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Noa: Z-distance from VWAP with Kalman Smoother Title: Noa: Z-distance from VWAP with Kalman Smoother Description: The "Z-distance from VWAP with Kalman Smoother" is a tool constructed on the premise that price evolves in distinct stages: normal or extreme trends (upward or downward) and transitional periods, termed as 'flips'. The Volume Weighted Average Price (VWAP) serves as a benchmark, representing the market's expectation of a fair value over a given time frame. However, since each stock trades on its unique price scale, direct comparisons are not feasible. This script introduces a standardized method, using the Z-score from the VWAP, to understand and compare these relationships across diverse scales. Core Principles: Stages of Price Movement: - Prices don't move purely randomly; while they contain a random element, they oscillate in discernible patterns or stages—either maintaining a trend (normal or extreme) or undergoing transition (flip). - VWAP as Fair Value: VWAP offers a dynamic representation of what the market perceives as fair value for a stock over a specific period. - Standardizing Price Relations: Given the varied scales at which different stocks trade, a model was imperative to standardize these relations. The Z-score from the VWAP fulfills this role, offering a normalized measure of how far the price deviates from its perceived fair value. Features: Z-score Levels: The indicator demarcates various stages of price movements, offering clarity on potential overbought or oversold conditions. - Extreme Up Trend: Indicated when the Z-score surpasses the upper limit. - Normal Up Trend: Represented when the Z-score lies between the flip upper and the upper limit. - Transition (Flip): Recognized when the Z-score oscillates within the flip range. - Normal Down Trend: Denoted when the Z-score is between the flip lower and the lower limit. - Extreme Down Trend: Marked when the Z-score falls below the lower limit. Visual Aids: - Color-coded regions between specific Z-score levels and the Z-score plot itself elucidate the current market state. - Kalman Filter: By incorporating a Kalman filter, the indicator offers a less noisy and smoother representation of the Z-score, enhancing its interpretability. Usage: Trend Analysis: - The Z-score states and the color-coded plot facilitate a nuanced understanding of the prevailing market trend. - Potential Reversal Points: Extremely positive or negative Z-scores might hint at impending reversals. - Buy/Sell Signals: Z-score's interactions with the flip level can be interpreted as potential trading signals. Example (for illustration purposes only): AAPL since April 2022: The stock exited from a normal uptrend and transitioned potentially towards a downtrend. By the end of April, AAPL flipped twice before transitioning to a normal downtrend. By early May, the stock moved into an aggressive downtrend. Market buyers were able to counter this downtrend by June, but selling pressure persisted, pushing the stock back into an aggressive downtrend. By the end of June, buyers halted the aggressive selling and transitioned the stock from an aggressive to normal downtrend, then to a flip, and finally to a normal uptrend by the end of August. AAPL briefly peaked into an aggressive uptrend before being pressured back to a normal downtrend. The rest of 2022 saw AAPL attempting several short-lived uptrend flips. However, 2023 brought a change, with AAPL flipping into a normal uptrend by the end of January, maintaining it until August of that year. Credits: This script, inspired by Z distance from VWAP by LazyBear and Kalman Smoother by alexgrover, was revamped and enriched by nord-ouestadvisors to embed these core principles and heighten its usability. A special acknowledgment to ChatGPT by OpenAI for the guidance.
Pine Script® göstergesi
nord-ouestadvisors tarafından
3
[Excalibur] Ehlers AutoCorrelation Periodogram ModifiedKeep your coins folks, I don't need them, don't want them. If you wish be generous, I do hope that charitable peoples worldwide with surplus food stocks may consider stocking local food banks before stuffing monetary bank vaults, for the crusade of remedying the needs of less than fortunate children, parents, elderly, homeless veterans, and everyone else who deserves nutritional sustenance for the soul. DEDICATION: This script is dedicated to the memory of Nikolai Dmitriyevich Kondratiev (Никола́й Дми́триевич Кондра́тьев) as tribute for being a pioneering economist and statistician, paving the way for modern econometrics by advocation of rigorous and empirical methodologies. One of his most substantial contributions to the study of business cycle theory include a revolutionary hypothesis recognizing the existence of dynamic cycle-like phenomenon inherent to economies that are characterized by distinct phases of expansion, stagnation, recession and recovery, what we now know as "Kondratiev Waves" (K-waves). Kondratiev was one of the first economists to recognize the vital significance of applying quantitative analysis on empirical data to evaluate economic dynamics by means of statistical methods. His understanding was that conceptual models alone were insufficient to adequately interpret real-world economic conditions, and that sophisticated analysis was necessary to better comprehend the nature of trending/cycling economic behaviors. Additionally, he recognized prosperous economic cycles were predominantly driven by a combination of technological innovations and infrastructure investments that resulted in profound implications for economic growth