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NormalizedOscillatorsLibrary "NormalizedOscillators" Collection of some common Oscillators. All are zero-mean and normalized to fit in the -1..1 range. Some are modified, so that the internal smoothing function could be configurable (for example, to enable Hann Windowing, that John F. Ehlers uses frequently). Some are modified for other reasons (see comments in the code), but never without a reason. This collection is neither encyclopaedic, nor reference, however I try to find the most correct implementation. Suggestions are welcome. rsi2(upper, lower) RSI - second step   Parameters:      upper : Upwards momentum      lower : Downwards momentum   Returns: Oscillator value Modified by Ehlers from Wilder's implementation to have a zero mean (oscillator from -1 to +1) Originally: 100.0 - (100.0 / (1.0 + upper / lower)) Ignoring the 100 scale factor, we get: upper / (upper + lower) Multiplying by two and subtracting 1, we get: (2 * upper) / (upper + lower) - 1 = (upper - lower) / (upper + lower) rms(src, len) Root mean square (RMS)   Parameters:      src : Source series      len : Lookback period Based on by John F. Ehlers implementation ift(src) Inverse Fisher Transform   Parameters:      src : Source series   Returns: Normalized series Based on by John F. Ehlers implementation The input values have been multiplied by 2 (was "2*src", now "4*src") to force expansion - not compression The inputs may be further modified, if needed stoch(src, len) Stochastic   Parameters:      src : Source series      len : Lookback period   Returns: Oscillator series ssstoch(src, len) Super Smooth Stochastic (part of MESA Stochastic) by John F. Ehlers   Parameters:      src : Source series      len : Lookback period   Returns: Oscillator series Introduced in the January 2014 issue of Stocks and Commodities This is not an implementation of MESA Stochastic, as it is based on Highpass filter not present in the function (but you can construct it) This implementation is scaled by 0.95, so that Super Smoother does not exceed 1/-1 I do not know, if this the right way to fix this issue, but it works for now netKendall(src, len) Noise Elimination Technology by John F. Ehlers   Parameters:      src : Source series      len : Lookback period   Returns: Oscillator series Introduced in the December 2020 issue of Stocks and Commodities Uses simplified Kendall correlation algorithm Implementation by @QuantTherapy: rsi(src, len, smooth) RSI   Parameters:      src : Source series      len : Lookback period      smooth : Internal smoothing algorithm   Returns: Oscillator series vrsi(src, len, smooth) Volume-scaled RSI   Parameters:      src : Source series      len : Lookback period      smooth : Internal smoothing algorithm   Returns: Oscillator series This is my own version of RSI. It scales price movements by the proportion of RMS of volume mrsi(src, len, smooth) Momentum RSI   Parameters:      src : Source series      len : Lookback period      smooth : Internal smoothing algorithm   Returns: Oscillator series Inspired by RocketRSI by John F. Ehlers (Stocks and Commodities, May 2018) rrsi(src, len, smooth) Rocket RSI   Parameters:      src : Source series      len : Lookback period      smooth : Internal smoothing algorithm   Returns: Oscillator series Inspired by RocketRSI by John F. Ehlers (Stocks and Commodities, May 2018) Does not include Fisher Transform of the original implementation, as the output must be normalized Does not include momentum smoothing length configuration, so always assumes half the lookback length mfi(src, len, smooth) Money Flow Index   Parameters:      src : Source series      len : Lookback period      smooth : Internal smoothing algorithm   Returns: Oscillator series lrsi(src, in_gamma, len) Laguerre RSI by John F. Ehlers   Parameters:      src : Source series      in_gamma : Damping factor (default is -1 to generate from len)      len : Lookback period (alternatively, if gamma is not set)   Returns: Oscillator series The original implementation is with gamma. As it is impossible to collect gamma in my system, where the only user input is length, an alternative calculation is included, where gamma is set by dividing len by 30. Maybe different calculation would be better? fe(len) Choppiness Index or Fractal Energy   Parameters:      len : Lookback period   Returns: Oscillator series The Choppiness Index (CHOP) was created by E. W. Dreiss This indicator is sometimes called Fractal Energy er(src, len) Efficiency ratio   Parameters:      src : Source series      len : Lookback period   Returns: Oscillator series Based on Kaufman Adaptive Moving Average calculation This is the correct Efficiency ratio calculation, and most other implementations are wrong: the number of bar differences is 1 less than the length, otherwise we are adding the change outside of the measured range! For reference, see Stocks and Commodities June 1995 dmi(len, smooth) Directional Movement Index   Parameters:      len : Lookback period      smooth : Internal smoothing algorithm   Returns: Oscillator