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VOLQ Sigma TableThis indicator replaces the implied volatility of VOLQ with the daily volatility and reflects that value into the price on the NDX chart to create the VOLQ standard deviation table. It will only be useful for stocks related to the Nasdaq Index. For example, NDX, QQQ or so. And we want to predict the range of weekly fluctuations by plotting those values ​​as a line in the future. It is expressed as High 2σ by adding the standard deviation 2 sigma value of the VOLQ value from last week's closing price. It is expressed as High 1σ by adding the standard deviation 1 sigma value of the VOLQ value from last week's closing price. It is expressed as Low 1σ by subtracting the standard deviation 1 sigma value of the VOLQ value from the closing price of the previous week. It is expressed as Low 2σ by subtracting the standard deviation 2 sigma value of the VOLQ value from last week's closing price. 1day predicts daily fluctuations. 2day predicts 2-day fluctuations. 3day predicts 3-day fluctuations. 4day predicts 4-day fluctuations. 5day predicts 5-day fluctuations. In the settings you can select the start date to display the VOLQ line via input. ----------------------------- What motivated me to create this indicator? From my point of view, the reason for classifying vix volq historical volatility (realized volatility) is that the most important point is that VIXX and VolQ are calculated from implied volatility. It can be standardized as one-month volatility. There are many strike prices, but exchanges use the implied volatility of options traded on their own exchanges. Because historical volatility depends on how the period is set, to compare with VIXX, we compare it with a month, that is, 20 business days. One-month implied volatility means (actually different depending on the strike price), because option traders expect that the one-month volatility will be this much, and it is the volatility created by volatility trading. So we see it as the volatility expected by derivatives traders, especially volatility traders. I'm trying to infer what the market thinks will fluctuate this much from the numbers generated there.
Indicatore Pine Script®
di Cube_Lee
1
Willspread Chart + POIV & ADVolumen TrendColor sπThe Indicator is a combination of different types of measurements to the Price Action. 1. Spread: The Spread is set to measure your Symbol to another chosen Market like Dollar as Contra . But you can switch also between different markets. 2. Accumulation/Distribution with True Range of High or Low including OpenInterest. This only works with Futures . --Energies, Metals, Bonds, Softs, Currencies, Livestock, live cattle , feeder cattle, lean hogs , index-- Open Interest for: ZW, ZC, ZS, ZM, ZL, ZO, ZR, CL, RB, HO, NG, GC, SI, HG, PA, PL, ZN, ZB, ZT, ZF, CC, CT, KC, SB, JO, LB, AUDUSD, GBPUSD, USDCAD, EURUSD, USDJPY, USDCHF, USDMXN, NZDUSD, USDRUB, DX, BTC, ETH, LE, GF, HE, NQ, NDX, ES, SPX, RTY, VIX, 3. Accumulation/Distribution with True Range of High or Low including Volume . 4. The color shows if the Market has positive or negative (Willspread, Volume or Open Interest) 5. The Indicator also shows Divergences to Price and Willspread Movements. If you want to have more information just give me a message.