and development. I will mention this... nation's economies MUST be supported and defended to continuously evolve incrementally in order to flourish in perpetuity OR suffer through eras with lasting ramifications of societal stagnation and implosion. Analogous to the realm of economics, aperiodic cycles/frequencies, both enduring and ephemeral, do exist in all facets of life, every second of every day. To name a few that any blind man can naturally see are: heartbeat (cardiac cycles), respiration rates, circadian rhythms of sleep, powerful magnetic solar cycles, seasonal cycles, lunar cycles, weather patterns, vegetative growth cycles, and ocean waves. Do not pretend for one second that these basic aforementioned examples do not affect business cycle fluctuations in minuscule and monumental ways hour to hour, day to day, season to season, year to year, and decade to decade in every nation on the planet. Kondratiev's original seminal theories in macroeconomics from nearly a century ago have proven remarkably prescient with many of his antiquated elementary observations/notions/hypotheses in macroeconomics being scholastically studied and topically researched further. Therefore, I am compelled to honor and recognize his statistical insight and foresight. If only.. Kondratiev could hold a pocket sized computer in the cup of both hands bearing the TradingView logo and platform services, I truly believe he would be amazed in marvelous delight with a GARGANTUAN smile on his face. INTRODUCTION: Firstly, this is NOT technically speaking an indicator like most others. I would describe it as an advanced cycle period detector to obtain market data spectral estimates with low latency and moderate frequency resolution. Developers can take advantage of this detector by creating scripts that utilize a "Dominant Cycle Source" input to adaptively govern algorithms. Be forewarned, I would only recommend this for advanced developers, not novice code dabbling. Although, there is some Pine wizardry introduced here for novice Pine enthusiasts to witness and learn from. AI did describe the code into one super-crunched sentence as, "a rare feat of exceptionally formatted code masterfully balancing visual clarity, precision, and complexity to provide immense educational value for both programming newcomers and expert Pine coders alike." Understand all of the above aforementioned? Buckle up and proceed for a lengthy read of verbose complexity... This is my enhanced and heavily modified version of autocorrelation periodogram (ACP) for Pine Script v5.0. It was originally devised by the mathemagician John Ehlers for detecting dominant cycles (frequencies) in an asset's price action. I have been sitting on code similar to this for a long time, but I decided to unleash the advanced code with my fashion. Originally Ehlers released this with multiple versions, one in a 2016 TASC article and the other in his last published 2013 book "Cycle Analytics for Traders", chapter 8. He wasn't joking about "concepts of advanced technical trading" and ACP is nowhere near to his most intimidating and ingenious calculations in code. I will say the book goes into many finer details about the original periodogram, so if you wish to delve into even more elaborate info regarding Ehlers' original ACP form AND how you may adapt algorithms, you'll have to obtain one. Note to reader, comparing Ehlers' original code to my chimeric code embracing the "Power of Pine", you will notice they have little resemblance. What you see is a new species of autocorrelation periodogram combining Ehlers' innovation with my fascinations of what ACP could be in a Pine package. One other intention of this script's code is to pay homage to Ehlers' lifelong works. Like Kondratiev, Ehlers is also a hardcore cycle enthusiast. I intend to carry on the fire Ehlers envisioned and I believe that is literally displayed here as a pleasant "fiery" example endowed with Pine. With that said, I tried to make the code as computationally efficient as possible, without going into dozens of more crazy lines of code to speed things up even more. There's also a few creative modifications I made by making alterations to the originating formulas that I felt were improvements, one of them being lag reduction. By recently questioning every single thing I thought I knew about ACP, combined with the accumulation of my current knowledge base, this is the innovative revision I came up with. I could have improved it more but decided not to mind thrash too many TV members, maybe later... I am now confident Pine should have adequate overhead left over to attach various indicators to the dominant cycle via input.source(). TV, I apologize in advance if in the future a server cluster combusts into a raging inferno... Coders, be fully prepared to build entire algorithms from pure raw code, because not all of the built-in Pine functions fully support dynamic periods (e.g. length=ANYTHING). Many of them do, as this was requested and granted a while ago, but some functions are just inherently finicky due to implementation combinations and MUST be emulated via raw code. I would imagine some comprehensive library or numerous authored scripts have portions of raw code for Pine built-ins some where on TV if you look diligently enough. 