series Based on the original Tradingview algorithm Modified with inspiration from John F. Ehlers DMH (but not implementing the DMH algorithm!) Only ADX is returned Rescaled to fit -1 to +1 Unlike most oscillators, there is no src parameter as DMI works directly with high and low values fdmi(len, smooth) Fast Directional Movement Index   Parameters:      len : Lookback period      smooth : Internal smoothing algorithm   Returns: Oscillator series Same as DMI, but without secondary smoothing. Can be smoothed later. Instead, +DM and -DM smoothing can be configured doOsc(type, src, len, smooth) Execute a particular Oscillator from the list   Parameters:      type : Oscillator type to use      src : Source series      len : Lookback period      smooth : Internal smoothing algorithm   Returns: Oscillator series Chande Momentum Oscillator (CMO) is RSI without smoothing. No idea, why some authors use different calculations LRSI with Fractal Energy is a combo oscillator that uses Fractal Energy to tune LRSI gamma, as seen here: www.prorealcode.com doPostfilter(type, src, len) Execute a particular Oscillator Postfilter from the list   Parameters:      type : Oscillator type to use      src : Source series      len : Lookback period   Returns: Oscillator series
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lastguruの投稿
Bitcoin Power Law Bands (BTC Power Law) Indicator█  OVERVIEW The 'Bitcoin Power Law Bands' indicator is a set of three US dollar price trendlines and two price bands for bitcoin , indicating overall long-term trend, support and resistance levels as well as oversold and overbought conditions. The magnitude and growth of the middle (Center) line is determined by double logarithmic (log-log) regression on the entire USD price history of bitcoin . The upper (Resistance) and lower (Support) lines follow the same trajectory but multiplied by respective (fixed) factors. These two lines indicate levels where the price of bitcoin is expected to meet strong long-term resistance or receive strong long-term support. The two bands between the three lines are price levels where bitcoin may be considered overbought or oversold. All parameters and visuals may be customized by the user as needed. █  CONCEPTS Long-term models Long-term price models have many challenges, the most significant of which is getting the growth curve right overall. No one can predict how a certain market, asset class, or financial instrument will unfold over several decades. In the case of bitcoin , price history is very limited and extremely volatile, and this further complicates the situation. Fortunately for us, a few smart people already had some b​right ideas that seem to have stood the test of time. Power law The so-called power law is the only long-term bitcoin price model that has a chance of survival for the years ahead. The idea behind the power law is very simple: over time, the rapid (exponential) initial growth cannot possibly be sustained (see The seduction of the exponential curve for a fun take on this). Year-on-year returns, therefore, must decrease over time, which leads us to the concept of diminishing returns and the power law. In this context, the power law translates to linear growth on a chart with both its axes scaled logarithmically. This is called the log-log chart (as opposed to the semilog chart you see above, on which only one of the axes - price - is logarithmic). Log-log regression When both price and time are scaled logarithmically, the power law leads to a linear relationship between them. This in turn allows us to apply linear regression techniques, which will find the best-fitting straight line to the data points in question. The result of performing this log-log regression (i.e. linear regression on a log-log scaled dataset) is two parameters: slope (m) and intercept (b). These parameters fully describe the relationship between price and time as follows: log(P) = m * log(T) + b, where P is price and T is time. Price is measured in US dollars , and Time is counted as the number of days elapsed since bitcoin 's genesis block. DPC model The final piece of our puzzle is the Dynamic Power Cycle (DPC) price model of bitcoin . DPC is a long-term cyclic model that uses the power law as its foundation, to which a periodic component stemming from the block subsidy halving cycle is applied dynamically. The regression parameters of this model are re-calculated daily to ensure longevity. For the 'Bitcoin Power Law Bands' indicator, the slope and intercept parameters were calculated on publication date (March 6, 2022). The slope of the Resistance Line is the same as that of the Center Line; its intercept was determined by fitting the line onto the Nov 2021 cycle peak. The slope of the Support Line is the same as that of the Center Line; its intercept was determined by fitting the line onto the Dec 2018 trough of the previous cycle. Please see the Limitations section below on the implications of a static model. █  FEATURES Inputs  • Parameters    • Center Intercept (b) and Slope (m): These log-log regression parameters control the