Indicatore Pine Script®
di ComJoTrader
1
Implied Volatility Estimator using Black Scholes [Loxx]Implied Volatility Estimator using Black Scholes derives a estimation of implied volatility using the Black Scholes options pricing model. The Bisection algorithm is used for our purposes here. This includes the ability to adjust for dividends. Implied Volatility The implied volatility (IV) of an option contract is that value of the volatility of the underlying instrument which, when input in an option pricing model (such as Black–Scholes), will return a theoretical value equal to the current market price of that option. The VIX , in contrast, is a model-free estimate of Implied Volatility. The latter is viewed as being important because it represents a measure of risk for the underlying asset. Elevated Implied Volatility suggests that risks to underlying are also elevated. Ordinarily, to estimate implied volatility we rely upon Black-Scholes (1973). This implies that we are prepared to accept the assumptions of Black Scholes (1973). Inputs Spot price: select from 33 different types of price inputs Strike Price: the strike price of the option you're wishing to model Market Price: this is the market price of the option; choose, last, bid, or ask to see different results Historical Volatility Period: the input period for historical volatility ; historical volatility isn't used in the Bisection algo, this is to serve as a comparison, even though historical volatility is from price movement of the underlying asset where as implied volatility is the volatility of the option Historical Volatility Type: choose from various types of implied volatility , search my indicators for details on each of these Option Base Currency: this is to calculate the risk-free rate, this is used if you wish to automatically calculate the risk-free rate instead of using the manual input. this uses the 10 year bold yield of the corresponding country % Manual Risk-free Rate: here you can manually enter the risk-free rate Use manual input for Risk-free Rate? : choose manual or automatic for risk-free rate % Manual Yearly Dividend Yield: here you can manually enter the yearly dividend yield Adjust for Dividends?: choose if you even want to use use dividends Automatically Calculate Yearly Dividend Yield? choose if you want to use automatic vs manual dividend yield calculation Time Now Type: choose how you want to calculate time right now, see the tool tip Days in Year: choose how many days in the year, 365 for all days, 252 for trading days, etc Hours Per Day: how many hours per day? 24, 8 working hours, or 6.5 trading hours Expiry date settings: here you can specify the exact time the option expires *** the algorithm inputs for low and high aren't to be changed unless you're working through the mathematics of how Bisection works. Included Option pricing panel Loxx's Expanded Source Types Related Indicators Cox-Ross-Rubinstein Binomial Tree Options Pricing Model
Indicatore Pine Script®
di loxx
1
vol_coneDraws a volatility cone on the chart, using the contract's realized volatility (rv). The inputs are: - window: the number of past periods to use for computing the realized volatility. VIX uses 30 calendar days, which is 21 trading days, so 21 is the default. - stdevs: the number of standard deviations that the cone will cover. - periods to project: the length of the volatility cone. - periods per year: the number of periods in a year. for a daily chart, this is 252. for a thirty minute chart on a contract that trades 23 hours a day, this is 23 * 2 * 252 = 11592. for an accurate cone, this input must be set correctly, according to the chart's time frame. - history: show the lagged projections. in other words, if the cone is set to project 21 periods in the future, the lines drawn show the top and bottom edges of the cone from 23 periods ago. - rate: the current interest or discount rate. this is used to compute the forward price of the underlying contract. using an accurate forward price allows you to compare the realized volatility projection to the implied volatility projections derived from options prices. Example settings for a 30 minute chart of a contract that trades 23 hours per day, with 1 standard deviation, a 21 day rv calculation, and half a day projected: - stdevs: 1 - periods to project: 23 - window: 23 * 2 * 21 = 966 - periods per year: 23 * 2 * 252 = 11592 Additionally, a table is drawn in the upper right hand corner, with several values: - rv: the contract's current realized volatility. - rnk: the rv's percentile rank, compared to the rv values on past bars. - acc: the proportion of times price settled inside, versus outside, the volatility cone, "periods to project" into the future. this should be around 65-70% for most contracts when the cone is set to 1 standard deviation. - up: the upper bound of the cone for the projection period. - dn: the lower bound of the cone for the projection period. Limitations: - pinescript only seems to be able to draw a limited distance into the future. If you choose too many "periods to project", the cone will start drawing vertically at some limit. - the cone is not totally smooth owing to the facts a) it is comprised of a limited number of lines and b) each bar does not represent the same amount of time in pinescript, as some cross weekends, session gaps, etc.