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. While I was refactoring my life (forgoing many other "important" endeavors) in the early half of 2023, I primarily focused on this code over and over in my surplus time. During that same time I was working on other innovations that are far above and beyond what this code is. I hope you understand. The best way programmatically may be to incorporate this code into your private Pine project directly, after brutal testing of course, but that may be too challenging for many in early development. Being able to see the periodogram is also beneficial, so input sourcing may be the "better" avenue to tether portions of the dominant cycle to algorithms. Unique indication being able to utilize the dominantCycle may be advantageous when tethering this script to those algorithms. The easiest way is to manually set your indicators to what ACP recognizes as the dominant cycle, but that's actually not considered dynamic real time adaption of an indicator. Different indicators may need a proportion of the dominantCycle, say half it's value, while others may need the full value of it. That's up to you to figure that out in practice. Sourcing one or more custom indicators dynamically to one detector's dominantCycle may require code like this: `int sourceDC = int(math.max(6, math.min(49, input.source(close, "Dominant Cycle Source"))))`. Keep in mind, some algos can use a float, while algos with a for loop require an integer. I have witnessed a few attempts by talented TV members for a Pine based autocorrelation periodogram, but not in this caliber. Trust me, coding ACP is no ordinary task to accomplish in Pine and modifying it blessed with applicable improvements is even more challenging. For over 4 years, I have been slowly improving this code here and there randomly. It is beautiful just like a real flame, but... this one can still burn you! My mind was fried to charcoal black a few times wrestling with it in the distant past. My very first attempt at translating ACP was a month long endeavor because PSv3 simply didn't have arrays back then. Anyways, this is ACP with a newer engine, I hope you enjoy it. Any TV subscriber can utilize this code as they please. If you are capable of sufficiently using it properly, please use it wisely with intended good will. That is all I beg of you. Lastly, you now see how I have rasterized my Pine with Ehlers' swami-like tech. Yep, this whole time I have been using hline() since PSv3, not plot(). Evidently, plot() still has a deficiency limited to only 32 plots when it comes to creating intense eye candy indicators, the last I checked. The use of hline() is the optimal choice for rasterizing Ehlers styled heatmaps. This does only contain two color schemes of the many I have formerly created, but that's all that is essentially needed for this gizmo. Anything else is generally for a spectacle or seeing how brutal Pine can be color treated. The real hurdle is being able to manipulate colors dynamically with Merlin like capabilities from multiple algo results. That's the true challenging part of these heatmap contraptions to obtain multi-colored "predator vision" level indication. You now have basic hline() food for thought empowerment to wield as you can imaginatively dream in Pine projects. PERIODOGRAM UTILITY IN REAL WORLD SCENARIOS: This code is a testament to the abilities that have yet to be fully realized with indication advancements. Periodograms, spectrograms, and heatmaps are a powerful tool with real-world applications in various fields such as financial markets, electrical engineering, astronomy, seismology, and neuro/medical applications. For instance, among these diverse fields, it may help traders and investors identify market cycles/periodicities in financial markets, support engineers in optimizing electrical or acoustic systems, aid astronomers in understanding celestial object attributes, assist seismologists with predicting earthquake risks, help medical researchers with neurological disorder identification, and detection of asymptomatic cardiovascular clotting in the vaxxed via full body thermography. In either field of study, technologies in likeness to periodograms may very well provide us with a better sliver of analysis beyond what was ever formerly invented. Periodograms can identify dominant cycles and frequency components in data, which may provide valuable insights and possibly provide better-informed decisions. By utilizing periodograms within aspects of market analytics, individuals and organizations can potentially refrain from making blinded decisions and leverage data-driven insights instead. PERIODOGRAM INTERPRETATION: The periodogram renders the power spectrum of a signal, with the y-axis representing the periodicity (frequencies/wavelengths) and the x-axis representing time. The y-axis is divided into periods, with each elevation representing a period. In this periodogram, the y-axis ranges from 6 at the very bottom to 49 at the top, with intermediate values in between, all indicating the power of the corresponding frequency component by color. The higher the position occurs on the y-axis, the longer the period or lower the frequency. The x-axis of the periodogram represents time and is divided into equal intervals, with each vertical column on the axis corresponding to the time interval when the signal was measured. The most recent values/colors are on the right side. The intensity of the colors on the periodogram indicate the power level of the corresponding frequency or period. The fire color scheme is distinctly like the heat intensity from any casual flame witnessed in a small fire from a lighter, match, or camp fire. The most intense power would be indicated by the brightest of yellow, while the lowest power would be indicated by the darkest shade of red or just black. By analyzing the pattern of colors across different periods, one may gain insights into the dominant frequency