper initialization at bar_index==0 - Period parameter accepts poitive floating point numerics (AND integers!) - Infinite impulse response (IIR) filter - Compact code footprint - Minimized computational overhead
Indicatore Pine Script®
di ImmortalFreedom
4
3D Surface Modeling [PhenLabs]📊 3D Surface Modeling Version: PineScript™ v6 📌 Description The 3D Surface Modeling indicator revolutionizes technical analysis by generating three-dimensional visualizations of multiple technical indicators across various timeframes. This advanced analytical tool processes and renders complex indicator data through a sophisticated matrix-based calculation system, creating an intuitive 3D surface representation of market dynamics. The indicator employs array-based computations to simultaneously analyze multiple instances of selected technical indicators, mapping their behavior patterns across different temporal dimensions. This unique approach enables traders to identify complex market patterns and relationships that may be invisible in traditional 2D charts. 🚀 Points of Innovation Matrix-Based Computation Engine: Processes up to 500 concurrent indicator calculations in real-time Dynamic 3D Rendering System: Creates depth perception through sophisticated line arrays and color gradients Multi-Indicator Integration: Seamlessly combines VWAP, Hurst, RSI, Stochastic, CCI, MFI, and Fractal Dimension analyses Adaptive Scaling Algorithm: Automatically adjusts visualization parameters based on indicator type and market conditions 🔧 Core Components Indicator Processing Module: Handles real-time calculation of multiple technical indicators using array-based mathematics 3D Visualization Engine: Converts indicator data into three-dimensional surfaces using line arrays and color mapping Dynamic Scaling System: Implements custom normalization algorithms for different indicator types Color Gradient Generator: Creates depth perception through programmatic color transitions 🔥 Key Features Multi-Indicator Support: Comprehensive analysis across seven different technical indicators Customizable Visualization: User-defined color schemes and line width parameters Real-time Processing: Continuous calculation and rendering of 3D surfaces Cross-Timeframe Analysis: Simultaneous visualization of indicator behavior across multiple periods 🎨 Visualization Surface Plot: Three-dimensional representation using up to 500 lines with dynamic color gradients Depth Indicators: Color intensity variations showing indicator value magnitude Pattern Recognition: Visual identification of market structures across multiple timeframes 📖 Usage Guidelines Indicator Selection Type: VWAP, Hurst, RSI, Stochastic, CCI, MFI, Fractal Dimension Default: VWAP Starting Length: Minimum 5 periods Default: 10 Step Size: Interval between calculations Range: 1-10 Visualization Parameters Color Scheme: Green, Red, Blue options Line Width: 1-5 pixels Surface Resolution: Up to 500 lines ✅ Best Use Cases Multi-timeframe market analysis Pattern recognition across different technical indicators Trend strength assessment through 3D visualization Market behavior study across multiple periods ⚠️ Limitations High computational resource requirements Maximum 500 line restriction Requires substantial historical data Complex visualization learning curve 🔬 How It Works 1. Data Processing: Calculates selected indicator values across multiple timeframes Stores results in multi-dimensional arrays Applies custom scaling algorithms 2. Visualization Generation: Creates line arrays for 3D surface representation Applies color gradients based on value magnitude Renders real-time updates to surface plot 3. Display Integration: Synchronizes with chart timeframe Updates surface plot dynamically Maintains visual consistency across updates 🌟 Credits: Inspired by LonesomeTheBlue (modified for multiple indicator types with scaling fixes and additional unique mappings) 💡 Note: Optimal performance requires sufficient computing resources and historical data. Users should start with default settings and gradually adjust parameters based on their analysis requirements and system capabilities.
Indicatore Pine Script®
di PhenLabs
3
ICT Opening Range Projections (tristanlee85)ICT Opening Range Projections This indicator visualizes key price levels based on ICT's (Inner Circle Trader) "Opening Range" concept. This 30-minute time interval establishes price levels that the algorithm will refer to throughout the session. The indicator displays these levels, including standard deviation projections, internal subdivisions (quadrants), and the opening price. 🟪 What It Does The Opening Range is a crucial 30-minute window where market algorithms establish significant price levels. ICT theory suggests this range forms the basis for daily price movement. This script helps you: Mark the high, low, and opening price of each session. Divide the range into quadrants (premium, discount, and midpoint/Consequent Encroachment). Project potential price targets beyond the range using configurable standard deviation multiples . 🟪 How to Use It This tool aids in time-based technical analysis rooted in ICT's Opening Range model, helping you observe price interaction with algorithmic levels. Example uses include: Identifying early structural boundaries. Observing price behavior within premium/discount zones. Visualizing initial displacement from the range to anticipate future moves. Comparing price reactions at projected standard deviation levels. Aligning price action with significant times like London or NY Open. Note: This indicator provides a visual framework; it does not offer trade signals or interpretations. 🟪 Key Information Time Zone: New York time (ET) is required on your chart. Sessions: Supports multiple sessions, including NY midnight, NY AM, NY PM, and three custom timeframes. Time Interval: Supports multi-timeframe up to 15 minutes. Best used on a 1-minute chart for accuracy. 🟪 Session Options The Opening Range interval is configurable for up to 6 sessions: Pre-defined ICT Sessions: NY Midnight: 12:00 AM – 12:30 AM ET NY AM: 9:30 AM – 10:00 AM ET NY PM: 1:30 PM – 2:00 PM ET Custom Sessions: Three user-defined start/end time pairs. This example shows a custom session from 03:30 - 04:00: 🟪 Understanding the Levels The Opening Price is the open of the first 1-minute candle within the chosen session. At session close, the Opening Range is calculated using its High and Low . An optional swing-based mode uses swing highs/lows for range boundaries. The range is divided into quadrants by its midpoint ( Consequent Encroachment or CE): Upper Quadrant: CE to high (premium). Lower Quadrant: Low to CE (discount). These subdivisions help visualize internal range dynamics, where price often reacts during algorithmic delivery. 🟪 Working with Ranges By default, the range is determined by the highest high and lowest low of the 30-minute session: A range can also be determined by the highest/lowest swing points: Quadrants outline the premium and discount of a range that price will reference: Small ranges still follow the same algorithmic logic, but may be deemed insignificant for one's trading. These can be filtered in the settings by specifying a minimum ticks limit. In this example, the range is 42 ticks (10.5 points) but the indicator is configured for 80 ticks (20 points). We can select which levels will plot if the range is below the limit. Here, only the 00:00 opening price is plotted: You may opt to include the range high/low, quadrants, and projections as well. This will plot a red (configurable) range bracket to indicate it is below the limit while plotting the levels: 🟪 Price Projections Projections extend beyond the Opening Range using standard deviations, framing the market beyond the initial session and identifying potential targets. You define the standard deviation multiples (e.g., 1.0, 1.5, 2.0). Both positive and negative extensions are displayed, symmetrically projected from the range's high and low. The Dynamic Levels option plots only the next projection level once price crosses the previous extreme. For example, only the 0.5 STDEV level plots until price reaches it, then the 1.0 level appears, and so on. This continues up to your defined maximum projections, or indefinitely if standard deviations are set to 0. This example shows dynamic levels for a total of 6 sessions, only 1 of which meet a configured minimum limit of 50 ticks: Small ranges followed by significant displacement are impacted the most with the number of levels plotted. You may hide projections when configuring the minimum ticks. A fixed standard deviation will plot levels in both directions, regardless of the price range. Here, we plot up to 3.0 which hiding projections for small ranges: 🟪 Legal Disclaimer This indicator is provided for informational and educational purposes only. It is not financial advice, and should not be construed as a recommendation to buy or sell any financial instrument. Trading involves substantial risk, and you could lose a significant amount of money. Past performance is not indicative of future results. Always consult with a qualified financial professional before making any trading or investment decisions. The creators and distributors of this indicator assume no responsibility for your trading outcomes.
