Inizia

Statistics

Position Cost DistributionThe Position Cost Distribution indicator (also known as the Market Position Overview, Chip Distribution, or CYQ Algorithm) provides an estimate of how shares are distributed across different price levels. Visually, it resembles the Volume Profile indicator, though they rely on distinct computational approaches. 🟠 Principle The Position Cost Distribution algorithm is based on the principle that a security's total shares outstanding usually remains constant, except under conditions like stock splits, reverse splits, or new share issuance. It views all trading activity as simply exchanging share positions between holders at different price points. By analyzing daily trade volume and the prior day's distribution, the algorithm infers the resulting share distribution after each day. By tracking these inferred transpositions over time, the indicator builds up an aggregate view of the estimated share concentration at each price level. This provides insights into potential buying and selling pressure zones that could form support or resistance areas. Together with the Volume Profile, the Position Cost Distribution gives traders multiple lenses for examining market structure from both a volume and positional standpoint. Both can help identify meaningful technical price levels. 🟠 Algorithm The algorithm initializes by allocating all shares to the price range encompassed by the first bar displayed on the chart. Preferably, the chart window should include the stock's IPO date, allowing the model to distribute shares specifically to the IPO price. For subsequent trading sessions, the indicator performs the following calculations: 1. The daily turnover ratio is calculated by dividing the bar's trading volume by total outstanding shares. 2. For each price level (bucket), the number of shares is reduced by the turnover amount to represent shares transferring from existing holders. 3. The bar's total volume is then added to buckets corresponding to that period's price range. Currently, the model assumes each share has an equal probability of being exchanged, regardless of how long ago it was acquired or at what price. Potential optimizations could incorporate factors like making shares held longer face a smaller chance of transfer compared to more recently purchased shares. ──────────────────────────────────────────── 中文介绍：该指标为“筹码分布”的一个 TradingView 实现 :)
Indicatore Pine Script™
di AlphaViz
11
SFC Valuation Model - US SectorSector analysis is an assessment of the economic and financial condition and prospects of a given sector of the economy. Sector analysis serves to provide an investor with a judgment about how well companies in the sector are expected to perform. Sector analysis is typically employed by investors who specialize in a particular sector, or who use a top-down or sector rotation approach to investing. Sector analysis is based on the premise that certain sectors perform better during different stages of the business cycle. The business cycle refers to the up and down changes in economic activity that occur in an economy over time. The business cycle consists of expansions, which are periods of economic growth, and contractions, which are periods of economic decline. Investors who employ a top-down approach to sector analysis focus first on macroeconomic conditions in their search for companies that have the potential to outperform. They start by looking at those macroeconomic factors that have the biggest impact on the largest part of the population and the economy, such as unemployment rates, economic outputs, and inflation. Every sector shows the average return from three ETFs - SPDR, Vanguard, iShares. There is a possibility to see the returns from every ETF by just holding the cursor on the sector name. There are few valuation methods/steps - Macroeconomics - analyse the current economic; - Define how the sector is performing; - Relative valuation method - compare few stocks and find the Outlier; - Absolute valuation method historically- define how the stock performed in the past; - Absolute valuation method - define how the stock is performed now and find the fair value; - Technical analysis How to use: 1. Once you have completed the initial evaluation step, simply load the indicator. 2. Analyse which sector is outperforming.
Indicatore Pine Script™
di SerpentForexClub
WIPFunctionLyaponovLibrary "WIPFunctionLyaponov" Lyapunov exponents are mathematical measures used to describe the behavior of a system over time. They are named after Russian mathematician Alexei Lyapunov, who first introduced the concept in the late 19th century. The exponent is defined as the rate at which a particular function or variable changes over time, and can be positive, negative, or zero. Positive exponents indicate that a system tends to grow or expand over time, while negative exponents indicate that a system tends to shrink or decay. Zero exponents indicate that the system does not change significantly over time. Lyapunov exponents are used in various fields of science and engineering, including physics, economics, and biology, to study the long-term behavior of complex systems. ~ generated description from vicuna13b --- To calculate the Lyapunov Exponent (LE) of a given Time Series, we need to follow these steps: 1. Firstly, you should have access to your data in some format like CSV or Excel file. If not, then you can collect it manually using tools such as stopwatches and measuring tapes. 2. Once the data is collected, clean it up by removing any outliers that may skew results. This step involves checking for inconsistencies within your dataset (e.g., extremely large or small values) and either discarding them entirely or replacing with more reasonable estimates based on surrounding values. 3. Next, you need to determine the dimension of your time series data. In most cases, this will be equal to the number of variables being measured in each observation period (e.g., temperature, humidity, wind speed). 4. Now that we have a clean dataset with known dimensions, we can calculate the LE for our Time Series using the following formula: λ = log(||M^T * M - I||)/log(||v||) where: λ (Lyapunov Exponent) is the quantity that will be calculated. ||...|| denotes an Euclidean norm of a vector or matrix, which essentially means taking the square root of the sum of squares for each element in the vector/matrix. M represents our Jacobian Matrix whose elements are given by: J_ij = (∂fj / ∂xj) where fj is the jth variable and xj is the ith component of the initial condition vector x(t). In other words, each element in this matrix represents how much a small change in one variable affects another. I denotes an identity matrix whose elements are all equal to 1 (or any constant value if you prefer). This term essentially acts as a baseline for comparison purposes since we want our Jacobian Matrix M^T * M to be close to it when the system is stable and far away from it when the system is unstable. v represents an arbitrary vector whose Euclidean norm ||v|| will serve as a scaling factor in our calculation. The choice of this particular vector does not matter since we are only interested in its magnitude (i.e., length) for purposes of normalization. However, if you want to ensure that your results are accurate and consistent across different datasets or scenarios, it is recommended to use the same initial condition vector x(t) as used earlier when calculating our Jacobian Matrix M. 5. Finally, once we have calculated λ using the formula above, we can interpret its value in terms of stability/instability for our Time Series data: - If λ < 0, then this indicates that the system is stable (i.e., nearby trajectories will converge towards each other over time). - On the other hand, if λ > 0, then this implies that the system is unstable (i.e., nearby trajectories will diverge away from one another over time). ~ generated description from airoboros33b --- Reference: en.wikipedia.org www.collimator.ai blog.abhranil.net www.researchgate.net physics.stackexchange.com --- This is a work in progress, it may contain errors so use with caution. If you find flaws or suggest something new, please leave a comment bellow. _measure_function(i)   helper function to get the name of distance function by a index (0 -> 13).\ Functions: SSD, Euclidean, Manhattan, Minkowski, Chebyshev, Correlation, Cosine, Camberra, MAE, MSE, Lorentzian, Intersection, Penrose Shape, Meehl.   Parameters:      i (int) _test(L)   Helper function to test the output exponents state system and outputs description into a string.   Parameters:      L (float ) estimate(X, initial_distance, distance_function)   Estimate the Lyaponov Exponents for multiple series in a row matrix.   Parameters:      X (map)      initial_distance (float) : Initial distance limit.      distance_function (string) : Name of the distance function to be used, default:`ssd`.   Returns: List of Lyaponov exponents. max(L)   Maximal Lyaponov Exponent.   Parameters:      L (float ) : List of Lyapunov exponents.   Returns: Highest exponent.