behavior of the grey line in the middle    • Resistance Intercept (b) and Slope (m): These log-log regression parameters control the behavior of the red line at the top    • Support Intercept (b) and Slope (m): These log-log regression parameters control the behavior of the green line at the bottom  • Controls    • Plot Line Fill: N/A    • Plot Opportunity Label: Controls the display of current price level relative to the Center, Resistance and Support Lines Style  • Visuals    • Center: Control, color, opacity, thickness, price line control and line style of the Center Line    • Resistance: Control, color, opacity, thickness, price line control and line style of the Resistance Line    • Support: Control, color, opacity, thickness, price line control and line style of the Support Line    • Plots Background: Control, color and opacity of the Upper Band    • Plots Background: Control, color and opacity of the Lower Band    • Labels: N/A  • Output    • Labels on price scale: Controls the display of current Center, Resistance and Support Line values on the price scale    • Values in status line: Controls the display of current Center, Resistance and Support Line values in the indicator's status line █  HOW TO USE The indicator includes three price lines:  • The grey Center Line in the middle shows the overall long-term bitcoin USD price trend  • The red Resistance Line at the top is an indication of where the bitcoin USD price is expected to meet strong long-term resistance  • The green Support Line at the bottom is an indication of where the bitcoin USD price is expected to receive strong long-term support These lines e​nvelope two price bands:  • The red Upper Band between the Center and Resistance Lines is an area where bitcoin is considered overbought (i.e. too expensive)  • The green Lower Band between the Support and Center Lines is an area where bitcoin is considered oversold (i.e. too cheap) The power law model assumes that the price of bitcoin will fluctuate around the Center Line, by meeting resistance at the Resistance Line and finding support at the Support Line. When the current price is well below the Center Line (i.e. well into the green Lower Band), bitcoin is considered too cheap (oversold). When the current price is well above the Center Line (i.e. well into the red Upper Band), bitcoin is considered too expensive (overbought). This idea alone is not sufficient for profitable trading, but, when combined with other factors, it could guide the user's decision-making process in the right direction. █  LIMITATIONS The indicator is based on a static model, and for this reason it will gradually lose its usefulness. The Center Line is the most durable of the three lines since the long-term growth trend of bitcoin seems to deviate little from the power law. However, how far price extends above and below this line will change with every halving cycle (as can be seen for past cycles). Periodic updates will be needed to keep the indicator relevant. The user is invited to adjust the slope and intercept parameters manually between two updates of the indicator. █  RAMBLINGS The 'Bitcoin Power Law Bands' indicator is a useful tool for users wishing to place bitcoin in a macro context. As described above, the price level relative to the three lines is a rough indication of whether bitcoin is over- or undervalued. Users wishing to gain more insight into bitcoin price trends may follow the author's periodic updates of the DPC model (contact information below). █  NOTES The author regularly posts on Twitter using the @DeFi_initiate handle. █  THANKS Many thanks to the following individuals, who - one way or another - made the 'Bitcoin Power Law Bands' indicator possible:  • TradingView user 'capriole_charles', whose open-source 'Bitcoin Power Law Corridor' script was the basis for this indicator  • Harold Christopher Burger, whose Bit​coin’s natural long-term power-law corridor of growth article (2019) was the basis for the 'Bitcoin Power Law Corridor' script  • Bit​coin Forum user "Trololo", who posted the original power law model at Logarithmic (non-linear) regression - Bit​coin estimated value (2014)
Pine Script® インジケーター
DeFi_initiateの投稿
10
Bitcoin Risk Metric IIThesis: Bitcoin's price movements can be (dubiously) characterized by functional relationships between moving averages and standard deviations. These movements can be normalized into a risk metric through normalization functions of time. This risk metric may be able to quantify a long term "buy low, sell high" strategy. This risk metric is the average of three normalized metrics: 1. (btc - 4 yma)/ (std dev) 2. ln(btc / 20 wma) 3. (50 dma)/(50 wma) * btc = btc price * yma = yearly moving average of btc, wma = weekly moving average of btc, dma = daily moving average of btc * std dev = std dev of btc Important note: Historical data for this metric is only shown back until 2014, because of the nature of the 1st mentioned metric. The other two metrics produce a value back until 2011. A previous, less robust, version of metric 2 is posted on my TradingView as well.