Indicatore Pine Script®
di tristanlee85
6
Momentum + Keltner Stochastic Combo)The Momentum-Keltner-Stochastic Combination Strategy: A Technical Analysis and Empirical Validation This study presents an advanced algorithmic trading strategy that implements a hybrid approach between momentum-based price dynamics and relative positioning within a volatility-adjusted Keltner Channel framework. The strategy utilizes an innovative "Keltner Stochastic" concept as its primary decision-making factor for market entries and exits, while implementing a dynamic capital allocation model with risk-based stop-loss mechanisms. Empirical testing demonstrates the strategy's potential for generating alpha in various market conditions through the combination of trend-following momentum principles and mean-reversion elements within defined volatility thresholds. 1. Introduction Financial market trading increasingly relies on the integration of various technical indicators for identifying optimal trading opportunities (Lo et al., 2000). While individual indicators are often compromised by market noise, combinations of complementary approaches have shown superior performance in detecting significant market movements (Murphy, 1999; Kaufman, 2013). This research introduces a novel algorithmic strategy that synthesizes momentum principles with volatility-adjusted envelope analysis through Keltner Channels. 2. Theoretical Foundation 2.1 Momentum Component The momentum component of the strategy builds upon the seminal work of Jegadeesh and Titman (1993), who demonstrated that stocks which performed well (poorly) over a 3 to 12-month period continue to perform well (poorly) over subsequent months. As Moskowitz et al. (2012) further established, this time-series momentum effect persists across various asset classes and time frames. The present strategy implements a short-term momentum lookback period (7 bars) to identify the prevailing price direction, consistent with findings by Chan et al. (2000) that shorter-term momentum signals can be effective in algorithmic trading systems. 2.2 Keltner Channels Keltner Channels, as formalized by Chester Keltner (1960) and later modified by Linda Bradford Raschke, represent a volatility-based envelope system that plots bands at a specified distance from a central exponential moving average (Keltner, 1960; Raschke & Connors, 1996). Unlike traditional Bollinger Bands that use standard deviation, Keltner Channels typically employ Average True Range (ATR) to establish the bands' distance from the central line, providing a smoother volatility measure as established by Wilder (1978). 2.3 Stochastic Oscillator Principles The strategy incorporates a modified stochastic oscillator approach, conceptually similar to Lane's Stochastic (Lane, 1984), but applied to a price's position within Keltner Channels rather than standard price ranges. This creates what we term "Keltner Stochastic," measuring the relative position of price within the volatility-adjusted channel as a percentage value. 3. Strategy Methodology 3.1 Entry and Exit Conditions The strategy employs a contrarian approach within the channel framework: Long Entry Condition: Close price > Close price periods ago (momentum filter) KeltnerStochastic < threshold (oversold within channel) Short Entry Condition: Close price < Close price periods ago (momentum filter) KeltnerStochastic > threshold (overbought within channel) Exit Conditions: Exit long positions when KeltnerStochastic > threshold Exit short positions when KeltnerStochastic < threshold This methodology aligns with research by Brock et al. (1992) on the effectiveness of trading range breakouts with confirmation filters. 3.2 Risk Management Stop-loss mechanisms are implemented using fixed price movements (1185 index points), providing definitive risk boundaries per trade. This approach is consistent with findings by Sweeney (1988) that fixed stop-loss systems can enhance risk-adjusted returns when properly calibrated. 3.3 Dynamic Position Sizing The strategy implements an equity-based position sizing algorithm that increases or decreases contract size based on cumulative performance: $ContractSize = \min(baseContracts + \lfloor\frac{\max(profitLoss, 0)}{equityStep}\rfloor - \lfloor\frac{|\min(profitLoss, 0)|}{equityStep}\rfloor, maxContracts)$ This adaptive approach follows modern portfolio theory principles (Markowitz, 1952) and Kelly criterion concepts (Kelly, 1956), scaling exposure proportionally to account equity. 4. Empirical Performance Analysis Using historical data across multiple market regimes, the strategy demonstrates several key performance characteristics: Enhanced performance during trending markets with moderate volatility Reduced drawdowns during choppy market conditions through the dual-filter approach Optimal performance when the threshold parameter is calibrated to market-specific characteristics (Pardo, 2008) 5. Strategy Limitations and Future Research While effective in many market conditions, this strategy faces challenges during: Rapid volatility expansion events where stop-loss mechanisms may be inadequate Prolonged sideways markets with insufficient momentum Markets with structural changes in volatility profiles Future research should explore: Adaptive threshold parameters based on regime detection Integration with additional confirmatory indicators Machine learning approaches to optimize parameter selection across different market environments (Cavalcante et al., 2016) References Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764. Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P., & Oliveira, A. L. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55, 194-211. Chan, L. K. C., Jegadeesh, N., & Lakonishok, J. (2000). Momentum strategies. The Journal of Finance, 51(5), 1681-1713. Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65-91. Kaufman, P. J. (2013). Trading systems and methods (5th ed.). John Wiley & Sons. Kelly, J. L. (1956). A new interpretation of information rate. The Bell System Technical Journal, 35(4), 917-926. Keltner, C. W. (1960). How to make money in commodities. The Keltner Statistical Service. Lane, G. C. (1984). Lane's stochastics. Technical Analysis of Stocks & Commodities, 2(3), 87-90. Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The Journal of Finance, 55(4), 1705-1765. Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91. Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104(2), 228-250. Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. New York Institute of Finance. Pardo, R. (2008). The evaluation and optimization of trading strategies (2nd ed.). John Wiley & Sons. Raschke, L. B., & Connors, L. A. (1996). Street smarts: High probability short-term trading strategies. M. Gordon Publishing Group. Sweeney, R. J. (1988). Some new filter rule tests: Methods and results. Journal of Financial and Quantitative Analysis, 23(3), 285-300. Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
Strategia Pine Script®
di EdgeTools
Prediction Based on Linreg & Atr We created this algorithm with the goal of predicting future prices 📊, specifically where the value of any asset will go in the next 20 periods ⏳. It uses linear regression based on past prices, calculating a slope and an intercept to forecast future behavior 🔮. This prediction is then adjusted according to market volatility, measured by the ATR 📉, and the direction of trend signals, which are based on the MACD and moving averages 📈. How Does the Linreg & ATR Prediction Work? 1. Trend Calculation and Signals: o Technical Indicators: We use short- and long-term exponential moving averages (EMA), RSI, MACD, and Bollinger Bands 📊 to assess market direction and sentiment (not visually presented in the script). o Calculation Functions: These include functions to calculate slope, average, intercept, standard deviation, and Pearson's R, which are crucial for regression analysis 📉. 2. Predicting Future Prices: o Linear Regression: The algorithm calculates the slope, average, and intercept of past prices to create a regression channel 📈, helping to predict the range of future prices 🔮. o Standard Deviation and Pearson's R: These metrics determine the strength of the regression 🔍. 3. Adjusting the Prediction: o The predicted value is adjusted by considering market volatility (ATR 📉) and the direction of trend signals 🔮, ensuring that the prediction is aligned with the current market environment 🌍. 4. Visualization: o Prediction Lines and Bands: The algorithm plots lines that display the predicted future price along with a prediction range (upper and lower bounds) 📉📈. 5. EMA Cross Signals: o EMA Conditions and Total Score: A bullish crossover signal is generated when the total score is positive and the short EMA crosses above the long EMA 📈. A bearish crossover signal is generated when the total score is negative and the short EMA crosses below the long EMA 📉. 6. Additional Considerations: o Multi-Timeframe Regression Channel: The script calculates regression channels for different timeframes (5m, 15m, 30m, 4h) ⏳, helping determine the overall market direction 📊 (not visually presented). Confidence Interpretation: • High Confidence (close to 100%): Indicates strong alignment between timeframes with a clear trend (bullish or bearish) 🔥. • Low Confidence (close to 0%): Shows disagreement or weak signals between timeframes ⚠️. Confidence complements the interpretation of the prediction range and expected direction 🔮, aiding in decision-making for market entry or exit 🚀. Español Creamos este algoritmo con el objetivo de predecir los precios futuros 📊, específicamente hacia dónde irá el valor de cualquier activo en los próximos 20 períodos ⏳. Utiliza regresión lineal basada en los precios pasados, calculando una pendiente y una intersección para prever el comportamiento futuro 🔮. Esta predicción se ajusta según la volatilidad del mercado, medida por el ATR 📉, y la dirección de las señales de tendencia, que se basan en el MACD y las medias móviles 📈. ¿Cómo Funciona la Predicción con Linreg & ATR? Cálculo de Tendencias y Señales: Indicadores Técnicos: Usamos medias móviles exponenciales (EMA) a corto y largo plazo, RSI, MACD y Bandas de Bollinger 📊 para evaluar la dirección y el sentimiento del mercado (no presentados visualmente en el script). Funciones de Cálculo: Incluye funciones para calcular pendiente, media, intersección, desviación estándar y el coeficiente de correlación de Pearson, esenciales para el análisis de regresión 📉. Predicción de Precios Futuros: Regresión Lineal: El algoritmo calcula la pendiente, la media y la intersección de los precios pasados para crear un canal de regresión 📈, ayudando a predecir el rango de precios futuros 🔮. Desviación Estándar y Pearson's R: Estas métricas determinan la fuerza de la regresión 🔍. Ajuste de la Predicción: El valor predicho se ajusta considerando la volatilidad del mercado (ATR 📉) y la dirección de las señales de tendencia 🔮, asegurando que la predicción esté alineada con el entorno actual del mercado 🌍. Visualización: Líneas y Bandas de Predicción: El algoritmo traza líneas que muestran el precio futuro predicho, junto con un rango de predicción (límites superior e inferior) 📉📈. Señales de Cruce de EMAs: Condiciones de EMAs y Puntaje Total: Se genera una señal de cruce alcista cuando el puntaje total es positivo y la EMA corta cruza por encima de la EMA larga 📈. Se genera una señal de cruce bajista cuando el puntaje total es negativo y la EMA corta cruza por debajo de la EMA larga 📉. Consideraciones Adicionales: Canal de Regresión Multi-Timeframe: El script calcula canales de regresión para diferentes marcos de tiempo (5m, 15m, 30m, 4h) ⏳, ayudando a determinar la dirección general del mercado 📊 (no presentado visualmente). Interpretación de la Confianza: Alta Confianza (cerca del 100%): Indica una fuerte alineación entre los marcos temporales con una tendencia clara (alcista o bajista) 🔥. Baja Confianza (cerca del 0%): Muestra desacuerdo o señales débiles entre los marcos temporales ⚠️. La confianza complementa la interpretación del rango de predicción y la dirección esperada 🔮, ayudando en las decisiones de entrada o salida en el mercado 🚀.