Libreria Pine Script™
di RicardoSantos
1
SFC Valuation Model - AbsoluteFinancial statement analysis is the process of analyzing a company’s financial statements for decision-making purposes. External stakeholders use it to understand the overall health of an organization and to evaluate financial performance and business value. Internal constituents use it as a monitoring tool for managing the finances. Most often, analysts will use three main techniques for analyzing a company’s financial statements. First, horizontal analysis involves comparing historical data. Usually, the purpose of horizontal analysis is to detect growth trends across different time periods. Second, vertical analysis compares items on a financial statement in relation to each other. For instance, an expense item could be expressed as a percentage of company sales. Finally, ratio analysis, a central part of fundamental equity analysis, compares line-item data. Price-to-earnings (P/E) ratios, earnings per share, or dividend yield are examples of ratio analysis. The indicator shows the most important metrics to help investors evaluate a stock. It saves a lot of time searching for metrics on different websites and writing them into different platforms for further analysis. There are few valuation methods/steps - Macroeconomics - analyse the current economic; - Define how the sector is performing; - Relative valuation method - compare few stocks and find the Outlier; - Absolute valuation method historically- define how the stock performed in the past; - Absolute valuation method - define how the stock is performed now and find the fair value; - Technical analysis How to use: 1. Once you have completed the initial evaluation steps, simply load the indicator. 2. Make your analysis. 3. Complete the checklist by writing down your thoughts.
Indicatore Pine Script™
di SerpentForexClub
1
SFC Valuation Model - RelativeComparable company analysis, or “Comps” for short, is commonly used to value firms by comparing them to publicly traded companies with similar business operations. An analyst will compare the current share price a public company relative to some metric such as its earnings to derive a P/E ratio. It will then use that ratio to value the company it is trying to determine the worth of. One of the most popular relative valuation multiples is the price-to-earnings (P/E) ratio. It is calculated by dividing stock price by earnings per share (EPS), and is expressed as a company's share price as a multiple of its earnings. A company with a high P/E ratio is trading at a higher price per dollar of earnings than its peers and is considered overvalued. Likewise, a company with a low P/E ratio is trading at a lower price per dollar of EPS and is considered undervalued. This framework can be carried out with any multiple of price to gauge relative market value. Therefore, if the average P/E for an industry is 10x and a particular company in that industry is trading at 5x earnings, it is relatively undervalued to its peers. Limitations Like any valuation tool, relative valuation has its limitations. The biggest limitation is the assumption that the market has valued the business correctly. Second, all valuation metrics are based on past performance. Investors' perceptions of future performance heavily influence stock prices and most relative valuation metrics don’t account for growth. Finally and most importantly, relative valuation is no assurance that the "cheaper" company will outperform its peer. With this indicator, investors can easily compare a few companies and find the outlier. It calculates the average for the sector and highlights the stock that is above the average. Due to some limitations, the indicator can only compare 5 tickers, but users can always load it twice for more stocks. Save hours of data entry into Excel spreadsheets to compare stocks ! There are few valuation methods/steps - Macroeconomics - analyse the current economic; - Define how the sector is performing; - Relative valuation method - compare few stocks and find the Outlier; - Absolute valuation method historically- define how the stock performed in the past; - Absolute valuation method - define how the stock is performed now and find the fair value; - Technical analysis How to use: 1. Once you have completed the initial evaluation steps, simply load the indicator. 2. Add the forwarded EPS. 3. The indicator will do the rest of the calculations for you.
Indicatore Pine Script™
di SerpentForexClub
SFC Valuation Model - Fair ValueValuation is the analytical process of determining the current (or projected) worth of an asset or a company. There are many techniques used for doing a valuation. An analyst placing a value on a company looks at the business's management, the composition of its capital structure, the prospect of future earnings, and the market value of its assets, among other metrics. Fundamental analysis is often employed in valuation, although several other methods may be employed such as the capital asset pricing model (CAPM) or the dividend discount model (DDM), Discounted Cash Flow (DCF) and many others. A valuation can be useful when trying to determine the fair value of a security, which is determined by what a buyer is willing to pay a seller, assuming both parties enter the transaction willingly. When a security trades on an exchange, buyers and sellers determine the market value of a stock or bond. There is no universal standard for calculating the intrinsic value of a company or stock. Financial analysts attempt to determine an asset's intrinsic value by using fundamental and technical analyses to gauge its actual financial performance. Intrinsic value is useful because it can help an investor understand whether a potential investment is overvalued or undervalued. This indicator allows investors to simulate different scenarios depending on their view of the stock's value. It calculates different models automatically, but users can define the fair value manually by changing the settings. For example: change the weight of the model; choose how conservatively want to evaluate the stock; use different growth rate or discount rate and so on. The indicator shows other useful metrics in order to help investors to evaluate the stock. This indicator can save users hours of searching financial data and calculating fair value. There are few valuation methods/steps - Macroeconomics - analyse the current economic; - Define how the sector is performing; - Relative valuation method - compare few stocks and find the Outlier; - Absolute valuation method historically- define how the stock performed in the past; - Absolute valuation method - define how the stock is performed now and find the fair value; - Technical analysis How to use: 1. Once you have completed the initial evaluation steps, simply load the indicator. 2. Check the default settings and see if they suit you. 3. Find the fair value and wait for the stock to reach it.
Indicatore Pine Script™
di SerpentForexClub
Median of Means Estimator Median of Means (MoM) is a measure of central tendency like mean (average) and median. However, it could be a better and robust estimator of central tendency when the data is not normal, asymmetric, have fat tails (like stock price data) and have outliers. The MoM can be used as a robust trend following tool and in other derived indicators. Median of means (MoM) is calculated as follows, the MoM estimator shuffles the "n" data points and then splits them into k groups of m data points (n= k*m). It then computes the Arithmetic Mean of each group (k). Finally, it calculate the median over the resulting k Arithmetic Means. This technique diminishes the effect that outliers have on the final estimation by splitting the data and only considering the median of the resulting sub-estimations. This preserves the overall trend despite the data shuffle. Below is an example to illustrate the advantages of MoM Set A Set B Set C 3 4 4 3 4 4 3 5 5 3 5 5 4 5 5 4 5 5 5 5 5 5 5 5 6 6 8 6 6 8 7 7 10 7 7 15 8 8 40 9 9 50 10 100 100 Median 5 5 5 Mean 5.5 12.1 17.9 MoM 5.7 6.0 17.3 For all three sets the median is the same, though set A and B are the same except for one outlier in set B (100) it skews the mean but the median is resilient. However, in set C the group has several high values despite that the median is not responsive and still give 5 as the central tendency of the group, but the median of means is a value of 17.3 which is very close to the group mean 17.9. In all three cases (set A, B and C) the MoM provides a better snapshot of the central tendency of the group. Note: The MoM is dependent on the way we split the data initially and the value might slightly vary when the randomization is done sevral time and the resulting value can give the confidence interval of the MoM estimator.