components of the signal and visually identify recurring cycles/patterns of periodicity. SETTINGS CONFIGURATIONS BRIEFLY EXPLAINED: Source Options: These settings allow you to choose the data source for the analysis. Using the `Source` selection, you may tether to additional data streams (e.g. close, hlcc4, hl2), which also may include samples from any other indicator. For example, this could be my "Chirped Sine Wave Generator" script found in my member profile. By using the `SineWave` selection, you may analyze a theoretical sinusoidal wave with a user-defined period, something already incorporated into the code. The `SineWave` will be displayed over top of the periodogram. Roofing Filter Options: These inputs control the range of the passband for ACP to analyze. Ehlers had two versions of his highpass filters for his releases, so I included an option for you to see the obvious difference when performing a comparison of both. You may choose between 1st and 2nd order high-pass filters. Spectral Controls: These settings control the core functionality of the spectral analysis results. You can adjust the autocorrelation lag, adjust the level of smoothing for Fourier coefficients, and control the contrast/behavior of the heatmap displaying the power spectra. I provided two color schemes by checking or unchecking a checkbox. Dominant Cycle Options: These settings allow you to customize the various types of dominant cycle values. You can choose between floating-point and integer values, and select the rounding method used to derive the final dominantCycle values. Also, you may control the level of smoothing applied to the dominant cycle values. DOMINANT CYCLE VALUE SELECTIONS: External to the acs() function, the code takes a dominant cycle value returned from acs() and changes its numeric form based on a specified type and form chosen within the indicator settings. The dominant cycle value can be represented as an integer or a decimal number, depending on the attached algorithm's requirements. For example, FIR filters will require an integer while many IIR filters can use a float. The float forms can be either rounded, smoothed, or floored. If the resulting value is desired to be an integer, it can be rounded up/down or just be in an integer form, depending on how your algorithm may utilize it. AUTOCORRELATION SPECTRUM FUNCTION BASICALLY EXPLAINED: In the beginning of the acs() code, the population of caches for precalculated angular frequency factors and smoothing coefficients occur. By precalculating these factors/coefs only once and then storing them in an array, the indicator can save time and computational resources when performing subsequent calculations that require them later. In the following code block, the "Calculate AutoCorrelations" is calculated for each period within the passband width. The calculation involves numerous summations of values extracted from the roofing filter. Finally, a correlation values array is populated with the resulting values, which are normalized correlation coefficients. Moving on to the next block of code, labeled "Decompose Fourier Components", Fourier decomposition is performed on the autocorrelation coefficients. It iterates this time through the applicable period range of 6 to 49, calculating the real and imaginary parts of the Fourier components. Frequencies 6 to 49 are the primary focus of interest for this periodogram. Using the precalculated angular frequency factors, the resulting real and imaginary parts are then utilized to calculate the spectral Fourier components, which are stored in an array for later use. The next section of code smooths the noise ridden Fourier components between the periods of 6 and 49 with a selected filter. This species also employs numerous SuperSmoothers to condition noisy Fourier components. One of the big differences is Ehlers' versions used basic EMAs in this section of code. I decided to add SuperSmoothers. The final sections of the acs() code determines the peak power component for normalization and then computes the dominant cycle period from the smoothed Fourier components. It first identifies a single spectral component with the highest power value and then assigns it as the peak power. Next, it normalizes the spectral components using the peak power value as a denominator. It then calculates the average dominant cycle period from the normalized spectral components using Ehlers' "Center of Gravity" calculation. Finally, the function returns the dominant cycle period along with the normalized spectral components for later external use to plot the periodogram. POST SCRIPT: Concluding, I have to acknowledge a newly found analyst for assistance that I couldn't receive from anywhere else. For one, Claude doesn't know much about Pine, is unfortunately color blind, and can't even see the Pine reference, but it was able to intuitively shred my code with laser precise realizations. Not only that, formulating and reformulating my description needed crucial finesse applied to it, and I couldn't have provided what you have read here without that artificial insight. Finding the right order of words to convey the complexity of ACP and the elaborate accompanying content was a daunting task. No code in my life has ever absorbed so much time and hard fricking work, than what you witness here, an ACP gem cut pristinely. I'm unveiling my version of ACP for an empowering cause, in the hopes a future global army of code wielders will tether it to highly functional computational contraptions they might possess. Here is ACP fully blessed poetically with the "Power of Pine" in sublime code. ENJOY!