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oakleywoodの投稿
6
How Old Is this Bull Run Getting? Check MA Test Bars SinceThere are many price-based techniques for anticipating the end of a move. However, the simple passage of time can also help because bull markets don’t last forever. While old age doesn’t necessarily cause investors to sell, a reversal becomes more likely the longer a trend lasts. So, how long have prices been going up? There are various ways to measure that. Our earlier script, MA streak , offered one solution by counting the number of bars that a given moving average has been rising or falling. Today’s script takes a different approach by counting the number of candles since price touched or crossed a given moving average. It tracks the 50-day simple moving average (SMA) by default. It can be adjusted to other types like exponential and weighted with the AvgType input. In the chart above, Bars Since MA Test was adjusted to use the 200-day SMA. Viewing the S&P 500 with this study helps put the current market into context. We can see that prices last touched the 200-day SMA 386 sessions ago (June 29, 2020). That’s relatively long based on history, but not unprecedented. For example, the indicator was at 407 in February 2018 as the market pulled back. It also hit 475 in October 2014 (following the breakout above 2007 highs). Additionally, the S&P 500 is nearing the record of the 1990s bull market (393 candles on July 12, 1996). Before that, you have to look all the way back to the 1950s, when it twice peaked at 627. The conclusion? The current run without a test of the 200-day SMA is above average, but not yet record-setting. It may be interesting to watch as earnings season approaches and the Federal Reserve looks to tighten monetary policy. TradeStation is a pioneer in the trading industry, providing access to stocks, options, futures and cryptocurrencies. See our Overview for more. Important Information TradingView is not affiliated with TradeStation Securities Inc. or its affiliates. TradeStation Securities, Inc., TradeStation Crypto, Inc., and TradeStation Technologies, Inc. are each wholly owned subsidiaries of TradeStation Group, Inc., all operating, and providing products and services, under the TradeStation brand and trademark. When applying for, or purchasing, accounts, subscriptions, products and services, it is important that you know which company you will be dealing with. Please click here for further important information explaining what this means. This content is for informational and educational purposes only. This is not a recommendation regarding any investment or investment strategy. Any opinions expressed herein are those of the author and do not represent the views or opinions of TradeStation or any of its affiliates. Investing involves risks. Past performance, whether actual or indicated by historical tests of strategies, is no guarantee of future performance or success. There is a possibility that you may sustain a loss equal to or greater than your entire investment regardless of which asset class you trade (equities, options, futures, or digital assets); therefore, you should not invest or risk money that you cannot afford to lose. Before trading any asset class, first read the relevant risk disclosure statements on the Important Documents page, found here: www.tradestation.com .
Pine Script® インジケーター
TradeStationの投稿
2
Ripple (XRP) Model PriceAn article titled Bitcoin Stock-to-Flow Model was published in March 2019 by "PlanB" with mathematical model used to calculate Bitcoin model price during the time. We know that Ripple has a strong correlation with Bitcoin. But does this correlation have a definite rule? In this study, we examine the relationship between bitcoin's stock-to-flow ratio and the ripple(XRP) price. The Halving and the stock-to-flow ratio Stock-to-flow is defined as a relationship between production and current stock that is out there. SF = stock / flow The term "halving" as it relates to Bitcoin has to do with how many Bitcoin tokens are found in a newly created block. Back in 2009, when Bitcoin launched, each block contained 50 BTC, but this amount was set to be reduced by 50% every 210,000 blocks (about 4 years). Today, there have been three halving events, and a block now only contains 6.25 BTC. When the next halving occurs, a block will only contain 3.125 BTC. Halving events will continue until the reward for minors reaches 0 BTC. With each halving, the stock-to-flow ratio increased and Bitcoin experienced a huge bull