Indicatore Pine Script™
di federalTacos5392b
2
Cumulative Distribution of a Dataset [SS]This is the Cumulative Distribution of a Dataset indicator that also calculates the Kurtosis and Skewness for a selected dataset and determines the normality and distribution type. What it does, in pragmatic terms? In the most simplest terms, it calculates the cumulative distribution function (or CDF) of user-defined dataset. The cumulative distribution function (CDF) is a concept used in statistics and probability to describe how the probability of a random variable taking on a certain value or less is distributed across the entire range of possible values. In simpler terms, you can conceptualize the CDF as this: Imagine you have a list of data, such as test scores of students in a class. The CDF helps you answer questions like, "What's the probability that a randomly chosen student scored 80 or less on the test?" Or in our case, say we are in a strong up or downtrend on a stock. The CDF can help us answer questions like "Based on this current xyz trend, what is the probability that a ticker will fall above X price or below Y price". Within the indicator, you can manually assess a price of interest. Let's say, for NVDA, we want to know the probability NVDA goes above or below $450. We can enter $450 into the indicator and get this result: Other functions: Kurtosis and Skewness Functions: In addition to calculating and plotting the CDF, we can also plot the kurtosis & Skewness. This can help you look for outlier periods where the distribution of your dataset changed. It can potentially alert you to when a stock is behaving abnormally and when it is more stable and evenly distributed. Tests of normality The indicator will use the kurtosis and skewness to determine the normality of the dataset. The indicator is programmed to recognize up to 7 different distribution types and alert you to them and the implications they have in your overall assessment. e.g. #1 AMC during short squeeze: e.g. #2: BA during the COVID crash: Plotting the standardized Z-Score of the Distribution Dataset You can also standardize the dataset by converting it into Z-Score format: Plot the raw, CDF results Two values are plotting, the green and the red. The green represents the probability of a ticker going higher than the current value. The red represents the probability of a ticker going lower than the current value. Limitations There are some limitations of the indicator which I think are important to point out. They are: The indicator cannot tell you timelines, it can only tell you the general probability that data within the dataset will fall above or below a certain value. The indicator cannot take into account projected periods of consolidation. It is possible a ticker can remain in a consolidation phase for a very long time. This would have the effect of stabilizing the probability in one direction (if there was a lot of downside room, it can normalize the data out so that the extent of the downside probability is mitigated). Thus, its important to use judgement and other methods to assess the likelihood that a stock will pullback or continue up, based on the overall probability. The indicator is only looking at an individual dataset. Using this indicator, you have to omit a large amount of data and look at solely a confined dataset. In a way, this actually improves the accuracy, but can also be misleading, depending on the size and strength of the dataset being chosen. It is important to balance your choice of dataset time with such things as: a) The strength of the uptrend or downtrend. b) The length of the uptrend or downtrend. c) The overall performance of the stock leading into the dataset time period And that is the indicator in a nutshell. Hopefully you find it helpful and interesting. Feel free to leave questions, comments and suggestions below. Safe trades everyone and take care!
Indicatore Pine Script™
di Steversteves
30
Returns Model by TenozenHey there! I've been diving into the book "Paul Wilmott on Quantitative Finance," and I stumbled upon this cool model for calculating and modeling returns. Basically, it helps us figure out how much a price has changed over a set number of periods—I like to use 20 periods as a default. Once we get that rate of change value, we crunch some numbers to find the standard deviation and mean using all the historical data we have. That's the foundation of this model. Now, let's talk about how to use it. This model shows us how returns and price behavior are connected. When returns hang out in the +1 to +2 standard deviation range, it usually means returns are about to drop, and vice versa. Often, this leads to corresponding price moves. But here's the thing: sometimes prices don't do what we expect. Why? It's because there's another hidden factor at play—I like to call it "power." This "power" isn't something we can see directly, but it's there. Basically, when returns are within that standard deviation range, the market faces resistance when trying to move in its preferred direction, whether bullish or bearish. The strength of this "power" determines if the market will snap back to the average or go for a wild ride. It can show up as small price wiggles, big price jumps, or lightning-fast moves. By understanding this "power," we can get a better handle on what the market might do next and avoid getting blindsided. In the meantime, I couldn't explain "power" yet, but In the future, when I've learned enough, I'd love to share the model with you guys! So... I'm planning to explore and share more models from this book as I learn, even if those pesky math formulas can be tough to crack. I hope you find this indicator as helpful as I do, and if you've got any suggestions or feedback, please feel free to share! Ciao!
Indicatore Pine Script™
di Tenozen
12
TTP PNR filterPNR filter uses the "percentile nearest rank" method to produce signals from any source including oscillator indicators and price bars. Features: * Length - how many candles back in time to use for calculating PNR * % low and high - what range of the spread of values captured will form the PNR band. Use 99&100 to create a band on the 1% highest percentile or 0&1 to create a band in the lowest percentile. It accepts float numbers so you can find very rare occurrences. * src - by default it will use the close price but PNR filter can be used with any source. It's particularly useful when working with oscillators like RSI, MACD, ADX, etc. * Signal direction - The indicator will print 1 when the selected conditions are met. Once the PNR band is plotted you can chose from cross over, cross under, above and below conditions to trigger a signal. * Signal source - the band consists in a % low and % high, this option allows you to pick which band will be used with the "signal direction" parameter. Example configuration: 1) Select 200 as the length 2) Select % low 0 and % high 1 3) Add RSI to the chart and select it as the source parameter 4) Select signal direction cross over 5) Select signal source % high which corresponds to the 1% band In this setup you are finding values of RSI that in the past 200 candles have been that low only 1% of the time. With each new candle the calculation window will move as well leaving the oldest candle out.
Indicatore Pine Script™
di TheTradingParrot
12
Signal to Noise TrendSignal to Noise Ratio The Signal to Noise Ratio or SNR is used to assess the quality of information or data by comparing the strength of a useful signal to the presence of background noise or random variations. In Finance the SNR refers to the ratio of strength of a trading signal to the background noise. A high SNR suggest a clear and reliable signal, meanwhile a low SNR indicates more noise (random fluctuations, volatility, or randomness). Signal To Noise Trend This indicator basically calculates the signal to noise of returns and then gets the Z-Score of the signal to noise ratio to find extremes levels of signal and noise. The Lines basically are standard deviations from the mean. 1,2,3 Are standard deviations same with the -1,-2,-3 Lines. The signal is expressed as the positive Z-Score value, and the Noise is the negative Z-Score Value. The moving average enhances the indicator ability to display the trend of returns and the trend strength. It provides a smooth representation of the Signal to Nose Ratio values. There are more trending conditions when there is a higher signal, and there is more "ranging" conditions when there is more noise present in the markets. The Standard deviations help find extreme levels of signal and noise. If the noise reaches the standard deviation of -3 then that means that there is a extreme negative deviation from the mean, and this would be a rare occurrence, with a lot of noise. This could indicate a potential reversion in market states, and could be followed by a trending move. Another example is that if the Z-Score value reaches a Standard deviation of 3, this could mean that there is extremely strong and rare signal, and could potentially mean a change to a more noisy environment soon.