Indicatore Pine Script®
di taylor_o
vol_boxA simple script to draw a realized volatility forecast, in the form of a box. The script calculates realized volatility using the EWMA method, using a number of periods of your choosing. Using the "periods per year", you can adjust the script to work on any time frame. For example, if you are using an hourly chart with bitcoin, there are 24 periods * 365 = 8760 periods per year. This setting is essential for the realized volatility figure to be accurate as an annualized figure, like VIX. By default, the settings are set to mimic CBOE volatility indices. That is, 252 days per year, and 20 period window on the daily timeframe (simulating a 30 trading day period). Inside the box are three figures: 1. The current realized volatility. 2. The rank. E.g. "10%" means the current realized volatility is less than 90% of realized volatility measures. 3. The "accuracy": how often price has closed within the box, historically. Inputs: stdevs: the number of standard deviations for the box periods to project: the number of periods to forecast window: the number of periods for calculating realized volatility periods per year: the number of periods in one year (e.g. 252 for the "D" timeframe)
Indicatore Pine Script®
di taylor_o
8
Crude Oil: Backwardation Vs ContangoCrude Oil, CL Plots Futures Curve: Futures contract prices over the next 3.5 years; to easily visualize Backwardation Vs Contango(carrying charge) markets. Carrying charge (contract prices increasing into the future) = normal, representing the costs of carrying/storage of a commodity. When this is flipped to Backwardation(As the above; contract prices decreasing into the future): it's a bullish sign: Buyers want this commodity, and they want it NOW. Note: indicator does not map to time axis in the same way as price; it simply plots the progression of contract months out into the future; left to right; so timeframe DOESN'T MATTER for this plot TO UPDATE (every year or so): in REQUEST CONTRACTS section, delete old contracts (top) and add new ones (bottom). Then in PLOTTING section, Delete old contract labels (bottom); add new contract labels (top); adjust the X in 'bar_index-(X+_historical)' numbers accordingly This is one of several similar Futures Curve indicators: Meats | Metals | Grains | VIX | Crude Oil If you want to build from this; to work on other commodities; be aware that Tradingview limits the number of contract calls to 40 (hence the multiple indicators) Tips: -Right click and reset chart if you can't see the plot; or if you have trouble with the scaling. -Right click and add to new scale if you prefer this not to overlay directly on price. Or move to new pane below. -If this takes too long to load (due to so many security calls); comment out the more distant future half of the contracts; and their respective labels. Or comment out every other contract and every other label if you prefer. --Added historical input: input days back in time; to see the historical shape of the Futures curve via selecting 'days back' snapshot updated 20th June 2022 © twingall
Indicatore Pine Script®
di twingall
1
Gap Reversion StrategyToday I am releasing to the community an original short-term, high-probability gap trading strategy, backed by a 20 year backtest. This strategy capitalizes on the mean reverting behavior of equity ETFs, which is largely driven by fear in the market. The strategy buys into that fear at a level that has historically mean reverted within ~5 days. Larry Connors has published useful research and variations of strategies based on this behavior that I would recommend any quantitative trader read. What it does: This strategy, for 1 day charts on equity ETFs, looks for an overnight gap down when the RSI is also in/near an oversold position. Then, it places a limit order further below the opening of the gapped-down day. It then exits the position based on a higher RSI level. The limit buy order is cancelled if the price doesn't reach your limit price that day. So, the larger you make the gap and limit %, the less signals you will have. Features: Inputs to allow the adjustment of the limit order %, the gap %, and the RSI entry/exit levels. An option to have the limit order be based on a % of ATR instead of a % of asset price. An optional filter that can turn-off trades when the VIX is unusually high. A built in stop. Built in alerts. Disclaimer: This is not financial advice. Open-source scripts I publish in the community are largely meant to spark ideas that can be used as building blocks for part of a more robust trade management strategy. If you would like to implement a version of any script, I would recommend making significant additions/modifications to the strategy & risk management functions. If you don’t know how to program in Pine, then hire a Pine-coder. We can help!
Strategia Pine Script®
di TradeAutomation
3
PClose Levels 2.0This script plots the levels generated via a combination of SPX 2Y Quartiles for everyday, red days, and green days. It is intended for use solely with SPX. These quartiles are also sorted by VIX averages into bands that expand and contract with VIX. It gives us an idea of what levels to potentially expect resistance/support fairly well, but is designed to be used in conjunction with other indicators and macroeconomic information. Green Dashed is your Expected Max Range (EMR+) based on Green Day averages. Green Dotted is your Expected Range (ER+) based on full dataset averages. Green solid lines are POS2 and POS1, based on Green Day averages. White Dotted is your Expected Move (EM), based on full dataset averages. Red solid lines are NEG1 and NEG2, based on Red Day averages. Red Dotted is your Expected Range (ER-) based on full dataset averages. Red Dashed is your Expected Max Range (EMR-) based on Red Day averages.