Indicatore Pine Script®
di PINE_LABS
7
RSI with Swing Trade by Kelvin_VAlgorithm Description: "RSI with Swing Trade by Kelvin_V" 1. Introduction: This algorithm uses the RSI (Relative Strength Index) and optional Moving Averages (MA) to detect potential uptrends and downtrends in the market. The key feature of this script is that it visually changes the candle colors based on the market conditions, making it easier for users to identify potential trend swings or wave patterns. The strategy offers flexibility by allowing users to enable or disable the MA condition. When the MA condition is enabled, the strategy will confirm trends using two moving averages. When disabled, the strategy will only use RSI to detect potential market swings. 2. Key Features of the Algorithm: RSI (Relative Strength Index): The RSI is used to identify potential market turning points based on overbought and oversold conditions. When the RSI exceeds a predefined upper threshold (e.g., 60), it suggests a potential uptrend. When the RSI drops below a lower threshold (e.g., 40), it suggests a potential downtrend. Moving Averages (MA) - Optional: Two Moving Averages (Short MA and Long MA) are used to confirm trends. If the Short MA crosses above the Long MA, it indicates an uptrend. If the Short MA crosses below the Long MA, it indicates a downtrend. Users have the option to enable or disable this MA condition. Visual Candle Coloring: Green candles represent a potential uptrend, indicating a bullish move based on RSI (and MA if enabled). Red candles represent a potential downtrend, indicating a bearish move based on RSI (and MA if enabled). 3. How the Algorithm Works: RSI Levels: The user can set RSI upper and lower bands to represent potential overbought and oversold levels. For example: RSI > 60: Indicates a potential uptrend (bullish move). RSI < 40: Indicates a potential downtrend (bearish move). Optional MA Condition: The algorithm also allows the user to apply the MA condition to further confirm the trend: Short MA > Long MA: Confirms an uptrend, reinforcing a bullish signal. Short MA < Long MA: Confirms a downtrend, reinforcing a bearish signal. This condition can be disabled, allowing the user to focus solely on RSI signals if desired. Swing Trade Logic: Uptrend: If the RSI exceeds the upper threshold (e.g., 60) and (optionally) the Short MA is above the Long MA, the candles will turn green to signal a potential uptrend. Downtrend: If the RSI falls below the lower threshold (e.g., 40) and (optionally) the Short MA is below the Long MA, the candles will turn red to signal a potential downtrend. Visual Representation: The candle colors change dynamically based on the RSI values and moving average conditions, making it easier for traders to visually identify potential trend swings or wave patterns without relying on complex chart analysis. 4. User Customization: The algorithm provides multiple customization options: RSI Length: Users can adjust the period for RSI calculation (default is 4). RSI Upper Band (Potential Uptrend): Users can customize the upper RSI level (default is 60) to indicate a potential bullish move. RSI Lower Band (Potential Downtrend): Users can customize the lower RSI level (default is 40) to indicate a potential bearish move. MA Type: Users can choose between SMA (Simple Moving Average) and EMA (Exponential Moving Average) for moving average calculations. Enable/Disable MA Condition: Users can toggle the MA condition on or off, depending on whether they want to add moving averages to the trend confirmation process. 5. Benefits of the Algorithm: Easy Identification of Trends: By changing candle colors based on RSI and MA conditions, the algorithm makes it easy for users to visually detect potential trend reversals and trend swings. Flexible Conditions: The user has full control over the RSI and MA settings, allowing them to adapt the strategy to different market conditions and timeframes. Clear Visualization: With the candle color changes, users can quickly recognize when a potential uptrend or downtrend is forming, enabling faster decision-making in their trading. 6. Example Usage: Day traders: Can apply this strategy on short timeframes such as 5 minutes or 15 minutes to detect quick trends or reversals. Swing traders: Can use this strategy on longer timeframes like 1 hour or 4 hours to identify and follow larger market swings.
Indicatore Pine Script®
di Kelvin-WyckoffVSA
Intramarket Difference Index StrategyHi Traders !! The IDI Strategy: In layman’s terms this strategy compares two indicators across markets and exploits their differences. note: it is best the two markets are correlated as then we know we are trading a short to long term deviation from both markets' general trend with the assumption both markets will trend again sometime in the future thereby exhausting our trading opportunity. 📍 Import Notes: This Strategy calculates trade position size independently (i.e. risk per trade is controlled in the user inputs tab), this means that the ‘Order size’ input in the ‘Properties’ tab will have no effect on the strategy. Why ? because this allows us to define custom position size algorithms which we can use to improve our risk management and equity growth over time. Here we have the option to have fixed quantity or fixed percentage of equity ATR (Average True Range) based stops in addition to the turtle trading position size algorithm. ‘Pyramiding’ does not work for this strategy’, similar to the order size input togeling this input will have no effect on the strategy as the strategy explicitly defines the maximum order size to be 1. This strategy is not perfect, and as of writing of this post I have not traded this algo. Always take your time to backtests and debug the strategy. 🔷 The IDI Strategy: By default this strategy pulls data from your current TV chart and then compares it to the base market, be default BINANCE:BTCUSD . The strategy pulls SMA and RSI data from either market (we call this the difference data), standardizes the data (solving the different unit problem across markets) such that it is comparable and then differentiates the data, calling the result of this transformation and difference the Intramarket Difference (ID). The formula for the the ID is ID = market1_diff_data - market2_diff_data (1) Where market(i)_diff_data = diff_data / ATR(j)_market(i)^0.5, where i = {1, 2} and j = the natural numbers excluding 0 Formula (1) interpretation is the following When ID > 0: this means the current market outperforms the base market When ID = 0: Markets are at long run equilibrium When ID < 0: this means the current market underperforms the base market To form the strategy we define one of two strategy type’s which are Trend and Mean Revesion respectively. 🔸 Trend Case: Given the ‘‘Strategy Type’’ is equal to TREND we define a threshold for which if the ID crosses over we go long and if the ID crosses under the negative of the threshold we go short. The motivating idea is that the ID is an indicator of the two symbols being out of sync, and given we know volatility clustering, momentum and mean reversion of anomalies to be a stylised fact of financial data we can construct a trading premise. Let's first talk more about this premise. For some markets (cryptocurrency markets - synthetic symbols in TV) the stylised fact of momentum is true, this means that higher momentum is followed by higher momentum, and given we know momentum to be a vector quantity (with magnitude and direction) this momentum can be both positive and negative i.e. when the ID crosses above some threshold we make an assumption it will continue in that direction for some time before executing back to its long run equilibrium of 0 which is a reasonable assumption to make if the market are correlated. For example for the BTCUSD - ETHUSD pair, if the ID > +threshold (inputs for MA and RSI based ID thresholds are found under the ‘‘INTRAMARKET DIFFERENCE INDEX’’ group’), ETHUSD outperforms BTCUSD, we assume the momentum to continue so we go long ETHUSD. In the standard case we would exit the market when the IDI returns to its long run equilibrium of 0 (for the positive case the ID may return to 0 because ETH’s difference data may have decreased or BTC’s difference data may have increased). However in this strategy we will not define this as our exit condition, why ? This is because we want to ‘‘let our winners run’’, to achieve this we define a trailing Donchian Channel stop loss (along with a fixed ATR based stop as our volatility proxy). If we were too use the 0 exit the strategy may print a buy signal (ID > +threshold in the simple case, market regimes may be used), return to 0 and then print another buy signal, and this process can loop may times, this high trade frequency means we fail capture the entire market move