Indicatore Pine Script™
di Traders_Endeavors
lib_profileLibrary "lib_profile" a library with functions to calculate a volume profile for either a set of candles within the current chart, or a single candle from its lower timeframe security data. All you need is to feed the method delete(this)   deletes this bucket's plot from the chart   Namespace types: Bucket   Parameters:      this (Bucket) method delete(this)   Namespace types: Profile   Parameters:      this (Profile) method delete(this)   Namespace types: Bucket   Parameters:      this (Bucket ) method delete(this)   Namespace types: Profile   Parameters:      this (Profile ) method update(this, top, bottom, value, fraction)   updates this bucket's data   Namespace types: Bucket   Parameters:      this (Bucket)      top (float)      bottom (float)      value (float)      fraction (float) method update(this, tops, bottoms, values)   update this Profile's data (recalculates the whole profile and applies the result to this object) TODO optimisation to calculate this incremental to improve performance in realtime on high resolution   Namespace types: Profile   Parameters:      this (Profile)      tops (float ) : array of range top/high values (either from ltf or chart candles using history() function      bottoms (float ) : array of range bottom/low values (either from ltf or chart candles using history() function      values (float ) : array of range volume/1 values (either from ltf or chart candles using history() function (1s can be used for analysing candles in bucket/price range over time) method tostring(this)   allows debug print of a bucket   Namespace types: Bucket   Parameters:      this (Bucket) method draw(this, start_t, start_i, end_t, end_i, args, line_color)   allows drawing a line in a Profile, representing this bucket and it's value + it's value's fraction of the Profile total value   Namespace types: Bucket   Parameters:      this (Bucket)      start_t (int) : the time x coordinate of the line's left end (depends on the Profile box)      start_i (int) : the bar_index x coordinate of the line's left end (depends on the Profile box)      end_t (int) : the time x coordinate of the line's right end (depends on the Profile box)      end_i (int) : the bar_index x coordinate of the line's right end (depends on the Profile box)      args (LineArgs type from robbatt/lib_plot_objects/24) : the default arguments for the line style      line_color (color) : the color override for POC/VAH/VAL lines method draw(this, forced_width)   draw all components of this Profile (Box, Background, Bucket lines, POC/VAH/VAL overlay levels and labels)   Namespace types: Profile   Parameters:      this (Profile)      forced_width (int) : allows to force width of the Profile Box, overrides the ProfileArgs.default_size and ProfileArgs.extend arguments (default: na) method init(this)   Namespace types: ProfileArgs   Parameters:      this (ProfileArgs) method init(this)   Namespace types: Profile   Parameters:      this (Profile) profile(tops, bottoms, values, resolution, vah_pc, val_pc, bucket_buffer)   split a chart/parent bar into 'resolution' sections, figure out in which section the most volume/time was spent, by analysing a given set of (intra)bars' top/bottom/volume values. Then return price center of the bin with the highest volume, essentially marking the point of control / highest volume (poc) in the chart/parent bar.   Parameters:      tops (float ) : array of range top/high values (either from ltf or chart candles using history() function      bottoms (float ) : array of range bottom/low values (either from ltf or chart candles using history() function      values (float ) : array of range volume/1 values (either from ltf or chart candles using history() function (1s can be used for analysing candles in bucket/price range over time)      resolution (int) : amount of buckets/price ranges to sort the candle data into (analyse how much volume / time was spent in a certain bucket/price range) (default: 25)      vah_pc (float) : a threshold percentage (of values' total) for the top end of the value area (default: 80)      val_pc (float) : a threshold percentage (of values' total) for the bottom end of the value area (default: 20)      bucket_buffer (Bucket ) : optional buffer of empty Buckets to fill, if omitted a new one is created and returned. The buffer length must match the resolution   Returns: poc (price level), vah (price level), val (price level), poc_index (idx in buckets), vah_index (idx in buckets), val_index (idx in buckets), buckets (filled buffer or new) create_profile(start_idx, tops, bottoms, values, resolution, vah_pc, val_pc, args)   split a chart/parent bar into 'resolution' sections, figure out in which section the most volume/time was spent, by analysing a given set of (intra)bars' top/bottom/volume values. Then return price center of the bin with the highest volume, essentially marking the point of control / highest volume (poc) in the chart/parent bar.   Parameters:      start_idx (int) : the bar_index at which the Profile should start drawing      tops (float ) : array of range top/high values (either from ltf or chart candles using history() function      bottoms (float ) : array of range bottom/low values (either from ltf or chart candles using history() function      values (float ) : array of range volume/1 values (either from ltf or chart candles using history() function (1s can be used for analysing candles in bucket/price range over time)      resolution (int) : amount of buckets/price ranges to sort the candle data into (analyse how much volume / time was spent in a certain bucket/price range) (default: 25)      vah_pc (float) : a threshold percentage (of values' total) for the top end of the value area (default: 80)      val_pc (float) : a threshold percentage (of values' total) for the bottom end of the value area (default: 20)      args (ProfileArgs)   Returns: poc (price level), vah (price level), val (price level), poc_index (idx in buckets), vah_index (idx in buckets), val_index (idx in buckets), buckets (filled buffer or new) history(src, len, offset)   allows fetching an array of values from the history series with offset from current candle   Parameters:      src (int)      len (int)      offset (int) history(src, len, offset)   allows fetching an array of values from the history series with offset from current candle   Parameters:      src (float)      len (int)      offset (int) history(src, len, offset)   allows fetching an array of values from the history series with offset from current candle   Parameters:      src (bool)      len (int)      offset (int) history(src, len, offset)   allows fetching an array of values from the history series with offset from current candle   Parameters:      src (string)      len (int)      offset (int) Bucket   Fields:      idx (series int) : the index of this Bucket within the Profile starting with 0 for the lowest Bucket at the bottom of the Profile      value (series float) : the value of this Bucket, can be volume or time, for using time pass and array of 1s to the update function      top (series float) : the top of this Bucket's price range (for calculation)      btm (series float) : the bottom of this Bucket's price range (for calculation)      center (series float) : the center of this Bucket's price range (for plotting)      fraction (series float) : the fraction this Bucket's value is compared to the total of the Profile      plot_bucket_line (Line type from robbatt/lib_plot_objects/24) : the line that resembles this bucket and it's valeu in the Profile ProfileArgs   Fields:      show_poc (series bool) : whether to plot a POC line across the Profile Box (default: true)      show_profile (series bool) : whether to plot a line for each Bucket in the Profile Box, indicating the value per Bucket (Price range), e.g. volume that occured in a certain time and price range (default: false)      show_va (series bool) : whether to plot a VAH/VAL line across the Profile Box (default: false)      show_va_fill (series bool) : whether to fill the 'value' area between VAH/VAL line (default: false)      show_background (series bool) : whether to fill the Profile Box with a background color (default: false)      show_labels (series bool) : whether to add labels to the right end of the POC/VAH/VAL line (default: false)      show_price_levels (series bool) : whether add price values to the labels to the right end of the POC/VAH/VAL line (default: false)      extend (series bool) : whether extend the Profile Box to the current candle (default: false)      default_size (series int) : the default min. width of the Profile Box (default: 30)      args_poc_line (LineArgs type from robbatt/lib_plot_objects/24) : arguments for the poc line plot      args_va_line (LineArgs type from robbatt/lib_plot_objects/24) : arguments for the va line plot      args_poc_label (LabelArgs type from robbatt/lib_plot_objects/24) : arguments for the poc label plot      args_va_label (LabelArgs type from robbatt/lib_plot_objects/24) : arguments for the va label plot      args_profile_line (LineArgs type from robbatt/lib_plot_objects/24) : arguments for the Bucket line plots      args_profile_bg (BoxArgs type from robbatt/lib_plot_objects/24)      va_fill_color (series color) : color for the va area fill plot Profile   Fields:      start (series int) : left x coordinate for the Profile Box      end (series int) : right x coordinate for the Profile Box      resolution (series int) : the amount of buckets/price ranges the Profile will dissect the data into      vah_threshold_pc (series float) : the percentage of the total data value to mark the upper threshold for the main value area      val_threshold_pc (series float) : the percentage of the total data value to mark the lower threshold for the main value area      args (ProfileArgs) : the style arguments for the Profile Box      h (series float) : the highest price of the data      l (series float) : the lowest price of the data      total (series float) : the total data value (e.g. volume of all candles, or just one each to analyse candle distribution over time)      buckets (Bucket ) : the Bucket objects holding the data for each price range bucket      poc_bucket_index (series int) : the Bucket index in buckets, that holds the poc Bucket      vah_bucket_index (series int) : the Bucket index in buckets, that holds the vah Bucket      val_bucket_index (series int) : the Bucket index in buckets, that holds the val Bucket      poc (series float) : the according price level marking the Point Of Control      vah (series float) : the according price level marking the Value Area High      val (series float) : the according price level marking the Value Area Low      plot_poc (Line type from robbatt/lib_plot_objects/24)      plot_vah (Line type from robbatt/lib_plot_objects/24)      plot_val (Line type from robbatt/lib_plot_objects/24)      plot_poc_label (Label type from robbatt/lib_plot_objects/24)      plot_vah_label (Label type from robbatt/lib_plot_objects/24)      plot_val_label (Label type from robbatt/lib_plot_objects/24)      plot_va_fill (LineFill type from robbatt/lib_plot_objects/24)      plot_profile_bg (Box type from robbatt/lib_plot_objects/24)