Pine Script® göstergesi
ImmortalFreedom tarafından
36
Dynamic GANN Square Of 9 BandsDynamic GANN Square Of 9 Bands Created on 3 Sept 2023 Adjust Increment Value: Customize increment to match symbol and price characteristics for accuracy. Green Line: 200 EMA. Identifies trend direction; moves with the prevailing trend. Red Lines: Mark prominent reversal levels closer to the red range; ideal for mean reversion strategies. Crossing red levels may indicate trend continuation to the next red level. Grey Lines: Show immediate target reversal levels; watch for potential reversals. Key Features: Levels are different from Standard Deviation Lines. Levels remain fixed and parallel, unaffected by volatility. Despite its dynamism, it can serve as a leading indicator, revealing potential trend changes. Primarily designed for trend-following strategies. Additional Tips: Use additional confirmations Manage predefined risk and quantity Additional Resources: GANN Square Of 9 Pivots:
Pine Script® göstergesi
Arun_K_Bhaskar tarafından
9
Velocity Acceleration Indicator [CC]The Velocity Acceleration Indicator was created by Scott Cong (Stocks and Commodities Sep 2023, pgs 8-15). This is another personal variation of his formula designed to capture the overall velocity acceleration of the underlying stock by applying the velocity formula to the original indicator to find the acceleration of the underlying velocity. I changed a few things around and managed actually to get less lag and quicker signals for this version, so make sure you compare the Velocity Indicator script that I published yesterday. This indicator is also visually similar to a typical stochastic indicator but uses a different underlying calculation. This works well as a momentum indicator, and the values are completely unbounded, so the best ways to determine bullish or bearish trends is either by using a crossover or crossunder between the indicator and the midline or to buy or sell the indicator when it reaches a high or low point and starts to fall or rise respectively. I used the zero line for my default version to help determine the bullish or bearish trends. I have also included multiple colors to differentiate between very strong signals and normal signals, so very strong signals are darker in color, and normal signals use lighter colors. Buy when the line turns green and sell when it turns red. Let me know if there are any other indicators or scripts you would like to see me publish! I will have some more new scripts in the next week or so.
Pine Script® göstergesi
cheatcountry tarafından
2
Velocity Indicator [CC]The Velocity Indicator was created by Scott Cong (Stocks and Commodities Sep 2023, pgs 8-15). This is my variation of his formula designed to capture the overall velocity of the underlying stock by applying the typical velocity formula. This indicator is visually similar to a typical stochastic indicator but uses a different underlying calculation. This works well as a momentum indicator, and the values are completely unbounded, so the best ways to determine bullish or bearish trends is either by using a crossover or crossunder between the indicator and the midline or to buy or sell the indicator when it reaches a high or low point and starts to fall or rise respectively. For my default version, I used the zero line to help determine the bullish or bearish trends. I have also included multiple colors to differentiate between very strong signals and normal signals, so very strong signals are darker in color, and normal signals use lighter colors. Buy when the line turns green and sell when it turns red. Let me know if there are any other indicators or scripts you would like to see me publish! I will have some more new scripts in the next week or so.
Pine Script® göstergesi
cheatcountry tarafından
7
Volume ValueWhen VelocityTitle: Volume ValueWhen Velocity Trading Strategy ▶ Introduction: The " Volume ValueWhen Velocity " trading strategy is designed to generate long position signals based on various technical conditions, including volume thresholds, RSI (Relative Strength Index), and price action relative to the Simple Moving Average (SMA). The strategy aims to identify potential buy opportunities when specific criteria are met, helping traders capitalize on potential bullish movements. ▶ How to use and conditions ★ Important : Only on Spot Binance BINANCE:BTCUSDT Name: Volume ValueWhen Velocity Operating mode: Long on Spot BINANCE BINANCE:BTCUSDT Timeframe: Only one hour Market: Crypto currency: Bitcoin only Signal type: Medium or short term Entry: All sections in the Technical Indicators and Conditions section must be saved to enter (This is explained below) Exit: Based on loss limit and profit limit It is removed in the settings section Backtesting: ⁃ Exchange: BINANCE BINANCE:BTCUSDT ⁃ Pair: BTCUSDT ⁃ Timeframe:1h ⁃ Fee: 0.1% - Initial Capital: 1,000 USDT - Position sizing: 500 usdt -Trading Range: 2022-07-01 11:30 ___ 2023-07-21 14:30 ▶ Strategy Settings and Parameters: 1. `strategy(title='Volume ValueWhen Velocity', ...`: Sets the strategy title, initial capital, default quantity type, default quantity value, commission value, and trading currency. ↬ Stop-Loss and Take-Profit Settings: 1. long_stoploss_value and long_stoploss_percentage : Define the stop-loss percentage for long positions. 2. long_takeprofit_value and long_takeprofit_percentage : Define the take-profit percentage for long positions. ↬ ValueWhen Occurrence Parameters: 1. occurrence_ValueWhen_1 and occurrence_ValueWhen_2 : Control the occurrences of value events. 