lowering our profit, furthermore on lower time frames this high trade frequencies mean we pay more transaction costs (due to price slippage, commission and big-ask spread) which means less profit. By capturing the sum of many momentum moves we are essentially following the trend hence the trend following strategy type. Here we also print the IDI (with default strategy settings with the MA difference type), we can see that by letting our winners run we may catch many valid momentum moves, that results in a larger final pnl that if we would otherwise exit based on the equilibrium condition(Valid trades are denoted by solid green and red arrows respectively and all other valid trades which occur within the original signal are light green and red small arrows). another example... Note: if you would like to plot the IDI separately copy and paste the following code in a new Pine Script indicator template. indicator("IDI") // INTRAMARKET INDEX var string g_idi = "intramarket diffirence index" ui_index_1 = input.symbol("BINANCE:BTCUSD", title = "Base market", group = g_idi) // ui_index_2 = input.symbol("BINANCE:ETHUSD", title = "Quote Market", group = g_idi) type = input.string("MA", title = "Differrencing Series", options = , group = g_idi) ui_ma_lkb = input.int(24, title = "lookback of ma and volatility scaling constant", group = g_idi) ui_rsi_lkb = input.int(14, title = "Lookback of RSI", group = g_idi) ui_atr_lkb = input.int(300, title = "ATR lookback - Normalising value", group = g_idi) ui_ma_threshold = input.float(5, title = "Threshold of Upward/Downward Trend (MA)", group = g_idi) ui_rsi_threshold = input.float(20, title = "Threshold of Upward/Downward Trend (RSI)", group = g_idi) //>>+----------------------------------------------------------------+} // CUSTOM FUNCTIONS | //<<+----------------------------------------------------------------+{ // construct UDT (User defined type) containing the IDI (Intramarket Difference Index) source values // UDT will hold many variables / functions grouped under the UDT type functions float Close // close price float ma // ma of symbol float rsi // rsi of the asset float atr // atr of the asset // the security data getUDTdata(symbol, malookback, rsilookback, atrlookback) => indexHighTF = barstate.isrealtime ? 1 : 0 = request.security(symbol, timeframe = timeframe.period, expression = [close , // Instentiate UDT variables ta.sma(close, malookback) , ta.rsi(close, rsilookback) , ta.atr(atrlookback) ]) data = functions.new(close_, ma_, rsi_, atr_) data // Intramerket Difference Index idi(type, symbol1, malookback, rsilookback, atrlookback, mathreshold, rsithreshold) => threshold = float(na) index1 = getUDTdata(symbol1, malookback, rsilookback, atrlookback) index2 = getUDTdata(syminfo.tickerid, malookback, rsilookback, atrlookback) // declare difference variables for both base and quote symbols, conditional on which difference type is selected var diffindex1 = 0.0, var diffindex2 = 0.0, // declare Intramarket Difference Index based on series type, note // if > 0, index 2 outpreforms index 1, buy index 2 (momentum based) until equalibrium // if < 0, index 2 underpreforms index 1, sell index 1 (momentum based) until equalibrium // for idi to be valid both series must be stationary and normalised so both series hae he same scale intramarket_difference = 0.0 if type == "MA" threshold := mathreshold diffindex1 := (index1.Close - index1.ma) / math.pow(index1.atr*malookback, 0.5) diffindex2 := (index2.Close - index2.ma) / math.pow(index2.atr*malookback, 0.5) intramarket_difference := diffindex2 - diffindex1 else if type == "RSI" threshold := rsilookback diffindex1 := index1.rsi diffindex2 := index2.rsi intramarket_difference := diffindex2 - diffindex1 //>>+----------------------------------------------------------------+} // STRATEGY FUNCTIONS CALLS | //<<+----------------------------------------------------------------+{ // plot the intramarket difference = idi(type, ui_index_1, ui_ma_lkb, ui_rsi_lkb, ui_atr_lkb, ui_ma_threshold, ui_rsi_threshold) //>>+----------------------------------------------------------------+} plot(intramarket_difference, color = color.orange) hline(type == "MA" ? ui_ma_threshold : ui_rsi_threshold, color = color.green) hline(type == "MA" ? -ui_ma_threshold : -ui_rsi_threshold, color = color.red) hline(0) Note it is possible that after printing a buy the strategy then prints many sell signals before returning to a buy, which again has the same implication (less profit. Potentially because we exit early only for price to continue upwards hence missing the larger "trend"). The image below showcases this cenario and again, by allowing our winner to run we may capture more profit (theoretically). This should be clear... 🔸 Mean Reversion Case: We stated prior that mean reversion of anomalies is an standerdies fact of financial data, how can we exploit this ? We exploit this by normalizing the ID by applying the Ehlers fisher transformation. The transformed data is then assumed to be approximately normally distributed. To form the strategy we employ the same logic as for the z score, if the FT normalized ID > 2.5 (< -2.5) we buy (short). Our exit conditions remain unchanged (fixed ATR stop and trailing Donchian Trailing stop) 🔷 Position Sizing: If ‘‘Fixed Risk From Initial Balance’’ is toggled true this means we risk a fixed percentage of our initial balance, if false we risk a fixed percentage of our equity (current balance). Note we also employ a volatility adjusted position sizing formula, the turtle training method which is defined as follows. Turtle position size = (1/ r * ATR * DV) * C Where, r = risk factor coefficient (default is 20) ATR(j) = risk proxy, over j times steps DV = Dollar Volatility, where DV = (1/Asset Price) * Capital at Risk 🔷 Risk Management: Correct money management means we can limit risk and increase reward (theoretically). Here we employ Max loss and gain per day Max loss per trade Max number of consecutive losing trades until trade skip To read more see the tooltips (info circle). 🔷 Take Profit: By defualt the script uses a Donchain Channel as a trailing stop and take profit, In addition to this the script defines a fixed ATR stop losses (by defualt, this covers cases where the DC range may be to wide making a fixed ATR stop usefull), ATR take profits however are defined but optional. ATR SL and TP defined for all trades 🔷 Hurst Regime (Regime Filter): The Hurst Exponent (H) aims to segment the market into three different states, Trending (H > 0.5), Random Geometric Brownian Motion (H = 0.5) and Mean Reverting / Contrarian (H < 0.5). In my interpretation this can be used as a trend filter that eliminates market noise. We utilize the trending and mean reverting based states, as extra conditions required for valid trades for both strategy types respectively, in the process increasing our trade entry quality. 🔷 Example model Architecture: Here is an example of one configuration of this strategy, combining all aspects discussed in this post. Future Updates - Automation integration (next update)
Strategia Pine Script®
di Amjad_S
AI SuperTrend x Pivot Percentile - Strategy [PresentTrading]█ Introduction and How it is Different The AI SuperTrend x Pivot Percentile strategy is a sophisticated trading approach that integrates AI-driven analysis with traditional technical indicators. Combining the AI SuperTrend with the Pivot Percentile strategy highlights several key advantages: 1. Enhanced Accuracy in Trend Prediction: The AI SuperTrend utilizes K-Nearest Neighbors (KNN) algorithm for trend prediction, improving accuracy by considering historical data patterns. This is complemented by the Pivot Percentile analysis which provides additional context on trend strength. 2. Comprehensive Market Analysis: The integration offers a multi-faceted approach to market analysis, combining AI insights with traditional technical indicators. This dual approach captures a broader range of market dynamics. BTC 6H L/S Performance Local █ Strategy: How it Works - Detailed Explanation 🔶 AI-Enhanced SuperTrend Indicators 1. SuperTrend Calculation: - The SuperTrend indicator is calculated using a moving average and the Average True Range (ATR). The basic formula is: - Upper Band = Moving Average + (Multiplier × ATR) - Lower Band = Moving Average - (Multiplier × ATR) - The moving average type (SMA, EMA, WMA, RMA, VWMA) and the length of the moving average and ATR are adjustable parameters. - The direction of the trend is determined based on the position of the closing price in relation to these bands. 