Libreria Pine Script™
di robbatt
3
MMI Auto Backtesting StrategyDescription: A strategy based on ATR with auto-backtesting capabilities, Take Profit and Stop Loss (either Normal or Trailing). It allows you to select ranges of values and step for each parameter, and backtest the strategy on a multitude of input combinations at once. You can alternatively use a constant value for each parameter. The backtesting results strive to be as close as possible to those given by Tradingview Strategy Tester. The strategy displays a table with results for different input combinations. This has columns showing current input combination as well as the following stats: Net Profit, Number of trades, % of Profitable trades, Profit Factor, Max Drawdown, Max Runup, Average Trade and Average number of bars in a trade. You can sort the table by any column (including sorting by multiple columns at the same time) to find, for example, input combination that gives highest Net Profit (or, if sorting by multiple columns, to find input combination with the best balance of Net Profit and % of Profitable trades). You can filter by any column as well (or multiple columns at the same time), using logical expressions like "< value", "> value", "<= value", ">= value". And you can use logical expressions like "< value%" for Net Profit, Max Drawdown, Max Runup and Average trade to filter by percentage value. You will see a "↓" symbol in column's header if that column is sorted from Highest to Lowest, a "↑" symbol if it's sorted from Lowest to Highest and a "𐕢" symbol if that column is being filtered. The table has customisable styles (like text color, background color of cells, etc.), and can show the total number of backtested combinations with the time taken to test them. You can also change Initial Capital and Position Size (either Contracts, Currency or % of Equity). Parameters: The following parameters are located in the "INPUTS (USUAL STRATEGY)" group, and control the behaviour of strategy itself (not the auto-backtesting functionality): - Period: ATR Length - Multiplier: ATR Multiplier - DPO: length of the filtering moving average - SL: stop loss - TP: take profit - Use Stop Loss: enable stop loss - Stop Loss Mode: stop loss mode (either Normal or Trailing) - Use Take Profit: enable take profit - Wicks: use high & low price, or close price The strategy also has various parameters separated by different groups: - INPUTS (AUTO-BACKTESTING): has the same parameters as the "INPUTS (USUAL STRATEGY)" group, but controls the input combinations for auto-backtesting; all the numeric parameters have 3 values: F/V (from), T (to) and S (step); if the checkbox to the left of F/V parameter is off, the value of F/V will indicate the constant value used for that parameter (if the checkbox is on, the values will be from F/V to T using step S) - STRATEGY: contains strategy related parameters like Initial Capital and Position Size - BACKTESTING: allows you to display either Percentage, Absolute or Both values in the table and has checkboxes that allow you to exclude certain columns from the table - SORTING: allows you to select sorting mode (Highest to Lowest or vice versa) and has checkboxes in case you want to sort by multiple columns at the same time - FILTERING: has a text field for each column of the strategy where you can type logical expressions to filter the values - TABLE: contains styling parameters Many parameters have the "(i)" description marker, so hover over it to see more details. Problems: - The script works best on lower timeframes and continuous markets (trades 24/7), in other cases the backtesting results may vary from those that Tradingview shows - The script shows closest results when Take Profit and Stop Loss are not used - Max Runup percentage value is often wrong Limitations: - As we are limited by the maximum time a script can be running (which is 20s for Free plan and 40s for Paid plans), we can only backtest several hundreds of combinations within that timeframe (though it depends on the parameters, market and timeframe of the chart you use)
Strategia Pine Script™
di sabricat
Percentile Based Trend StrengthThe "Percentile Based Trend Strength" (PBTS) calculates trend strength based on percentile values of high and low prices for various length periods and then identifies the current trend as either Bullish, Bearish, or N/A (No Trend). Here's a step-by-step explanation of the code: Percentile Calculations: For each specified length period (13, 21, 34, 55, 89, and 144 - Fibonacci numbers), the code calculates the 75th percentile of high prices (e.g., percentile_13H) and the 25th percentile of low prices (e.g., percentile_13L). These percentiles represent levels that prices need to exceed or fall below to indicate a strong trend. Calculate Highest High and Lowest Low: The highest high (75th percentile high price of longest length) and lowest low (25th percentile low price of longest length) for the longest length period (144) are calculated as highest_high and lowest_low. These values represent threshold price levels . Trend Strength Conditions: The code calculates various conditions to determine trend strength. For each percentile value and each length period, it checks if the percentile value is greater than the highest high (trendBull) or less than the lowest low (trendBear). These conditions are used to assess the strength of the bullish and bearish trends. Count Bull and Count Bear: The countBull and countBear variables count the number of bullish and bearish conditions met, respectively. These counts help evaluate trend strength. Weak Bull and Weak Bear Count: The code calculates the number of weak bullish and bearish conditions. Weak conditions occur when a percentile value falls within the range defined by the highest high and lowest low but doesn't meet the strong trend criteria. Bull Strength and Bear Strength: bullStrength and bearStrength are calculated based on the counts of bullish, bearish, weak bullish, and weak bearish conditions. These values represent the overall strength of the bullish and bearish trends. Strong Bull and Bear Conditions: These conditions occur when the 75th percentile of high prices (for bull conditions) or the 25th percentile of low prices (for bear conditions) exceeds or falls below the highest high or lowest low, respectively, for the specified length period. Strong bull conditions indicate a strong upward trend, while strong bear conditions indicate a strong downward trend. Strong conditions are indicative of more significant price movements and are considered as primary signals of trend strength. Weak Bull and Bear Conditions: Weak bull and bear conditions are more nuanced. They occur when the 75th percentile of high prices (for weak bull conditions) or the 25th percentile of low prices (for weak bear conditions) falls within the range defined by the highest high and lowest low for the specified length period. In other words, prices are not strong enough to reach the extreme levels represented by the highest high or lowest low, but they still exhibit some bullish or bearish tendencies within that range. Weak conditions suggest a less robust trend. They may indicate that while there is some bias toward a bullish or bearish trend, it is not as strong or decisive as in the case of strong conditions. Current Trend Identification: The current trend is determined by comparing bullStrength and bearStrength. If bullStrength is greater, it's considered a Bull trend; if bearStrength is greater, it's a Bear trend. If they are equal, the trend is identified as N/A (No Trend). Displaying Trend Information: The code creates a table to display the current trend, reversal probability (strength), count of bullish and bearish conditions, weak bullish and weak bearish counts, and colors the text accordingly. Plotting Percentiles: Finally, the code plots the percentile lines for visualization, with 20% transparency. It also plots the highest high and lowest low lines (75th and 25th percentile of the longest length 144) using their original colors. In summary, this indicator calculates trend strength based on percentile levels of high and low prices for different length periods. It then counts the number of bullish and bearish conditions, factors in weak conditions, and compares the strengths to identify the current trend as Bullish, Bearish, or No Trend. It provides a table with trend information and visualizes percentile lines on the chart.