Indicatore Pine Script®
di TheWaysian
3
Relative Strength Volatility Adjusted Ema [CC]The Relative Strength Volatility Adjusted Exponential Moving Average was created by Vitali Apirine (Stocks and Commodities Mar 2022) and this is his final indicator of his recent Relative Strength series. I published both of the previous indicators, Relative Strength Volume Adjusted Exponential Moving Average and Relative Strength Exponential Moving Average This indicator is particularly unique because it uses the Volatility Index (VIX) symbol as the default to determine volatility and uses this in place of the current stock's price into a typical relative strength calculation. As you can see in the chart, it follows the price much closer than the other two indicators and so of course this means that this indicator is best for choppy markets and the other two are better for trending markets. I would of course recommend to experiment with this one and see what works best for you. I have included strong buy and sell signals in addition to normal ones so strong signals are darker in color and normal signals are lighter in color. Buy when the line turns green and sell when it turns red. Let me know if there are any other indicators or scripts you would like to see me publish!
Indicatore Pine Script®
di cheatcountry
5
Nasdaq VXN Volatility Warning IndicatorToday I am sharing with the community a volatility indicator that uses the Nasdaq VXN Volatility Index to help you or your algorithms avoid black swan events. This is a similar the indicator I published last week that uses the SP500 VIX, but this indicator uses the Nasdaq VXN and can help inform strategies on the Nasdaq index or Nasdaq derivative instruments. Variance is most commonly used in statistics to derive standard deviation (with its square root). It does have another practical application, and that is to identify outliers in a sample of data. Variance is defined as the squared difference between a value and its mean. Calculating that squared difference means that the farther away the value is from the mean, the more the variance will grow (exponentially). This exponential difference makes outliers in the variance data more apparent. Why does this matter? There are assets or indices that exist in the stock market that might make us adjust our trading strategy if they are behaving in an unusual way. In some instances, we can use variance to identify that behavior and inform our strategy. Is that really possible? Let’s look at the relationship between VXN and the Nasdaq100 as an example. If you trade a Nasdaq index with a mean reversion strategy or algorithm, you know that they typically do best in times of volatility . These strategies essentially attempt to “call bottom” on a pullback. Their downside is that sometimes a pullback turns into a regime change, or a black swan event. The other downside is that there is no logical tight stop that actually increases their performance, so when they lose they tend to lose big. So that begs the question, how might one quantitatively identify if this dip could turn into a regime change or black swan event? The Nasdaq Volatility Index ( VXN ) uses options data to identify, on a large scale, what investors overall expect the market to do in the near future. The Volatility Index spikes in times of uncertainty and when investors expect the market to go down. However, during a black swan event, historically the VXN has spiked a lot harder. We can use variance here to identify if a spike in the VXN exceeds our threshold for a normal market pullback, and potentially avoid entering trades for a period of time (I.e. maybe we don’t buy that dip). Does this actually work? In backtesting, this cut the drawdown of my index reversion strategies in half. It also cuts out some good trades (because high investor fear isn’t always indicative of a regime change or black swan event). But, I’ll happily lose out on some good trades in exchange for half the drawdown. Lets look at some examples of periods of time that trades could have been avoided using this strategy/indicator: Example 1 – With the Volatility Warning Indicator, the mean reversion strategy could have avoided repeatedly buying this pullback that led to this asset losing over 75% of its value: Example 2 - June 2018 to June 2019 - With the Volatility Warning Indicator, the drawdown during this period reduces from 22% to 11%, and the overall returns increase from -8% to +3% How do you use this indicator? This indicator determines the variance of VXN against a long term mean. If the variance of the VXN spikes over an input threshold, the indicator goes up. The indicator will remain up for a defined period of bars/time after the variance returns below the threshold. I have included default values I’ve found to be significant for a short-term mean-reversion strategy, but your inputs might depend on your risk tolerance and strategy time-horizon. The default values are for 1hr VXN data/charts. It will pull in variance data for the VXN regardless of which chart the indicator is applied to. Disclaimer: Open-source scripts I publish in the community are largely meant to spark ideas or be used as building blocks for part of a more robust trade management strategy. If you would like to implement a version of any script, I would recommend making significant additions/modifications to the strategy & risk management functions. If you don’t know how to program in Pine, then hire a Pine-coder. We can help!