2. AI Integration with K-Nearest Neighbors (KNN): - The KNN algorithm is applied to predict trend direction. It uses historical price data and SuperTrend values to classify the current trend as bullish or bearish. - The algorithm calculates the 'distance' between the current data point and historical points. The 'k' nearest data points (neighbors) are identified based on this distance. - A weighted average of these neighbors' trends (bullish or bearish) is calculated to predict the current trend. For more please check: Multi-TF AI SuperTrend with ADX - Strategy 🔶 Pivot Percentile Analysis 1. Percentile Calculation: - This involves calculating the percentile ranks for high and low prices over a set of predefined lengths. - The percentile function is typically defined as: - Percentile = Value at (P/100) × (N + 1)th position - Where P is the desired percentile, and N is the number of data points. 2. Trend Strength Evaluation: - The calculated percentiles for highs and lows are used to determine the strength of bullish and bearish trends. - For instance, a high percentile rank in the high prices may indicate a strong bullish trend, and vice versa for bearish trends. For more please check: Pivot Percentile Trend - Strategy 🔶 Strategy Integration 1. Combining SuperTrend and Pivot Percentile: - The strategy synthesizes the insights from both AI-enhanced SuperTrend and Pivot Percentile analysis. - It compares the trend direction indicated by the SuperTrend with the strength of the trend as suggested by the Pivot Percentile analysis. 2. Signal Generation: - A trading signal is generated when both the AI-enhanced SuperTrend and the Pivot Percentile analysis agree on the trend direction. - For instance, a bullish signal is generated when both the SuperTrend is bullish, and the Pivot Percentile analysis shows strength in bullish trends. 🔶 Risk Management and Filters - ADX and DMI Filter: The strategy uses the Average Directional Index (ADX) and the Directional Movement Index (DMI) as filters to assess the trend's strength and direction. - Dynamic Trailing Stop Loss: Based on the SuperTrend indicator, the strategy dynamically adjusts stop-loss levels to manage risk effectively. This strategy stands out for its ability to combine real-time AI analysis with established technical indicators, offering traders a nuanced and responsive tool for navigating complex market conditions. The equations and algorithms involved are pivotal in accurately identifying market trends and potential trade opportunities. █ Usage To effectively use this strategy, traders should: 1. Understand the AI and Pivot Percentile Indicators: A clear grasp of how these indicators work will enable traders to make informed decisions. 2. Interpret the Signals Accurately: The strategy provides bullish, bearish, and neutral signals. Traders should align these signals with their market analysis and trading goals. 3. Monitor Market Conditions: Given that this strategy is sensitive to market dynamics, continuous monitoring is crucial for timely decision-making. 4. Adjust Settings as Needed: Traders should feel free to tweak the input parameters to suit their trading preferences and to respond to changing market conditions. █Default Settings and Their Impact on Performance 1. Trading Direction (Default: "Both") Effect: Determines whether the strategy will take long positions, short positions, or both. Adjusting this setting can align the strategy with the trader's market outlook or risk preference. 2. AI Settings (Neighbors: 3, Data Points: 24) Neighbors: The number of nearest neighbors in the KNN algorithm. A higher number might smooth out noise but could miss subtle, recent changes. A lower number makes the model more sensitive to recent data but may increase noise. Data Points: Defines the amount of historical data considered. More data points provide a broader context but may dilute recent trends' impact. 3. SuperTrend Settings (Length: 10, Factor: 3.0, MA Source: "WMA") Length: Affects the sensitivity of the SuperTrend indicator. A longer length results in a smoother, less sensitive indicator, ideal for long-term trends. Factor: Determines the bandwidth of the SuperTrend. A higher factor creates wider bands, capturing larger price movements but potentially missing short-term signals. MA Source: The type of moving average used (e.g., WMA - Weighted Moving Average). Different MA types can affect the trend indicator's responsiveness and smoothness. 4. AI Trend Prediction Settings (Price Trend: 10, Prediction Trend: 80) Price Trend and Prediction Trend Lengths: These settings define the lengths of weighted moving averages for price and SuperTrend, impacting the responsiveness and smoothness of the AI's trend predictions. 5. Pivot Percentile Settings (Length: 10) Length: Influences the calculation of pivot percentiles. A shorter length makes the percentile more responsive to recent price changes, while a longer length offers a broader view of price trends. 6. ADX and DMI Settings (ADX Length: 14, Time Frame: 'D') ADX Length: Defines the period for the Average Directional Index calculation. A longer period results in a smoother ADX line. Time Frame: Sets the time frame for the ADX and DMI calculations, affecting the sensitivity to market changes. 7. Commission, Slippage, and Initial Capital These settings relate to transaction costs and initial investment, directly impacting net profitability and strategy feasibility.
Strategia Pine Script®
di PresentTrading
16
HolidayLibrary "Holiday" - Full Control over Holidays and Daylight Savings Time (DLS) The Holiday Library is an essential tool for traders and analysts who engage in backtesting and live trading . This comprehensive library enables the incorporation of crucial calendar elements - specifically Daylight Savings Time (DLS) adjustments and public holidays - into trading strategies and backtesting environments. Key Features: - DLS Adjustments: The library takes into account the shifts in time due to Daylight Savings. This feature is particularly vital for backtesting strategies, as DLS can impact trading hours, which in turn affects the volatility and liquidity in the market. Accurate DLS adjustments ensure that backtesting scenarios are as close to real-life conditions as possible. - Comprehensive Holiday Metadata: The library includes a rich set of holiday metadata, allowing for the detailed scheduling of trading activities around public holidays. This feature is crucial for avoiding skewed results in backtesting, where holiday trading sessions might differ significantly in terms of volume and price movement. - Customizable Holiday Schedules: Users can add or remove specific holidays, tailoring the library to fit various regional market schedules or specific trading requirements. - Visualization Aids: The library supports on-chart labels, making it visually intuitive to identify holidays and DLS shifts directly on trading charts. Use Cases: 1. Strategy Development: When developing trading strategies, it’s important to account for non-trading days and altered trading hours due to holidays and DLS. This library enables a realistic and accurate representation of these factors. 2. Risk Management: Trading around holidays can be riskier due to thinner liquidity and greater volatility. By integrating holiday data, traders can better manage their risk exposure. 3. Backtesting Accuracy: For backtesting to be effective, it must simulate the actual market conditions as closely as possible. Incorporating holidays and DLS adjustments contributes to more reliable and realistic backtesting results. 4. Global Trading: For traders active in multiple global markets, this library provides an easy way to handle different holiday schedules and DLS shifts across regions. The Holiday Library is a versatile tool that enhances the precision and realism of trading simulations and strategy development . Its integration into the trading workflow is straightforward and beneficial for both novice and experienced traders. EasterAlgo(_year)   Calculates the date of Easter Sunday for a given year using the Anonymous Gregorian algorithm. `Gauss Algorithm for Easter Sunday` was developed by the mathematician Carl Friedrich Gauss This algorithm is based on the cycles of the moon and the fact that Easter always falls on the first Sunday after the first ecclesiastical full moon that occurs on or after March 21. While it's not considered to be 100% accurate due to rare exceptions, it does give the correct date in most cases. It's important to note that Gauss's formula has been found to be inaccurate for some 21st-century years in the Gregorian calendar. Specifically, the next suggested failure years are 2038, 2051. This function can be used for Good Friday (Friday before Easter), Easter Sunday, and Easter Monday (following Monday). en.wikipedia.org   Parameters:      _year (int) : `int` - The year for which to calculate the date of Easter Sunday. This should be a four-digit year (YYYY).   Returns: tuple - The month (1-12) and day (1-31) of Easter Sunday for the given year. easterInit()   Inits the date of Easter Sunday and Good Friday for a given year.   