Indicatore Pine Script™
di federalTacos5392b
Strategy Gaussian Anomaly DerivativeConcept behind this Strategy : Considering a normal "buy/sell" situation, an asset would be bought in average at the median price following a Gaussian like concept. A higher or lower average trend would significate that the current perceived value is respectively higher or lower than the current median price, which mean that the buyers are evaluating the price underpriced or overpriced. This behaviour would be even more relevent depending on its derivative evolution. Therefore, this Strategy setup is based on this Gaussian like concept anomaly of average close positionning compare to high-low average derivative, such as the derivative of the following ploted basic signal : 1-(high+low)/(2*close). This Strategy can actually be used like a trend change and continuation strength indicator aswell. In the Setup Signal part : You can define the filtering of the basis signal "1-(high+low)/(2*close)" on EMA or SMA as you wish. You can define the corresponding period and the threathold as a mutiply of the average 1/3 of all time value of the basis signal. You can define the SMA filtering period of the Derivative signal and the corresponding threathold on the same mutiply of the average 1/3 of all time value of the derivative. In the Setup Strategy part : You can set up your strategy assesment based on Long and/or Short. You can also define the considered period. The most successful tuned strategies I did were based on the derivative indicator with periods on the basis signal and the derivative under 30, can be 1 to 3 of te derivative and 7 to 21 for the basis signal. The threathold depends on the asset volatility aswell, 1 is usually the most efficient but 0 to 10 can be relevent depending on the situation I met. You can find an example of tuning for this strategy based on Kering's case hereafter. I hoping that you will enjoy using this Strategy, don't hesitate to comment, to question, to correct or complete it ! I would be very curious about similar famous approaches that would have already been made. Thank to you !
Strategia Pine Script™
di Koamind
5
Z MomentumOverview This is a Z-Scored Momentum Indicator. It allows you to understand the volatility of a financial instrument. This indicator calculates and displays the momentum of z-score returns expected value which can be used for finding the regime or for trading inefficiencies. Indicator Purpose: The primary purpose of the "Z-Score Momentum" indicator is to help traders identify potential trading opportunities by assessing how far the current returns of a financial instrument deviate from their historical mean returns. This analysis can aid in recognizing overbought or oversold conditions, trend strength, and potential reversal points. Things to note: A Z-Score is a measure of how many standard deviations a data point is away from the mean. EV: Expected Value, which is basically the average outcome. When the Z-Score Momentum is above 0, there is a positive Z-Score which indicates that the current returns of the financial instrument are above their historical mean returns over the specified return lookback period, which could mean Positive, Momentum, and in a extremely high Z-Score value, like above +2 Standard deviations it could indicate extreme conditions, but keep in mind this doesn't mean price will go down, this is just the EV. When the Z-Score Momentum is below 0, there is negative Z-Score which indicates that the current returns of the financial instrument are below their historical mean returns which means you could expect negative returns. In extreme Z-Score situations like -2 Standard deviations this could indicate extreme conditions and the negative momentum is coming to an end. TDLR: Interpretation: Positive Z-Score: When the Z-score is positive and increasing, it suggests that current returns are above their historical mean, indicating potential positive momentum. Negative Z-Score: Conversely, a negative and decreasing Z-score implies that current returns are below their historical mean, suggesting potential negative momentum. Extremely High or Low Z-Score: Extremely high (above +2) or low (below -2) Z-scores may indicate extreme market conditions that could be followed by reversals or significant price movements. The lines on the Indicator highlight the Standard deviations of the Z-Score. It shows the Standard deviations 1,2,3 and -1,-2,-3.
Indicatore Pine Script™
di Traders_Endeavors
Paytience DistributionPaytience Distribution Indicator User Guide Overview: The Paytience Distribution indicator is designed to visualize the distribution of any chosen data source. By default, it visualizes the distribution of a built-in Relative Strength Index (RSI). This guide provides details on its functionality and settings. Distribution Explanation: A distribution in statistics and data analysis represents the way values or a set of data are spread out or distributed over a range. The distribution can show where values are concentrated, values are absent or infrequent, or any other patterns. Visualizing distributions helps users understand underlying patterns and tendencies in the data. Settings and Parameters: Main Settings: Window Size - Description: This dictates the amount of data used to calculate the distribution. - Options: A whole number (integer). - Tooltip: A window size of 0 means it uses all the available data. Scale - Description: Adjusts the height of the distribution visualization. - Options: Any integer between 20 and 499. Round Source - Description: Rounds the chosen data source to a specified number of decimal places. - Options: Any whole number (integer). Minimum Value - Description: Specifies the minimum value you wish to account for in the distribution. - Options: Any integer from 0 to 100. - Tooltip: 0 being the lowest and 100 being the highest. Smoothing - Description: Applies a smoothing function to the distribution visualization to simplify its appearance. - Options: Any integer between 1 and 20. Include 0 - Description: Dictates whether zero should be included in the distribution visualization. - Options: True (include) or False (exclude). Standard Deviation - Description: Enables the visualization of standard deviation, which measures the amount of variation or dispersion in the chosen data set. - Tooltip: This is best suited for a source that has a vaguely Gaussian (bell-curved) distribution. - Options: True (enable) or False (disable). Color Options - High Color and Low Color: Specifies colors for high and low data points. - Standard Deviation Color: Designates a color for the standard deviation lines. Example Settings: Example Usage RSI - Description: Enables the use of RSI as the data source. - Options: True (enable) or False (disable). RSI Length - Description: Determines the period over which the RSI is calculated. - Options: Any integer greater than 1. Using an External Source: To visualize the distribution of an external source: Select the "Move to" option in the dropdown menu for the Paytience Distribution indicator on your chart. Set it to the existing panel where your external data source is placed. Navigate to "Pin to Scale" and pin the indicator to the same scale as your external source. Indicator Logic and Functions: Sinc Function: Used in signal processing, the sinc function ensures the elimination of aliasing effects. Sinc Filter: A filtering mechanism which uses sinc function to provide estimates on the data. Weighted Mean & Standard Deviation: These are statistical measures used to capture the central tendency and variability in the data, respectively. Output and Visualization: The indicator visualizes the distribution as a series of colored boxes, with the intensity of the color indicating the frequency of the data points in that range. Additionally, lines representing the standard deviation from the mean can be displayed if the "Standard Deviation" setting is enabled. The example RSI, if enabled, is plotted along with its common threshold lines at 70 (upper) and 30 (lower). Understanding the Paytience Distribution Indicator 1. What is a Distribution? A distribution represents the spread of data points across different values, showing how frequently each value occurs. For instance, if you're looking at a stock's closing prices over a month, you may find that the stock closed most frequently around $100, occasionally around $105, and rarely around $110. Graphically visualizing this distribution can help you see the central tendencies, variability, and shape of your data distribution. This visualization can be essential in determining key trading points, understanding volatility, and getting an overview of the market sentiment. 2. The Rounding Mechanism Every asset and dataset is unique. Some assets, especially cryptocurrencies or forex pairs, might have values that go up to many decimal places. Rounding these values is essential to generate a more readable and manageable distribution. Why is Rounding Needed? If every unique value from a high-precision dataset was treated distinctly, the resulting distribution would be sparse and less informative. By rounding off, the values are grouped, making the distribution more consolidated and understandable. Adjusting Rounding: The `Round Source` input allows users to determine the number of decimal places they'd like to consider. If you're working with an asset with many decimal places, adjust this setting to get a meaningful distribution. If the rounding is set too low for high precision assets, the distribution could lose its utility. 3. Standard Deviation and Oscillators Standard deviation is a measure of the amount of variation or dispersion of a set of values. In the context of this indicator: Use with Oscillators: When using oscillators like RSI, the standard deviation can provide insights into the oscillator's range. This means you can determine how much the oscillator typically deviates from its average value. Setting Bounds: By understanding this deviation, traders can better set reasonable upper and lower bounds, identifying overbought or oversold conditions in relation to the oscillator's historical behavior. 4. Resampling Resampling is the process of adjusting the time frame or value buckets of your data. In the context of this indicator, resampling ensures that the distribution is manageable and visually informative. Resample Size vs. Window Size: The `Resample Resolution` dictates the number of bins or buckets the distribution will be divided into. On the other hand, the `Window Size` determines how much of the recent data will be considered. It's crucial to ensure that the resample size is smaller than the window size, or else the distribution will not accurately reflect the data's behavior. Why Use Resampling? Especially for price-based sources, setting the window size around 500 (instead of 0) ensures that the distribution doesn't become too overloaded with data. When set to 0, the window size uses all available data, which may not always provide an actionable insight. 5. Uneven Sample Bins and Gaps You might notice that the width of sample bins in the distribution is not uniform, and there can be gaps. Reason for Uneven Widths: This happens because the indicator uses a 'resampled' distribution. The width represents the range of values in each bin, which might not be constant across bins. Some value ranges might have more data points, while others might have fewer. Gaps in Distribution: Sometimes, there might be no data points in certain value ranges, leading to gaps in the distribution. These gaps are not flaws but indicate ranges where no values were observed. In conclusion, the Paytience Distribution indicator offers a robust mechanism to visualize the distribution of data from various sources. By understanding its intricacies, users can make better-informed trading decisions based on the distribution and behavior of their chosen data source.