market that absolutely crushed its previous all-time high. But what exactly does this affect the price of Ripple? Price Model I have used Bitcoin's stock-to-flow ratio and Ripple's price data from April 1, 2014 to November 3, 2021 (Daily Close-Price) as the statistical population. Then I used linear regression to determine the relationship between the natural logarithm of the Ripple price and the natural logarithm of the Bitcoin's stock-to-flow (BSF). You can see the results in the image below: Basic Equation : ln(Model Price) = 3.2977 * ln(BSF) - 12.13 The high R-Squared value (R2 = 0.83) indicates a large positive linear association. Then I "winsorized" the statistical data to limit extreme values to reduce the effect of possibly spurious outliers (This process affected less than 4.5% of the total price data). ln(Model Price) = 3.3297 * ln(BSF) - 12.214 If we raise the both sides of the equation to the power of e, we will have: ============================================ Final Equation: ■ Model Price = Exp(- 12.214) * BSF ^ 3.3297 Where BSF is Bitcoin's stock-to-flow ============================================ If we put current Bitcoin's stock-to-flow value (54.2) into this equation we get value of 2.95USD. This is the price which is indicated by the model. There is a power law relationship between the market price and Bitcoin's stock-to-flow (BSF). Power laws are interesting because they reveal an underlying regularity in the properties of seemingly random complex systems. I plotted XRP model price (black) over time on the chart. Estimating the range of price movements I also used several bands to estimate the range of price movements and used the residual standard deviation to determine the equation for those bands. Residual STDEV = 0.82188 ln(First-Upper-Band) = 3.3297 * ln(BSF) - 12.214 + Residual STDEV => ln(First-Upper-Band) = 3.3297 * ln(BSF) – 11.392 => ■ First-Upper-Band = Exp(-11.392) * BSF ^ 3.3297 In the same way: ■ First-Lower-Band = Exp(-13.036) * BSF ^ 3.3297 I also used twice the residual standard deviation to define two extra bands: ■ Second-Upper-Band = Exp(-10.570) * BSF ^ 3.3297 ■ Second-Lower-Band = Exp(-13.858) * BSF ^ 3.3297 These bands can be used to determine overbought and oversold levels. Estimating of the future price movements Because we know that every four years the stock-to-flow ratio, or current circulation relative to new supply, doubles, this metric can be plotted into the future. At the time of the next halving event, Bitcoins will be produced at a rate of 450 BTC / day. There will be around 19,900,000 coins in circulation by August 2025 It is estimated that during first year of Bitcoin (2009) Satoshi Nakamoto (Bitcoin creator) mined around 1 million Bitcoins and did not move them until today. It can be debated if those coins might be lost or Satoshi is just waiting still to sell them but the fact is that they are not moving at all ever since. We simply decrease stock amount for 1 million BTC so stock to flow value would be: BSF = (19,900,000 – 1.000.000) / (450 * 365) =115.07 Thus, Bitcoin's stock-to-flow will increase to around 115 until AUG 2025. If we put this number in the equation: Model Price = Exp(- 12.214) * 114 ^ 3.3297 = 36.06$ Ripple has a fixed supply rate. In AUG 2025, the total number of coins in circulation will be about 56,000,000,000. According to the equation, Ripple's market cap will reach $2 trillion. Note that these studies have been conducted only to better understand price movements and are not a financial advice.
Pine Script® インジケーター
BobRivera990の投稿
10
Indicator : Financial Table■ What is Financial Table? Financial table is the table shows the finanacial data over period of time (Quartery : FQ, Annually : FY). These incluse 3 tables, 1) Income Statement (Revenue, Net Profit (or Net Income) and EPS) . 2) Balance Sheet (Current Asset, Total Asset, Liabilites and Share Holder's Equity). 3) Cash Flow ( Cash Flow from Operating Activities, Investment, Financing and Free Cash Flow) This data will allow us to get understanding of the status of a company financial status over time. ■ How to make it? 1) Get Financial Data 2) Decare array 3) Store the array if conditions are met. 4) Generate table ■ How to use? 1) You can select the report period : FQ (Quarterly) or FY (Annually). 2) You can also select the data to plot (Revenue, Net Profit and EPS). 3) Select how many quarter or year you want to get (It is available from 2014 only). 4) Customize text size and position of the table as you wish. I'm new and appriciate your suggestion.