Returns: tuple - The month (1-12) and day (1-31) of Easter Sunday and Good Friday for the given year. isLeapYear(_year)   Determine if a year is a leap year.   Parameters:      _year (int) : `int` - 4 digit year to check => YYYY   Returns: `bool` - true if input year is a leap year method timezoneHelper(utc)   Helper function to convert UTC time.   Namespace types: series int, simple int, input int, const int   Parameters:      utc (int) : `int` - UTC time shift in hours.   Returns: `string`- UTC time string with shift applied. weekofmonth()   Function to find the week of the month of a given Unix Time.   Returns: number - The week of the month of the specified UTC time. dayLightSavingsAdjustedUTC(utc, adjustForDLS)   dayLightSavingsAdjustedUTC   Parameters:      utc (int) : `int` - The normal UTC timestamp to be used for reference.      adjustForDLS (bool) : `bool` - Flag indicating whether to adjust for daylight savings time (DLS).   Returns: `int` - The adjusted UTC timestamp for the given normal UTC timestamp. getDayOfYear(monthOfYear, dayOfMonth, weekOfMonth, dayOfWeek, lastOccurrenceInMonth, holiday)   Function gets the day of the year of a given holiday (1-366)   Parameters:      monthOfYear (int)      dayOfMonth (int)      weekOfMonth (int)      dayOfWeek (int)      lastOccurrenceInMonth (bool)      holiday (string)   Returns: `int` - The day of the year of the holiday 1-366. method buildMap(holidayMap, holiday, monthOfYear, weekOfMonth, dayOfWeek, dayOfMonth, lastOccurrenceInMonth, closingTime)   Function to build the `holidaysMap`.   Namespace types: map   Parameters:      holidayMap (map) : `map` - The map of holidays.      holiday (string) : `string` - The name of the holiday.      monthOfYear (int) : `int` - The month of the year of the holiday.      weekOfMonth (int) : `int` - The week of the month of the holiday.      dayOfWeek (int) : `int` - The day of the week of the holiday.      dayOfMonth (int) : `int` - The day of the month of the holiday.      lastOccurrenceInMonth (bool) : `bool` - Flag indicating whether the holiday is the last occurrence of the day in the month.      closingTime (int) : `int` - The closing time of the holiday.   Returns: `map` - The updated map of holidays holidayInit(addHolidaysArray, removeHolidaysArray, defaultHolidays)   Initializes a HolidayStorage object with predefined US holidays.   Parameters:      addHolidaysArray (array) : `array` - The array of additional holidays to be added.      removeHolidaysArray (array) : `array` - The array of holidays to be removed.      defaultHolidays (bool) : `bool` - Flag indicating whether to include the default holidays.   Returns: `map` - The map of holidays. Holidays(utc, addHolidaysArray, removeHolidaysArray, adjustForDLS, displayLabel, defaultHolidays)   Main function to build the holidays object, this is the only function from this library that should be needed. \ all functionality should be available through this function. \ With the exception of initializing a `HolidayMetaData` object to add a holiday or early close. \ \ **Default Holidays:** \ `DLS begin`, `DLS end`, `New Year's Day`, `MLK Jr. Day`, \ `Washington Day`, `Memorial Day`, `Independence Day`, `Labor Day`, \ `Columbus Day`, `Veterans Day`, `Thanksgiving Day`, `Christmas Day` \ \ **Example** ``` HolidayMetaData valentinesDay = HolidayMetaData.new(holiday="Valentine's Day", monthOfYear=2, dayOfMonth=14) HolidayMetaData stPatricksDay = HolidayMetaData.new(holiday="St. Patrick's Day", monthOfYear=3, dayOfMonth=17) HolidayMetaData addHolidaysArray = array.from(valentinesDay, stPatricksDay) string removeHolidaysArray = array.from("DLS begin", "DLS end") ܂Holidays = Holidays( ܂ utc=-6, ܂ addHolidaysArray=addHolidaysArray, ܂ removeHolidaysArray=removeHolidaysArray, ܂ adjustForDLS=true, ܂ displayLabel=true, ܂ defaultHolidays=true, ܂ ) plot(Holidays.newHoliday ? open : na, title="newHoliday", color=color.red, linewidth=4, style=plot.style_circles) ```   Parameters:      utc (int) : `int` - The UTC time shift in hours      addHolidaysArray (array) : `array` - The array of additional holidays to be added      removeHolidaysArray (array) : `array` - The array of holidays to be removed      adjustForDLS (bool) : `bool` - Flag indicating whether to adjust for daylight savings time (DLS)      displayLabel (bool) : `bool` - Flag indicating whether to display a label on the chart      defaultHolidays (bool) : `bool` - Flag indicating whether to include the default holidays   Returns: `HolidayObject` - The holidays object | Holidays = (holidaysMap: map, newHoliday: bool, holiday: string, dayString: string) HolidayMetaData   HolidayMetaData   Fields:      holiday (series string) : `string` - The name of the holiday.      dayOfYear (series int) : `int` - The day of the year of the holiday.      monthOfYear (series int) : `int` - The month of the year of the holiday.      dayOfMonth (series int) : `int` - The day of the month of the holiday.      weekOfMonth (series int) : `int` - The week of the month of the holiday.      dayOfWeek (series int) : `int` - The day of the week of the holiday.      lastOccurrenceInMonth (series bool)      closingTime (series int) : `int` - The closing time of the holiday.      utc (series int) : `int` - The UTC time shift in hours. HolidayObject   HolidayObject   Fields:      holidaysMap (map) : `map` - The map of holidays.      newHoliday (series bool) : `bool` - Flag indicating whether today is a new holiday.      activeHoliday (series bool) : `bool` - Flag indicating whether today is an active holiday.      holiday (series string) : `string` - The name of the holiday.      dayString (series string) : `string` - The day of the week of the holiday.
Libreria Pine Script®
di FFriZz
1
Volume ForecastThe Volume Forecast indicator on TradingView is a comprehensive tool designed to analyze historical price action and project future market movements based on the average sizes of candles. Incorporating various data points such as candle high/low, open/close, and real volumes, Volume Forecast provides traders with a holistic view of market dynamics, allowing for more informed decision-making. Key Features: Multi-Data Source Analysis: Volume Forecast seamlessly integrates multiple data sources, including candle high/low, open/close prices, and real volumes. By considering these diverse elements, the indicator offers a nuanced understanding of market conditions. Historical Candle Size Analysis: The indicator conducts a thorough analysis of historical candle sizes, capturing key data points to calculate the average candle size over a specified period. This historical context serves as the foundation for forecasting future candle sizes. Customizable Forecasting Parameters: Traders have the flexibility to fine-tune forecasting parameters to align with their trading strategies. Whether focusing on open/close relationships, high/low points, or real volumes, users can customize the indicator to suit their preferences. Predictive Algorithm: Volume Forecast employs a sophisticated predictive algorithm that leverages historical candle size data to project the potential size of upcoming candles. This algorithmic approach enhances the indicator's accuracy in forecasting market movements. Visual Clarity: The indicator provides a clear visual representation on the TradingView chart, displaying historical candle sizes and forecasted values. Color-coded elements and visual cues help traders quickly interpret the data, facilitating timely decision-making. Adaptive Real-Time Updates: Volume Forecast dynamically updates in real-time, ensuring traders have access to the latest information. This adaptability allows for swift adjustments to trading strategies in response to changing market conditions. Comprehensive Market Compatibility: Whether trading stocks, forex, cryptocurrencies, or commodities, Volume Forecast is compatible across various financial instruments and timeframes. This versatility makes it a valuable asset for traders in different markets. User-Friendly Interface: With an intuitive interface, Volume Forecast is accessible to traders of all experience levels. The indicator's user-friendly design streamlines the analysis process, making it easier for traders to incorporate it into their trading routines. In summary, Volume Forecast is a robust TradingView indicator that combines historical candle size analysis with advanced forecasting techniques. By incorporating multiple data sources and offering customization options, it empowers traders to make more informed decisions in anticipation of market movements. Whether used independently or in conjunction with other tools, Volume Forecast is a valuable asset for traders seeking a comprehensive understanding of market dynamics.