Indicatore Pine Script™
di PaytienceIO
3
Label_Trades Enter your trade information to display on chartThis indicator is an overlay for your main chart. It will display your trade entry and trade close positions on your chart. After you place the indicator on you shart you will need to enter the trade information that you want to display. You can open thte input setting by clicking on the gear sprocket that appears when you hover your mouse over the indicator name. There are 7 seting you will want to fill in. Date and Time Bought Date and Time Sold Trade Lot Size Select whether the trades was 'long' or 'short' The price for buying the Trade The price for selling the Trade On the third tab The code is straightforward. Using a conditional based on whtehr the trade was 'long' or 'short' determines where the labels will be placed and whether they show a long trade or short trade. It also displays a tool tip when you hover over the label. The tooltip will display the number of lots bought or sold and the price. The lable.new() function is the meat of the indicator. I will go over a line to explainthe options available. Pinscript manual(www.tradingview.com) The function parameters can be called out as in the example above or the values can be placed comma seperated. If you do the latter you must enter the parameters in order. I like anming the parameters as I place them so I can easily see what I did. label.new( x=t_bot, // x is the time the transaction occured y=na, // y is the for the y-axis it is not used here so 'na' tells pinescript to ignore the parameter xloc=xloc.bar_time, // x_loc is specifying that x is a time value yloc=yloc.belowbar, // y-loc specifies to place the label under the bar. There are other locations to use. See language reference ((www.tradingview.com) style=label.style_triangleup, // This parameter selects the lable style. There are many other style to use, see the manual. color=color.green, // the Label fill color size=size.small, // the label size tooltip=str.tostring(lot_size) + " lots bought at $" + str.tostring(bot_val)) // Some parameters are tricky. This one needs to be a string but we are using an integer value(lot_size) and a float value(bol_val). They are all concatenated via the "+" sign. In oorder to do this the numeric values need to be cast or converted into strings. The string function str.tostring() does this.
Indicatore Pine Script™
di jryan3
Z-Score Based Momentum Zones with Advanced Volatility ChannelsThe indicator "Z-Score Based Momentum Zones with Advanced Volatility Channels" combines various technical analysis components, including volatility, price changes, and volume correction, to calculate Z-Scores and determine momentum zones and provide a visual representation of price movements and volatility based on multi timeframe highest high and lowest low values. Note: THIS IS A IMPROVEMNT OF "Multi Time Frame Composite Bands" INDICATOR OF MINE WITH MORE EMPHASIS ON MOMENTUM ZONES CALULATED BASED ON Z-SCORES Input Options look_back_length: This input specifies the look-back period for calculating intraday volatility. correction It is set to a default value of 5. lookback_period: This input sets the look-back period for calculating relative price change. The default value is 5. zscore_period: This input determines the look-back period for calculating the Z-Score. The default value is 500. avgZscore_length: This input defines the length of the momentum block used in calculations, with a default value of 14. include_vc: This is a boolean input that, if set to true, enables volume correction in the calculations. By default, it is set to false. 1. Volatility Bands (Composite High and Low): Composite High and Low: These are calculated by combining different moving averages of the high prices (high) and low prices (low). Specifically: a_high and a_low are calculated as the average of the highest (ta.highest) and lowest (ta.lowest) high and low prices over various look-back periods (5, 8, 13, 21, 34) to capture short and long-term trends. b_high and b_low are calculated as the simple moving average (SMA) of the high and low prices over different look-back periods (5, 8, 13) to smooth out the trends. high_c and low_c are obtained by averaging a_high with b_high and a_low with b_low respectively. IDV Correction Calulation : In this script the Intraday Volatility (IDV) is calculated as the simple moving average (SMA) of the daily high-low price range divided by the closing price. This measures how much the price fluctuates in a given period. Composite High and Low with Volatility: The final c_high and c_low values are obtained by adjusting high_c and low_c with the calculated intraday volatility (IDV). These values are used to create the "Composite High" and "Composite Low" plots. Composite High and Low with Volatility Correction: The final c_high and c_low values are obtained by adjusting high_c and low_c with the calculated intraday volatility (IDV). These values are used to create the "Composite High" and "Composite Low" plots. 2. Momentum Blocks Based on Z-Score: Relative Price Change (RPC): The Relative Price Change (rpdev) is calculated as the difference between the current high-low-close average (hlc3) and the previous simple moving average (psma_hlc3) of the same quantity. This measures the change in price over time. Additionally, std_hlc3 is calculated as the standard deviation of the hlc3 values over a specified look-back period. The standard deviation quantifies the dispersion or volatility in the price data. The rpdev is then divided by the std_hlc3 to normalize the price change by the volatility. This normalization ensures that the price change is expressed in terms of standard deviations, which is a common practice in quantitative analysis. Essentially, the rpdev represents how many standard deviations the current price is away from the previous moving average. Volume Correction (VC): If the include_vc input is set to true, volume correction is applied by dividing the trading volume by the previous simple moving average of the volume (psma_volume). This accounts for changes in trading activity. Volume Corrected Relative Price Change (VCRPD): The vcrpd is calculated by multiplying the rpdev by the volume correction factor (vc). This incorporates both price changes and volume data. Z-Scores: The Z-scores are calculated by taking the difference between the vcrpd and the mean (mean_vcrpd) and then dividing it by the standard deviation (stddev_vcrpd). Z-scores measure how many standard deviations a value is away from the mean. They help identify whether a value is unusually high or low compared to its historical distribution. Momentum Blocks: The "Momentum Blocks" are essentially derived from the Z-scores (avgZScore). The script assigns different colors to the "Fill Area" based on predefined Z-score ranges. These colored areas represent different momentum zones: Positive Z-scores indicate bullish momentum, and different shades of green are used to fill the area. Negative Z-scores indicate bearish momentum, and different shades of red are used. Z-scores near zero (between -0.25 and 0.25) suggest neutrality, and a yellow color is used.