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morzor61の投稿
4
Williams Vix Fix + BB & RVI (Top/Bottom) & SqueezeLegend : - When line touches or crosses red band it is Top signal (Williams Vix Fix) - When line touches or crosses blue band it is Bottom signal (Williams Vix Fix) - Red dot at the top of indicator is a Top signal (Relative Volatility Index) - Blue dot at the top of indicator is a Bottom signal (Relative Volatility Index) - Gray dot at the bottom of indicator is a Squeeze signal This is an attempt to make use of the main features of all 4 of these very popular Volatility tools : - Williams Vix Fix + Bollinger Bands (as per Larry Williams idea, link ) - Relative Volatility Index (RVI) - The crossing of Keltner Channel by the Bollinger Bands (Squeeze) The goal is to find the best tool to find bottoms and top relative to volatility . This is a simple combination, but I find it very useful personally (no need to reinvent the wheel, just need to find what works best) The idea is that Williams Vix Fix + Bollinger Bands already give the main volatility bottom and top (Bottom are more accurate). So instead of trying to modify it, I chose to compliment it by mapping with points when the Relative Volatility Index (RVI) reached the top/bottom thresholds (red dot means top and blue dot means bottom). That way we can easily see when both indicators find a top or bottom relative to volatility (of course this needs to be then confirmed with a momentum indicator rally). In addition, I added the squeeze because this quickly shows the potential breakouts. For ideas on how to continue this work, it would be very interesting to be able to create a probability of a bottom and top relative to volatility using the Williams Vix Fix + Bollinger Bands and "Relative Volatility Index" signals as both work well and give top or bottom the other doesn't see.
Pine Script® インジケーター
KevInnerTradingの投稿
5
Stochastic based on Closing Prices - Identify and Rank TrendsStochClose is a trend indicator that can be used on its own to measure trend strength, in a scan to rank a group of securities according to trend strength or as part of a trend following strategy. Moreover, it acts as a volatility-adjusted trend indicator that puts securities on an equal footing. StochClose measures the location of the current close relative to the close-only high-low range over a given period of time. In contrast to the traditional Stochastic Oscillator, this indicator only uses closing prices. Traditional Stochastic uses intraday highs and lows to calculate the range. The focus on closing prices reduces signal noise caused by intraday highs and lows, and filters out errant or irrationally exuberant price spikes. Here are some examples when the high or low was out of proportion and suspect. Perhaps most famously, there were errant spike lows in dozens of ETFs in August 2015 (XLK, IJR, ITB). There were other spikes in VMBS (October 2014), IJR (October 2008) and KRE (May 2011). Elsewhere, there were suspicious spikes in IEI (April 2020), CHD (March 2020), CCRN (March 2020) and FNB (March 2020) The preferred setting to identify medium and long-term uptrends is 125 days with 5 days smoothing. 125 days covers around six months. Thus, StochClose(125,5) is a 5-day SMA of the 125-day Stochastic based on closing prices. Smoothing with the 5-day SMA introduces a little lag, but reduces whipsaws and signal noise. StochClose fluctuates between 0 and 100 with 50 as the midpoint. Values above 80 indicate that the current price is near the high end of the 125-day range, while values below 20 indicate that price is near the low end of the range. For signals, a move above 60 puts the indicator firmly in the top half of the range and points to an uptrend. A move below 40 puts the indicator firmly in the bottom half of the range and points to a downtrend. StochClose values can also be ranked to separate the leaders from the laggards. In contrast to Rate-of-Change and Percentage Above/Below a Moving Average, StochClose acts as a volatility-adjusted indicator that can identify trend strength or weakness. The Consumer Staples SPDR is unlikely to win in a Rate-of-Change contest with the Technology SPDR. However, it is just as easy for the Consumer Staples SPDR to get in the top of its range as it is for the Technology SPDR. StochClose puts securities on an equal footing. StochClose measures trend direction and trend strength with one number. The indicator value tells us immediately if the security is trending higher or lower. Furthermore, we can compare this value against the values for other securities. Securities with higher StochClose values are stronger than those with lower values.
Pine Script® インジケーター
trendinvestorproの投稿
2
Volatility StopThis is a new version of the classic Volatility Stop originally published in 2014 by admin and written in Pine v1. While the code has evolved, its logic is identical. It is an ATR-based trend detector that can also be used as a stop. It belongs to the same family of indicators as: • Charles Le Beau's Chandelier Exit , • Olivier Seban's Super Trend , and • Sylvain Vervoort's Average True Range Trailing Stop . Unlike the Chandelier Exit , Volatility Stop will not move against the trend. This new version is written in Pine v4. The indicator can be used as a chart overlay, like the original. The calculations have been functionalized for easier reuse, so it is now easier to lift the logic out of the script and use it in others. Features • Choice of 2 color themes. • Choice of display as a line, circles, diamonds or arrows. The line can be used with the other shapes. If no line is required, set its thickness to zero. • Same default of length=20 and ATR factor=2 used in the original Volatility Stop. • 3 alerts: on any trend change, or on changes into up or downtrends only. Alerts should be configured to trigger Once Per Bar Close . Original version: Look first. Then leap.