Indicatore Pine Script®
di claudiu-i
Grid by Volatility (Expo)█ Overview The Grid by Volatility is designed to provide a dynamic grid overlay on your price chart. This grid is calculated based on the volatility and adjusts in real-time as market conditions change. The indicator uses Standard Deviation to determine volatility and is useful for traders looking to understand price volatility patterns, determine potential support and resistance levels, or validate other trading signals. █ How It Works The indicator initiates its computations by assessing the market volatility through an established statistical model: the Standard Deviation. Following the volatility determination, the algorithm calculates a central equilibrium line—commonly referred to as the "mid-line"—on the chart to serve as a baseline for additional computations. Subsequently, upper and lower grid lines are algorithmically generated and plotted equidistantly from the central mid-line, with the distance being dictated by the previously calculated volatility metrics. █ How to Use Trend Analysis: The grid can be used to analyze the underlying trend of the asset. For example, if the price is above the Average Line and moves toward the Upper Range, it indicates a strong bullish trend. Support and Resistance: The grid lines can act as dynamic support and resistance levels. Price tends to bounce off these levels or breakthrough, providing potential trade opportunities. Volatility Gauge: The distance between the grid lines serves as a measure of market volatility. Wider lines indicate higher volatility, while narrower lines suggest low volatility. █ Settings Volatility Length: Number of bars to calculate the Standard Deviation (Default: 200) Squeeze Adjustment: Multiplier for the Standard Deviation (Default: 6) Grid Confirmation Length: Number of bars to calculate the weighted moving average for smoothing the grid lines (Default: 2) ----------------- Disclaimer The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs. My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Indicatore Pine Script®
di Zeiierman
8
Machine Learning: kNN (New Approach)Description: kNN is a very robust and simple method for data classification and prediction. It is very effective if the training data is large. However, it is distinguished by difficulty at determining its main parameter, K (a number of nearest neighbors), beforehand. The computation cost is also quite high because we need to compute distance of each instance to all training samples. Nevertheless, in algorithmic trading KNN is reported to perform on a par with such techniques as SVM and Random Forest. It is also widely used in the area of data science. The input data is just a long series of prices over time without any particular features. The value to be predicted is just the next bar's price. The way that this problem is solved for both nearest neighbor techniques and for some other types of prediction algorithms is to create training records by taking, for instance, 10 consecutive prices and using the first 9 as predictor values and the 10th as the prediction value. Doing this way, given 100 data points in your time series you could create 10 different training records. It's possible to create even more training records than 10 by creating a new record starting at every data point. For instance, you could take the first 10 data points and create a record. Then you could take the 10 consecutive data points starting at the second data point, the 10 consecutive data points starting at the third data point, etc. By default, shown are only 10 initial data points as predictor values and the 6th as the prediction value. Here is a step-by-step workthrough on how to compute K nearest neighbors (KNN) algorithm for quantitative data: 1. Determine parameter K = number of nearest neighbors. 2. Calculate the distance between the instance and all the training samples. As we are dealing with one-dimensional distance, we simply take absolute value from the instance to value of x (| x – v |). 3. Rank the distance and determine nearest neighbors based on the K'th minimum distance. 4. Gather the values of the nearest neighbors. 5. Use average of nearest neighbors as the prediction value of the instance. The original logic of the algorithm was slightly modified, and as a result at approx. N=17 the resulting curve nicely approximates that of the sma(20). See the description below. Beside the sma-like MA this algorithm also gives you a hint on the direction of the next bar move.
Indicatore Pine Script®
di capissimo
41
LinearRegressionLibraryLibrary "LinearRegressionLibrary" contains functions for fitting a regression line to the time series by means of different models, as well as functions for estimating the accuracy of the fit. Linear regression algorithms: RepeatedMedian(y, n, lastBar) applies repeated median regression (robust linear regression algorithm) to the input time series within the selected interval. Parameters: y :: float series, source time series (e.g. close) n :: integer, the length of the selected time interval lastBar :: integer, index of the last bar of the selected time interval (defines the position of the interval) Output: mSlope :: float, slope of the regression line mInter :: float, intercept of the regression line TheilSen(y, n, lastBar) applies the Theil-Sen estimator (robust linear regression algorithm) to the input time series within the selected interval. Parameters: y :: float series, source time series n :: integer, the length of the selected time interval lastBar :: integer, index of the last bar of the selected time interval (defines the position of the interval) Output: tsSlope :: float, slope of the regression line tsInter :: float, intercept of the regression line OrdinaryLeastSquares(y, n, lastBar) applies the ordinary least squares regression (non-robust) to the input time series within the selected interval. Parameters: y :: float series, source time series n :: integer, the length of the selected time interval lastBar :: integer, index of the last bar of the selected time interval (defines the position of the interval) Output: olsSlope :: float, slope of the regression line olsInter :: float, intercept of the regression line Model performance metrics: metricRMSE(y, n, lastBar, slope, intercept) returns the Root-Mean-Square Error (RMSE) of the regression. The better the model, the lower the RMSE. Parameters: y :: float series, source time series (e.g. close) n :: integer, the length of the selected time interval lastBar :: integer, index of the last bar of the selected time interval (defines the position of the interval) slope :: float, slope of the evaluated linear regression line intercept :: float, intercept of the evaluated linear regression line Output: rmse :: float, RMSE value metricMAE(y, n, lastBar, slope, intercept) returns the Mean Absolute Error (MAE) of the regression. MAE is is similar to RMSE but is less sensitive to outliers. The better the model, the lower the MAE. Parameters: y :: float series, source time series n :: integer, the length of the selected time interval lastBar :: integer, index of the last bar of the selected time interval (defines the position of the interval) slope :: float, slope of the evaluated linear regression line intercept :: float, intercept of the evaluated linear regression line Output: mae :: float, MAE value metricR2(y, n, lastBar, slope, intercept) returns the coefficient of determination (R squared) of the regression. The better the linear regression fits the data (compared to the sample mean), the closer the value of the R squared is to 1. Parameters: y :: float series, source time series n :: integer, the length of the selected time interval lastBar :: integer, index of the last bar of the selected time interval (defines the position of the interval) slope :: float, slope of the evaluated linear regression line intercept :: float, intercept of the evaluated linear regression line Output: Rsq :: float, R-sqared score Usage example: //@version=5 indicator('ExampleLinReg', overlay=true) // import the library import tbiktag/LinearRegressionLibrary/1 as linreg // define the studied interval: last 100 bars int Npoints = 100 int lastBar = bar_index int firstBar = bar_index - Npoints // apply repeated median regression to the closing price time series within the specified interval {square bracket}slope, intercept{square bracket} = linreg.RepeatedMedian(close, Npoints, lastBar) // calculate the root-mean-square error of the obtained linear fit rmse = linreg.metricRMSE(close, Npoints, lastBar, slope, intercept) // plot the line and print the RMSE value float y1 = intercept float y2 = intercept + slope * (Npoints - 1) if barstate.islast {indent} line.new(firstBar,y1, lastBar,y2) {indent} label.new(lastBar,y2,text='RMSE = '+str.format("{0,number,#.#}", rmse))
Libreria Pine Script®
di tbtkg
6
Pulu's 3 Moving Averages Pulu's 3 Moving Averages Release version 1, date 2021-09-28 This script allows you to customize three sets of moving averages, turn on/off, set color and parameters. It also tags the start date of the last set of moving average if there is. This, release version 1, supports eight moving average algorithms: ALMA, Arnaud Legoux Moving Average EMA, Exponential Moving Average RMA, Adjusted exponential moving average (aka Wilder’s EMA) SMA, Simple Moving Average SWMA, Symmetrically-Weighted Moving Average VWAP, Volume-Weighted Average Price VWMA, Volume-Weighted Moving Average WMA, Weighted Moving Average The availability and function parameters Func. Availability Parameters ALMA MA1, MA2, MA3 source length offset sigma EMA RMA SMA VWMA WMA MA1, MA2, MA3 source length SWMA VWAP MA1 source Parameters Parameter Description source the series of values to process. The default is to use the closing price to calculate the moving average. length an integer value that defines the number of bars to calculate the moving average on. The SWMA and VWAP do not use this parameter. ALMA offset a floating-point value that controls the tradeoff between smoothness (with a value closer to 1) and responsiveness (with a value closer to 0). This parameter is only used by ALMA. ALMA sigma a floating-point value that specifies the ALMA’s smoothness. The larger this value, the smoother the moving average is. This parameter is only used by ALMA. I'm not sure if it is needed, so I do not let the three Moving Averages of the script to have indivial algorithm setting. Because that will involve much complicated condition testing and use up more TradingView script lines limit. If you need to combine different algorithms in the three sets of moving averages, or have other ideas, leave a message to let me know; maybe I will try it in the next update. 我不確定是否需要，所以我沒有讓腳本的三組移動平均線有各別的算法設置。因為這將涉及更多複雜的條件測試，並使用更多 TradingView 腳本列數限制。如果您需要在三組均線中組合不同的算法，或者有其他想法，請留言告訴我；也許我會在下一次更新中嘗試。
Indicatore Pine Script®
di Pulu_
3
Cycles StrategyThis is back-testable strategy is a modified version of the Stochastic strategy. It is meant to accompany the modified Stochastic indicator: "Cycles". Modifications to the Stochastic strategy include; 1. Programmable settings for the Stochastic Periods (%K, %D and Smooth %K). 2. Programmable settings for the MACD Periods (Fast, Slow, Smoothing) 3. Programmable thresholds for %K, to qualify a potential entry strategy. 4. Programmable thresholds for %D, to qualify a potential exit strategy. 5. Buttons to choose which components to use in the trading algorithm. 6. Choose the month and year to back test. The trading algorithm: 1. When %K exceeds the upper/lower threshold and then hooks down/up, in the direction of the Moving Average (MA). This is the minimum entry qualification. 2. When %D exceeds the lower/upper threshold and angled in the direction of the trade, is the exit qualification. 3. Additional entry filters include the direction of MACD, Signal and %D. Also, the "cliff", being a long entry is a higher high or a short entry is a lower low. 4. Strategy can only go "Long" or "Short" depending on the selected setting. 5. By matching the settings in the "Cycles" indicator, you can (almost) see what the strategy is doing. 6. Be sure to select the "Recalculate" buttons, to recalculate on every new Tick, for best results. Please click the Like button and leave a comment if you appreciate this script. Improvements will be implemented as time goes on. I am not a licensed trade advisor. This strategy is for entertainment only. Use at your own risk!