Indicatore Pine Script™
di federalTacos5392b
Robust Bollinger Bands with Trend StrengthThe "Robust Bollinger Bands with Trend Strength" indicator is a technical analysis tool designed assess price volatility, identify potential trading opportunities, and gauge trend strength. It combines several robust statistical methods and percentile-based calculations to provide valuable information about price movements with Improved Resilience to Noise while mitigating the impact of outliers and non-normality in price data. Here's a breakdown of how this indicator works and the information it provides: Bollinger Bands Calculation: Similar to traditional Bollinger Bands, this indicator calculates the upper and lower bands that envelop the median (centerline) of the price data. These bands represent the potential upper and lower boundaries of price movements. Robust Statistics: Instead of using standard deviation, this indicator employs robust statistical measures to calculate the bands (spread). Specifically, it uses the Interquartile Range (IQR), which is the range between the 25th percentile (low price) and the 75th percentile (high price). Robust statistics are less affected by extreme values (outliers) and data distributions that may not be perfectly normal. This makes the bands more resistant to unusual price spikes. Median as Centerline: The indicator utilizes the median of the chosen price source (either HLC3 or VWMA) as the central reference point for the bands. The median is less affected by outliers than the mean (average), making it a robust choice. This can help identify the center of price action, which is useful for understanding whether prices are trending or ranging. Trend Strength Assessment: The indicator goes beyond the standard Bollinger Bands by incorporating a measure of trend strength. It uses a robust rank-based correlation coefficient to assess the relationship between the price source and the bar index (time). This correlation coefficient, calculated over a specified length, helps determine whether a trend is strong, positive (uptrend), negative (down trend), or non-existent and weak. When the rank-based correlation coefficient shifts it indicates exhaustion of a prevailing trend. Trend Strength" indicator is designed to provide statistically valid information about trend strength while minimizing the impact of outliers and data distribution characteristics. The parameter choices, including a length of 14 and a correlation threshold of +/-0.7, considered to offer meaningful insights into market conditions and statistical validity (p-value ,0.05 statistically significant). The use of rank-based correlation is a robust alternative to traditional Pearson correlation, especially in the context of financial markets. Trend Fill: Based on the robust rank-based correlation coefficient, the indicator fills the area between the upper and lower Bollinger Bands with different colors to visually represent the trend strength. For example, it may use green for an uptrend, red for a down trend, and a neutral color for a weak or ranging market. This visual representation can help traders quickly identify potential trend opportunities. In addition the middle line also informs about the overall trend direction of the median.
Indicatore Pine Script™
di federalTacos5392b
2
Cross Correlation [Kioseff Trading]Hello! This script "Cross Correlation" calculates up to ~10,000 lag-symbol pair cross correlation values simultaneously! Cross correlation calculation for 20 symbols simultaneously +/- Lag Range is theoretically infinite (configurable min/max) Practically, calculate up to 10000 lag-symbol pairs Results can be sorted by greatest absolute difference or greatest sum Ability to "isolate" the symbol on your chart and check for cross correlation against a list of symbols Script defaults to stock pairs when on a stock, Forex pairs when on a Forex pair, crypto when on a crypto coin, futures when on a futures contract. A custom symbol list can be used for cross correlation checking Can check any number of available historical data points for cross correlation Practical Assessment Ideally, we can calculate cross correlation to determine if, in a list of assets, any of the assets frequently lead or lag one another. Example Say we are comparing the log returns for the previous 10 days for SPY and XLU. *A single time-interval corresponds to the timeframe of your chart i.e. 1-minute chart = 1-minute time interval. We're using days for this example. (Example Results) A lag value (k) +/-3 is used. The cross correlation (normalized) for k = +3 is -0.787 The cross correlation (normalized) for k = -3 is 0.216 A positive "k" value indicates the correlation when Asset A (SPY) leads Asset B (XLU) A negative "k" value indicates the correlation when Asset B (XLU) leads Asset A (SPY) A normalized cross correlation of -0.787 for k = +3 indicates an "adequately strong" negative relationship when SPY leads XLU by 3 days. When SPY increases or decreases - XLU frequently moves in the opposite direction 3 days later. A cross correlation value of 0.216 at k = −3 indicates a "weak" positive correlation when XLU leads SPY by 3 days. There's a slight tendency for SPY to move in the same direction as XLU 3 days later. After the cross-correlation score is normalized it will fall between -1 and 1. A cross-correlation score of 1 indicates a perfect directional relationship between asset A and asset B at the corresponding lag (k). A cross correlation of -1 indicates a perfect inverse relationship between asset A and asset B at the corresponding lag (k). A cross correlation of 0 indicates no correlation at the corresponding lag (k). The image above shows the primary usage for the script! The image above further explains the data points located in the table! The image above shows the script "isolating" the symbol on my chart and checking the cross correlation between the symbol and a list of symbols! Wrapping Up With this information, hopefully you can find some meaningful lead-lag relationships amongst assets! Thank you for checking this out (:
Indicatore Pine Script™
di KioseffTrading
4
Seasonality by Scan Your StratOverview : This indicator helps with seasonality on the security. Seasonal analysis searches for repeating patterns across the years. Our recommended timeframe to look for seasonality is a minimum of 5 years. The idea is to see if there are predictable movements in price that recur every calendar year. How it works/Calculations : It will take all the years that you choose, whether 5 or 10 or 15 years or more and will analyze the average movement by calendar year and then will detrend the results to give you a chart that goes horizontally and makes you see a clearer picture of periods of strengths or periods of weakness. It will take the ROC of each day for each year and average the results and at the end will give you a chart with line that can show uptrend or downtrend. Potential Pitfalls : Certain events of that year can affect the movement for that security more than a “normal” year would. In addition, there are similar price action moves that can happen across several years that have no true seasonal basis. For example, company news released coincidentally at the same time of the year over several years and can lead to show a seasonal pattern when there is not one. You can battle this by using more years like 10 or 15 years or by eliminating years from being analyze. On the other hand, using too many years (like distant past) may have little to do with today's price action and seasonal trends. How to use : You should not be using this indicator for entries or stop. This indicator will help you identify potential periods of strength and may help you in holding longer. Sometimes seasonal periods can start sooner or later to the chart. Very important, the indicator will work only on daily candle as the smallest timeframe. Settings : - Start year and end year : to put years where you want to start your analysis and where it ends. For example, you can put 2013 as a start and 2022 year end. *”Use years back” will need to be at 0 for this option to work. - Use years back : you can put directly 10 years and will analyze 2013 to 2022. If there is a number in here like 5, will mean the last 5 years and will trump any years on the above section. "0" means is deactivated and you can use start year and end year options. - Avoid year - there are 3 spots if you wanted to avoid certain years in a series. For example, lets say you want to analyze the last 10 years but you want to eliminate 2020 due to covid then you can put in here up to 3 different years that will not be taken into account. - Start month and end month : it will be automatic at start 1 and end 12, but if you wanted to just see a specific timeframe you can adjust in here. Disclaimer : This is still an indicator that is being tested and in no way should be used alone. Currently will be in closed beta to find bugs and to work on accuracy. The information contained in this script 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 are only for educational purposes!
Indicatore Pine Script™
di ScanYourStrat