2. `distance_value`: Specifies the minimum distance between occurrences of ValueWhen 1 and ValueWhen 2. ↬ RSI Settings: 1. rsi_over_sold and rsi_length : Define the oversold level and RSI length for RSI calculations. ↬ Volume Thresholds: 1. volume_threshold1 , volume_threshold2 , and volume_threshold3 : Set the volume thresholds for multiple volume conditions. ↬ ATR (Average True Range) Settings: 1. atr_small and atr_big : Specify the periods used to calculate the Average True Range. ▶ Date Range for Back-Testing: 1. start_date, end_date, start_month, end_month, start_year, and end_year : Define the date range for back-testing the strategy. ▶ Technical Indicators and Conditions: 1. rsi: Calculates the Relative Strength Index (RSI) based on the defined RSI length and the closing prices. 2. was_over_sold: Checks if the RSI was oversold in the last 10 bars. 3. getVolume and getVolume2 : Custom functions to retrieve volume data for specific bars. 4. firstCandleColor : Evaluates the color of the first candle based on different timeframes. 5. sma : Calculates the Simple Moving Average (SMA) of the closing price over 13 periods. 6. numCandles : Counts the number of candles since the close price crossed above the SMA. 7. atr1 : Checks if the ATR_small is less than ATR_big for the specified security and timeframe. 8. prevClose, prevCloseBarsAgo, and prevCloseChange : ValueWhen functions to calculate the change in the close price between specific occurrences. 9. atrval: A condition based on the ATR_value3. ▶ Buy Signal Condition: Condition: A combination of multiple volume conditions. buy_signal: The final buy signal condition that considers various technical conditions and their interactions. ▶ Long Strategy Execution: 1. The strategy will enter a long position (buy) when the buy_signal condition is met and within the specified date range. 2. A stop-loss and take-profit will be set for the long position to manage risk and potential profits. ▶ Conclusion: The " Volume ValueWhen Velocity " trading strategy is designed to identify long position opportunities based on a combination of volume conditions, RSI, and price action. The strategy aims to capitalize on potential bullish movements and utilizes a stop-loss and take-profit mechanism to manage risk and optimize potential returns. Traders can use this strategy as a starting point for their own trading systems or further customize it to suit their preferences and risk appetite. It is crucial to thoroughly back-test and validate any trading strategy before deploying it in live markets. ↯ Disclaimer: Risk Management is crucial, so adjust stop loss to your comfort level. A tight stop loss can help minimise potential losses. Use at your own risk. How you or we can improve? Source code is open so share your ideas! Leave a comment and smash the boost button!
Pine Script® stratejisi
Kimply_Tr tarafından
6
Visible Range Streaks of Unbroken Prior Highs/Lows [vnhilton](OVERVIEW) This indicator keeps track of the number of unbroken prior highs/lows (unbroken being no price closes above/below the prior high/low). Great for entries and take profits. The indicator calculates on visible bars for convenience when looking back into the history without having to use bar replay, which those on the TradingView free plan cannot use intraday bar replay, so the visible range is a perfect work-around. The 2 minute chart above shows NASDAQ:NKLA on Thursday 13th July, 2023, with a significant level of $1.5 leading to a breakout. Streaks lower than 10 were hidden in the chart. (FEATURES) - Custom minimum streak size to start displaying plots (the smaller the size the more signals) - Ability to show/hide numbers (that keep count of unbroken streaks), text signals (for when a streak is broken), break shapes (where the prior high/low was broken), and Zig Zag (lines between break shapes) - Customisable Zig Zag line width, style, and colours (1 colour for a positive gradient line, and another for a negative gradient line) - Customisable text signal text - Customisable numbers, text signal, break shape, number label & text signal label colours - Customisable number label, text signal label and break shape styles and sizes (number and text signal label share the same size)
Pine Script® göstergesi
vnhilton tarafından
1
Opening Range Gap + Std Dev [starclique]The ICT Opening Range Gap is a concept taught by Inner Circle Trader and is discussed in the videos: 'One Trading Setup For Life' and 2023 ICT Mentorship - Opening Range Gap Repricing Macro ORGs, or Opening Range Gaps, are gaps that form only on the Regular Trading Hours chart. The Regular Trading Hours gap occurs between 16:15 PM - 9:29 AM EST (UTC-4) These times are considered overnight trading, so it is useful to filter the PA (price action) formed there. The RTH option is only available for futures contracts and continuous futures from CME Group. To change your chart to RTH, first things first, make sure you’re looking at a futures contract for an asset class, then on the bottom right of your chart, you’ll see ETH (by default) - Click on that, and change it to RTH. Now your charts are filtering the price action that happened overnight. To draw out your gap, use the Close of the 4:14 PM candle and the open of the 9:30 AM candle. How is this concept useful? Well, It can be used in many ways. --- How To Use The ORG One of the ways you can use the opening range gap is simply as support and resistance