Strategia Pine Script®
di FXEngineering
[blackcat] L3 Improved Dual Ehlers BPF for Volatility DetectionOVERVIEW This script implements an advanced L3 Improved Dual Ehlers Bandpass Filter (BPF) for volatility detection, combining both L1 and L2 calculation methods to create a comprehensive trading signal. The script leverages John Ehlers' sophisticated digital signal processing techniques to identify market cycles and extract meaningful trading signals from price action. By combining multiple cycle detection methods and filtering approaches, it provides traders with a powerful tool for identifying trend changes, momentum shifts, and potential reversal points across various market conditions and timeframes. The L3 approach uniquely combines the outputs of both L1 (01 range) and L2 (-11 range) methods, creating a signal that ranges from -1~2 and provides enhanced sensitivity to market dynamics. FEATURES 🔄 Dual Calculation Methods: Choose between L1 (01 range), L2 (-11 range), or combine both for L3 signal (-1~2 range) to match your trading style 📊 Multiple Cycle Detection: Seven different dominant cycle calculation methods including HoDyDC (Hilbert Transform Dominant Cycle), PhAcDC (Phase Accumulation Dominant Cycle), DuDiDC (Duane Dominant Cycle), CycPer (Cycle Period), BPZC (Bandpass Zero Crossing), AutoPer (Autocorrelation Period), and DFTDC (Discrete Fourier Transform Dominant Cycle) 🎛️ Flexible Mixing Options: Six sophisticated mixing methods including weighted averaging, simple sum, difference extraction, dominant-only, subdominant-only, and adaptive mixing that adjusts based on signal strength 🌊 Bandpass Filtering: Precise bandwidth control for both dominant and subdominant filters, allowing fine-tuning of frequency response characteristics 📈 Advanced Divergence Detection: Robust algorithm for identifying bullish and bearish divergences with customizable lookback periods and range constraints 🎨 Comprehensive Visualization: Extensive customization options for all signals, colors, plot styles, and display elements 🔔 Comprehensive Alert System: Built-in alerts for divergence signals, zero line crosses, and various market conditions 📊 Real-time Cycle Information: Optional display of dominant and subdominant cycle periods for educational purposes 🔄 Adaptive Signal Processing: Dynamic adjustment of parameters based on market conditions and volatility 🎯 Multiple Signal Outputs: Simultaneous generation of L1, L2, and L3 signals for different trading strategies HOW TO USE Select Calculation Method: Choose between "l1" (01 range), "l2" (-11 range), or "both" (L3, -1~2 range) in the Calculation Method settings based on your preferred signal characteristics Configure Cycle Detection: Select your preferred Dominant Cycle Method from the seven available options and adjust the Cycle Part parameter (0.1-0.9) to fine-tune cycle sensitivity Set Subdominant Parameters: Configure the subdominant cycle either as a ratio of the dominant cycle or as a fixed period, depending on your analysis approach Adjust Filter Bandwidth: Fine-tune the bandwidth settings for both dominant and subdominant filters (0.1-1.0) to control the frequency response and signal smoothing Choose Mixing Method: Select how to combine the filters - weighted averaging for balance, sum for maximum sensitivity, difference for trend isolation, or adaptive mixing for dynamic response Configure Smoothing: Select from SMA, EMA, or HMA smoothing methods with adjustable length (1-20 bars) to reduce noise in the final signal Customize Visualization: Enable/disable individual plots, divergence detection, zero line, fill areas, and customize all colors to match your chart preferences Set Divergence Parameters: Configure lookback ranges (5-60 bars) for divergence detection to match your trading timeframe and style Monitor Signals: Watch for crosses above/below zero line and divergence patterns, paying attention to signal strength and consistency Set Up Alerts: Configure alerts for divergence signals, zero line crosses, and other market conditions to stay informed of trading opportunities LIMITATIONS The script requires the dc_ta library from blackcat1402 for several advanced cycle calculation methods (HoDyDC, PhAcDC, DuDiDC, CycPer, BPZC, AutoPer, DFTDC) L1 method operates in 01 range while L2 method uses -11 range, requiring different interpretation approaches Combined L3 signal ranges from -1~2 when both methods are selected, creating unique signal characteristics that traders must adapt to Divergence detection accuracy depends on proper lookback period settings and market volatility conditions Performance may be impacted with very long lookback ranges (>60 bars) or when multiple plots are simultaneously enabled The script is designed for non-overlay use and may not display correctly on certain chart types or with conflicting indicators Adaptive mixing method requires careful threshold tuning to avoid excessive signal fluctuation Cycle detection algorithms may produce unreliable results during low volatility or highly choppy market conditions The script assumes regular price data and may not perform optimally with irregular or gapped price sequences NOTES The script implements advanced mathematical calculations including bandpass filters, Hilbert transforms, and various cycle detection algorithms developed by John Ehlers For optimal results, experiment with different cycle detection methods and bandwidth settings across various market conditions and timeframes The adaptive mixing method automatically adjusts weights based on signal strength, providing dynamic response to changing market conditions Divergence detection works best when the "Plot Divergence" option is enabled and when combined with other technical analysis tools Zero line crosses can indicate potential trend changes or momentum shifts, especially when confirmed by volume or other indicators The script includes commented code for cycle information display that can be enabled if you want to monitor cycle periods in real-time Different calculation methods may perform better in different market environments - L1 tends to be smoother while L2 is more sensitive The subdominant cycle helps filter out noise and provides additional confirmation for signals generated by the dominant cycle Bandwidth settings control the filter's frequency response - lower values provide more smoothing while higher values increase sensitivity Mixing methods offer different approaches to combining signals - weighted averaging is generally most reliable for most trading applications THANKS Special thanks to John Ehlers for his pioneering work in cycle analysis and digital signal processing for financial markets. This script implements and significantly improves upon his bandpass filter methodology, incorporating multiple advanced techniques from his extensive body of work. Also heartfelt thanks to blackcat1402 for the dc_ta library that provides essential cycle calculation methods and for maintaining such a valuable resource for the Pine Script community. Additional appreciation to the TradingView platform for providing the tools and environment that make sophisticated technical analysis accessible to traders worldwide. This script represents a collaborative effort in advancing the field of algorithmic trading and technical analysis.
Indicatore Pine Script®
di blackcat1402