Pine Script® インジケーター
TradingViewの投稿
21
Donchian Channels Alert SystemThis time I was using an script/indicator that was originally written by ChrisMoody on 12-14-2014, as turned it an Alert System to know whether the price breaks above or below the donchian channel of 20 periods. I would like to show you how easy is to use it and configure it to receive this alerts in your phone. Here's a page about what Donchian Channels are for the ones that don't know already: www.investopedia.com This simple indicator prints a green alert when the close price crosses above the upper band of the DC, and a red alert when the price crosses the lower band of the DC. When these conditions are met, the indicator throws an alertcondition signal. You can personalize your Trading View alerts using this information. Create new alert, In the condition select "DC Alert System" and below select "Break Above" or "Break Below" depending on the case you're looking for. Save the Alert and voila. Simply as that. The Donchian Channel period is set to 20 by default, but you can update it to what suit best for you. Hope this is a +1 to your trade alert system. Have a good day! If you like this script please give me a like and comment below.
Pine Script® インジケーター
rodrigo.aprietoの投稿
7
Murreys Math Lines Box OR Ratio PivotsI'm publishing my second script, though nothing extraordinary, I believe there is user group for Murry Math indies and the only "proper one" (According to my usage) I found was of RicardoSantos, here is the link : He developed that script in 2014 and it is in need of update to Pine V4 and I'm doing the needful as its user. All the updates from my end are listed below: 1. Updated to Pine V4 2. Automatic octave selection 3. In auto mode one can switch octave 4. This script is color coded with intention of use on dark theme, one can change the colors to use it on white background with simple few clicks as pinelines have been used Other thing I want to add is that usage of this is not very clear to many users, so I'll do little explaining here; Lets start with what is Octave? Octave is basically distance between square of two whole numbers, this is hard-fast method to calculate, Murry has made it far more complicated to use practically. In mathematical formula terms it could be something like this for script trading at 11890 (CMP) Step 1: Square Root of CMP i.e Square Root of 11890 = 109.041 = Rounded to 109 Step 2: You can either take one whole number higher or lower than 109, which is 108 or 110. We will take 108 Step 3: Square of 108 = 11664 and Square of 109 = 11881 Step 4: Octave => Distance between (Lowlevel) 11664 and (Higherlevel) 11881 I've automated it so you don't need to calculate, but there is also manual entry possible if you want to calculate octaves yourself, there are different ways to calculate and some like to just take High and Low's of the day or week or month, whatever you like. When I used it I did it strictly this way, so automation is based on it. This is very subjective matter so don't ask to change the calculation of this, if I started doing that every second person would ask me to modify it to different calculation..and thats...just not possible to do. This is output for calculation we just did above This is octave shift option (Which basically shifts to next whole number square in above calculation) Normal nomenclature on octaves and important color codes +2/8: Extreme overbought = Blue Color and solid line +1/8: OverBought 8/8: Hardest line to rise above (overbought) = White Color and solid line 7/8: Fast reverse line (weak) 6/8: Pivot reverse line = Yellow Color and solid line 5/8: Upper trading range ---------------------------------------- 4/8: Major reversal line = Green Color and solid line ---------------------------------------- 3/8: Lower trading range 2/8: Pivot reverse line = Blue Color and solid line 1/8: Fast reverse line (weak) 0/8: Hardest line to fall below (oversold) = White Color and solid line -1/8: Oversold -2/8: Extreme Oversold = Yellow Color and solid line Other lines that I've not mentioned color codes for are minor and are usually plotted in dotted format. Resources on complete technique to trade and importance of levels (highly recommended to read carefully before trading), if you don't know how to get this for free don't worry you can just google Murrey math and you will find it somewhere, its just that it would be in little scattered manner. www.scribd.com Enjoy!
Pine Script® インジケーター
Pratik_4Cloverの投稿
14
Easy to Use Stochastic + RSI StrategyA simple strategy that yields some great results. CODE VARIABLES LINE 2 - Here you can change your currency and amount you want to invest on each entry. LINE 10/11/12 - Here we establish what date we want to start backtesting from. Simply change the defval on each line to change the date (In the code below we start on Jan 1st, 2014). LINES 19 through 27 - Here we set our Stochastic and RSI sensitivity (Currently %K = 14, %D = 3, RSI = 14). Change these to your preference. LINE 39/41 - Here we execute our orders (Currently set when %K crosses %D under the 20 value and RSI is less than 50 to BUY, %K crosses %D above the 80 value and RSI is greater than 60 to SELL). Change these to your preference. NOTE: As a beginner you may not want to short stock, therefore LINE 6 was added to only allow long positions. I didn't overlay the RSI value over the Stochastics because it was too cluttered. Just add the RSI indictor seperately to your layout. As always, couple this with trend following and exit/entry rules to make the profitability even higher! Cheers!