If we extend out the ORG from the example above, we can see that there is a clean retest of the opening range gap high after breaking structure to the upside and showing acceptance outside of the gap after consolidating within it. The ORG High (4:14 Candle Close in this case) was used as support. We then see an expansion to the upside. Another way to implement the ORG is by using it as a draw on liquidity (magnet for price) In this example, if we looked to the left, there was a huge ORG to the downside, leaving a massive gap. The market will want to rebalance that gap during the regular trading hours. The market rallies higher, rejects, comes down to clear the current days ORG low, then closes. That is one example of how you can combine liquidity & ICT market structure concepts with Opening Range Gaps to create a story in the charts. Now let’s discuss standard deviations. --- Standard Deviations Standard Deviations are essentially projection levels for ranges / POIs (Point of Interests) By this I mean, if you have a range, and you would like to see where it could potentially expand to, you’d place your fibonacci retracement tool on and high and low of the range, then use extension levels to find specific price points where price might reject from. Since 0 and 1 are your Range High and Low respectively, your projection levels would be something like 1.5, 2, 2.5, and 3, for the extension from your 1 Fib Level, and -0.5, -1, -1.5, and -2 for your 0 Fib level. The -1 and 2 level produce a 1:1 projection of your range low and high, meaning, if you expect price to expand as much as it did from the range low to range high, then you can project a -1 and 2 on your Fib, and it would show you what ICT calls “symmetrical price” Now, how are standard deviations relevant here? Well, if you’ve been paying attention to ICT’s recent videos, you would’ve caught that he’s recently started using Standard Deviation levels on breakers. So my brain got going while watching his video on ORGs, and I decided to place the fib on the ORG high and low and see what it’d produce. The results were very interesting. Using this same example, if we place our fib on the ORG High and Low, and add some projection levels, we can see that we rejected right at the -2 Standard Deviation Level. --- You can see that I also marked out the EQ (Equilibrium, 50%, 0.5 of Fib) of the ORG. This is because we can use this level as a take profit level if we’re using an old ORG as our draw. In days like these, where the gap formed was within a consolidation, and it continued to consolidate within the ORG zone that we extended, we can use the EQ in the same way we’d use an EQ for a range. If it’s showing acceptance above the EQ, we are bullish, and expect the high of the ORG to be tapped, and vice versa. --- Using The Indicator Here’s where our indicator comes in play. To avoid having to do all this work of zooming in and marking out the close and open of the respective ORG candles, we created the Opening Range Gap + Standard Deviations Indicator, with the help of our dedicated Star Clique coder, a1tmaniac. With the ORG + STD DEV indicator, you will be able to view ORG’s and their projections on the ETH (Electronic Trading Hours) chart. --- Features Range Box - Change the color of your Opening Range Gap to your liking - Enable or disable the box from appearing using the checkbox Range Midline - Change the color of your Opening Range Gap Equilibrium - Enable or disable the midline from appearing using the checkbox Std. Dev - Add whichever standard deviation levels you’d like. - By default, the indicator comes with 0.5, 1, 1.5, and 2 standard deviation levels. - Ensure that you add a comma ( , ) in between each standard deviation level - Enable or disable the standard deviations from appearing using the opacity of the color (change to 0%) Labels / Offset - Adjust the offset of the label for the Standard Deviations - Enable or disable the Labels from appearing using the checkbox Time - Adjust the time used for the indicators range - If you’d like to use this for a Session or ICT Killzone instead, adjust the time - Adjust the timezone used for the time referenced - Options are UTC, US (UTC-4, New York Local Time) or UK (UTC+1, London Time) - By default, the indicator is set to US
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fytte tarafından
9
[blackcat] L2 Barbara Star Supertrend IndicatorLevel 2 Background Barbara Star’s article on July 2023, “Stay On Track With The Supertrend Indicator”, I rewrote it as pine script for your information. Function A supertrend indicator is displayed either above or below the closing price to signal a buy or sell. The indicator changes color depending on whether you should buy or not. When the Supertrend indicator falls below the closing price, the indicator turns green, signaling one or more entry points to buy. Author Barbara Star describes the Supertrend indicator and how it can be used as a means for traders to stay in sync with the larger trend. She explains how J. Welles Wilder's Average True Range (ATR) forms a basis for supertrend calculations. ATR does not measure price direction, but rather provides a measure of volatility over a period of time. The Supertrend indicator, on the other hand, provides a more comprehensive view of trend direction. In addition, the indicator provides price levels at which a trend reversal would occur. Green color stands for up trend; Red color stands for down trend. Remarks Feedbacks are appreciated.