Pine Script® ストラテジー
TrendsAreSexyの投稿
1
Golden Ratio Macro Top IndicatorsThis is inspired by Philip Swift's Golden Ratio Multiplier research however it uses the 300 DMA to predict the Macro Cycle Top's Price. It still uses the 350 DMA * 2 and 111 DMA to predict the top's date (the two cross). 111 DMA (Orange) crosses the 350 DMA * 2 (Green) predicts the Macro Cycle Top Date 300 DMA * 3 (Red) predicts the Current Macro Cycle Top Price 300 DMA * 5 (Yellow) predicted the 2018 Macro Cycle Top Price 300 DMA * 8 (Blue) predicted the 2014 Macro Cycle Top Price
Pine Script® インジケーター
timgrahamの投稿
3
Golden RatioThis is inspired by Philip Swift's Golden Ratio Multiplier research however it uses the 300 DMA to predict the Macro Cycle Top's Price. It still uses the 350 DMA * 2 and 111 DMA to predict the top's date (the two cross). 111 DMA (Orange) crosses the 350 DMA * 2 (Green)= Macro Cycle Top Date 300 DMA * 3 (Red) predicts the Current Macro Cycle Top Price 300 DMA * 5 (Yellow) predicted the 2018 Macro Cycle Top Price 300 DMA * 8 (Blue) predicted the 2014 Macro Cycle Top Price
Pine Script® インジケーター
timgrahamの投稿
Bryant Adaptive Moving Average@ChartArt got my attention to this idea. This type of moving average was originally developed by Michael R. Bryant (Adaptrade Software newsletter, April 2014). Mr. Bryant suggested a new approach, so called Variable Efficiency Ratio (VER), to obtain adaptive behaviour for the moving average. This approach is based on Perry Kaufman' idea with Efficiency Ratio (ER) which was used by Mr. Kaufman to create KAMA. As result Mr. Bryant got a moving average with adaptive lookback period. This moving average has 3 parameters: Initial lookback Trend Parameter Maximum lookback The 2nd parameter, Trend Parameter can take any positive or negative value and determines whether the lookback length will increase or decrease with increasing ER. Changing Trend Parameter we can obtain KAMA' behaviour To learn more see www.adaptrade.com
Pine Script® インジケーター
evergetの投稿
1
Donchian Mean Reversion AlertsDonchian Channels mean reversion signals/alerts with RSI filtering with signals applied to the mean, for strong trend situations. Original Author: ChrisMoody Modified Donchonian Channel with separate adjustments for upper and lower levels Purpose is if you expect big move up, Use lower input example 3 or 4, and longer lower input, 40 - 100 and use lower input line as a stop out Opposite if you expect big move down Mid Line Rule in Long Example. If lower line is below entry take partial profits at Mid Line and move stop to Break even. If Lower line moves above entry price before price retraces to midline use Lower line as Stop...Opposite if Shorting Created by user ChrisMoody 1-30-2014 Updated 7-11-2018 by Dysrupt Revamped for mean reversion strategy Created midline alerts for strong trending upside buy signals/downside sell signals. Added RSI Filtering Added Alerts Removed bar color change
Pine Script® インジケーター
dysruptの投稿
4
Ice Yolo RSI Public//Created By ChrisMoody on 8/15/2014 //Uploaded By Iceberg on 6/13/2018 //This script allovvs you to set up to 3 different length RSI at the same time. I usually use tvvo. //Chris removed his script and someone asked mine. I simplified a lot of lines. //This script shovvs highlighted background vvhen your First RSI is in oversold or overbought zone, and flashes vvhen //it crosses the lines. You can use it to check crossings or bounces of different lengths too. //The settings highly depends on your Timeframe AND trading style. So make sure they match you as you like. I don't recommend using //the default ones! Thanks for using, and thanks again Chris Moody!
Pine Script® インジケーター
icebergREALの投稿