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blackcat1402 tarafından
3
Discrete Fourier Transformed Money Flow IndexThe Discrete Fourier Transform Money Flow Index indicator integrates the Money Flow Index (MFI) with Discrete Fourier Transform (credit to author wbburgin - May 26 2023 ) smoothing to offer a refined and smoothed depiction of the MFI's underlying trend. The MFI is calculated using the formula: MFI = 100 - (100 / (1 + MR)), where a high MFI value indicates robust buying pressure (signaling an overbought condition), and a low MFI value indicates substantial selling pressure (signaling an oversold condition). Why is the DFT and MFI combined? The aim of this combination between DFT and MFI is to effectively filter out short-term fluctuations and noise, enabling a clearer assessment of the overall trend. This smoothing process enhances the reliability of the MFI by emphasizing dominant and sustained buying or selling pressures. This script executes a full DFT but only uses filtering from one frequency component. The choice to focus on the magnitude at index 0 is significant as it captures the dominant or fundamental frequency in the data. By analyzing this primary cyclic behavior, we can identify recurring patterns and potential turning points more easily. This streamlined approach simplifies interpretation and enhances efficiency by reducing complexity associated with multiple frequency components. Overall, focusing on the dominant frequency and applying it to the MFI provides a concise and actionable assessment of the underlying data. Note: The FMFI indicator provides both smoothed and non-smoothed versions of the MFI, with the option to toggle the original non-smoothed MFI on or off in the settings. Application FMFI functions as a trend-following indicator. Bullish trends are denoted by the color white, while bearish trends are represented by the color purple. Circles plotted on the FMFI indicate regular bull and bear signals. Additionally, red arrows indicate a strong negative trend, while green arrows indicate a strong positive trend. These arrows are calculated based on the presence of regular bull and bear signals within overbought and oversold zones. To enhance its effectiveness, it is recommended to combine this indicator with other complementary technical analysis tools and integrate it into a comprehensive trading strategy. Traders are encouraged to explore a wide range of settings and timeframes to align the indicator with their unique trading preferences and adapt it to the current market conditions. By doing so, traders can optimize the indicator's performance and increase their potential for successful trading outcomes. Utility Traders and investors can employ this indicator to enhance their trend-following strategies. The white-colored components of the FMFI can help identify potential buying zones, while the purple-colored components can assist in identifying potential selling points. The red and green arrows can be used to pinpoint moments of strong bull or bear momentum, allowing traders to position themselves advantageously in their trading activities. Please note that future performance of any trading strategy is fundamentally unknowable, and past results do not guarantee future performance.
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profitprotrading tarafından
4
Futures All List / Sell SignalAs of May 2023, there are more than 180 usdt perpetual coins on the binance futures exchange. These coins are included in the indicator in lists of 40. They are sorted instantly in the table from largest to smallest. The sorting style can be changed in the indicator settings. This indicator collects RSI and TSI values at desired values. The result has a maximum value of 600. A value of 600 signals that the price will decrease or remain stable for a certain period of time. Generally, a short can be expected from the closest point to 600. If 3 separate lists are selected by using 3 of these indicators, 120 coins can be analyzed at the same time. Available in all time zones. Examine it in a 3-minute timeframe. The line inside the indicator draws the instantaneous values of the relevant coin.
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gokhanpirnal tarafından
3
Visible Range Linear Regression Channel [vnhilton](OVERVIEW) This indicator calculates the linear regression channel for the visible bars shown on the chart instead of the traditional fixed length linear regression channel TradingView provides (and is more accurate I believe). Inspired by TradingView's Linear Regression Channel and Visible Average Price indicator, and the DAS Trader linear regression indicator. (FEATURES) - Ability to extend lines to the right - Show/hide individual lines - Adjust standard deviation of bands - Adjust line style and width of basis and band lines - Change individual line colours and plot fills between the lines (DIFFERENCES) If you compare this indicator to TradingView's Linear Regression Channel, you will notice some differences (as of 11th June, 2023). Differences and reasons are: 1) The intercept is wrong. The formula TradingView uses to calculate the intercept includes the addition of the gradient, which I believe is incorrect. Difference #2 is also why the intercept is wrong. This indicator omits that addition. This was verified by comparing the gradient calculated in this indicator with the gradient determined by Excel with the same data. 2) The gradient is "wrong". In quotations as essentially TradingView's code attempts to find the line of best fit, with the y-axis on the most recent bar instead of the oldest bar. This leads to the gradient being the opposite to the gradient found in this indicator, which isn't wrong, but the later formula used to calculate the intercept doesn't take this into account, resulting in an incorrect intercept value. The gradient and intercept values in this indicator matches those found in Excel. 3) Standard deviation bands of both indicators. I believe the code TradingView uses to calculate standard deviation is incorrect (basing this just through visuals). This indicator uses the array.stdev function to find the correct value (verified with Excel numbers).
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vnhilton tarafından
8