Indicatore Pine Script®
di TradeAutomation
3
Commitment of Traders: Financial Metrics█  OVERVIEW This indicator displays the Commitment of Traders (​COT) financial data for futures markets. █  CONCEPTS Commitment of Traders ​(​COT) data is tallied by the Commodity ​Futures Trading Commission (CFTC) , a US federal agency that oversees the trading of derivative markets such as futures in the US. It is weekly data that provides traders with information about open interest for an asset. The CFTC oversees derivative markets traded on different exchanges, so ​COT data is available for assets that can be traded on CBOT, ​CME, NYMEX, COMEX, and ICEUS. A detailed description of the COT report can be found on the CFTC's website . COT data is separated into three notable reports: Legacy, Disaggregated, and Financial. This indicator presents data from the COT Financial (Traders in Financial Futures) report. The Financial report includes financial contracts, such as currencies, US Treasury securities, Eurodollars, stocks, VIX and Bloomberg commodity index. As such, the TFF data is limited to financial-related tickers. The TFF report breaks down the reportable open interest positions into four classifications: Dealer/Intermediary, Asset Manager/Institutional, Leveraged Funds, and Other Reportables. Our other COT indicators are:  • Commitment of Traders: Legacy Metrics  • Commitment of Traders: Disaggregated Metrics  • Commitment of Traders: Total █  HOW TO USE IT Load the indicator on an active chart (see here if you don't know how). By default, the indicator uses the chart's symbol to derive the COT data it displays. You can also specify a CFTC code in the "CFTC code" field of the script's inputs to display COT data from a symbol different than the chart's. The rest of this section documents the script's input fields. Metric Each metric represents a different column of the Commitment of Traders report. Details are available in the explanatory notes on the CFTC's website . Here is a summary of the metrics:  • "Open Interest" is the total of all futures and/or option contracts entered into and not yet offset by a transaction, by delivery, by exercise, etc.   The aggregate of all long open interest is equal to the aggregate of all short open interest.  • "Traders Total" is the number of all unique reportable traders, regardless of the trading direction.  • "Traders Dealer" is the number of traders classified as a "Dealer/Intermediary" reported holding any position with the specified direction.   A "producer/merchant/processor/user" is an entity typically described as the “sell side” of the market.   Though they may not predominately sell futures, they do design and sell various financial assets to clients.   They tend to have matched books or offset their risk across markets and clients.   Futures contracts are part of the pricing and balancing of risk associated with the products they sell and their activities.  • "Traders Asset Manager" is the number of traders classified as "Asset Manager/Institutional" reported holding any position with the specified direction.   These are institutional investors, including pension funds, endowments, insurance companies,   mutual funds and those portfolio/investment managers whose clients are predominantly institutional.  • "Traders Leveraged Funds" is the number of traders classified as "Leveraged Funds" reported holding any position with the specified direction.   These are typically hedge funds and various types of money managers. The traders may be engaged in managing and   conducting proprietary futures trading and trading on behalf of speculative clients.  • "Traders Other Reportable" is the number of reportable traders that are not placed in any of the three categories specified above.   The traders in this category mostly are using markets to hedge business risk, whether that risk is related to foreign exchange, equities or interest rates.   This category includes corporate treasuries, central banks, smaller banks, mortgage originators, credit unions and any other reportable traders not assigned to the other three categories.  • "Traders Total Reportable" is the number of all traders reported holding any position with the specified direction.   To determine the total number of reportable traders in a market, a trader is counted only once whether or not the trader appears in more than one category.   As a result, the sum of the numbers of traders in each separate category typically exceeds the total number of reportable traders.  • "Dealer/Asset Manager/Leveraged Funds/Total Reportable/Other Reportable Positions -- all positions held by the traders of the specified category.  • "Nonreportable Positions" is the long and short open interest derived by subtracting the total long and short reportable positions from the total open interest.   Accordingly, the number of traders involved and the commercial/non-commercial classification of each trader are unknown.  • "Concentration Gross/Net LT 4/8 TDR" is the percentage of open interest held by 4/8 of the largest traders, by gross/net positions,   without regard to whether they are classified as commercial or non-commercial. The Net position ratios are computed after offsetting each trader’s equal long and short positions.   A reportable trader with relatively large, balanced long and short positions in a single market, therefore,   may be among the four and eight largest traders in both the gross long and gross short categories, but will probably not be included among the four and eight largest traders on a net basis. Direction Each metric is available for a particular set of directions. Valid directions for each metric are specified with its name in the "Metric" field's dropdown menu. COT Selection Mode This field's value determines how the script determines which COT data to return from the chart's symbol: - "Root" uses the root of a futures symbol ("ES" for "ESH2020"). - "Base currency" uses the base currency in a forex pair ("EUR" for "EURUSD"). - "Currency" uses the quote currency, i.e., the currency the symbol is traded in ("JPY" for "TSE:9984" or "USDJPY"). - "Auto" tries all modes, in turn. If no COT data can be found, a runtime error is generated. Note that if the "CTFC Code" input field contains a code, it will override this input. Futures/Options Specifies the type of Commitment of Traders data to display: data concerning only Futures, only Options, or both. CTFC Code Instead of letting the script generate the CFTC COT code from the chart and the "COT Selection Mode" input when this field is empty, you can specify an unrelated CFTC COT code here, e.g., 001602 for wheat futures. Look first. Then leap.
Indicatore Pine Script®
di TradingView
6
vix_vx_regressionAn example of the linear regression library, showing the regression of VX futures on the VIX. The beta might help you weight VX futures when hedging SPX vega exposure. A VX future has point multiplier of 1000, whereas SPX options have a point multiplier of 100. Suppose the front month VX future has a beta of 0.6 and the front month SPX straddle has a vega of 8.5. Using these approximations, the VX future will underhedge the SPX straddle, since (0.6 * 1000) < (8.5 * 100). The position will have about 2.5 ($250) vega. Use the R^2 (coefficient of determination) to check how well the model fits the relationship between VX and VIX. The further from one this value, the less useful the model. (Note that the mini, VXM futures also have a 100 point multiplier).
Indicatore Pine Script®
di taylor_o
rv_iv_vrpThis script provides realized volatility (rv), implied volatility (iv), and volatility risk premium (vrp) information for each of CBOE's volatility indices. The individual outputs are: - Blue/red line: the realized volatility. This is an annualized, 20-period moving average estimate of realized volatility--in other words, the variability in the instrument's actual returns. The line is blue when realized volatility is below implied volatility, red otherwise. - Fuchsia line (opaque): the median of realized volatility. The median is based on all data between the "start" and "end" dates. - Gray line (transparent): the implied volatility (iv). According to CBOE's volatility methodology, this is similar to a weighted average of out-of-the-money ivs for options with approximately 30 calendar days to expiration. Notice that we compare rv20 to iv30 because there are about twenty trading periods in thirty calendar days. - Fuchsia line (transparent): the median of implied volatility. - Lightly shaded gray background: the background between "start" and "end" is shaded a very light gray. - Table: the table shows the current, percentile, and median values for iv, rv, and vrp. Percentile means the value is greater than "N" percent of all values for that measure. ----- Volatility risk premium (vrp) is simply the difference between implied and realized volatility. Along with implied and realized volatility, traders interpret this measure in various ways. Some prefer to be buying options when there volatility, implied or realized, reaches absolute levels, or low risk premium, whereas others have the opposite opinion. However, all volatility traders like to look at these measures in relation to their past values, which this script assists with. By the way, this script is similar to my "vol premia," which provides the vrp data for all of these instruments on one page. However, this script loads faster and lets you see historical data. I recommend viewing the indicator and the corresponding instrument at the same time, to see how volatility reacts to changes in the underlying price.
Indicatore Pine Script®
di taylor_o
2
Augmented Dickey–Fuller (ADF) mean reversion testThe augmented Dickey-Fuller test (ADF) is a statistical test for the tendency of a price series sample to mean revert . The current price of a mean-reverting series may tell us something about the next move (as opposed, for example, to a geometric Brownian motion). Thus, the ADF test allows us to spot market inefficiencies and potentially exploit this information in a trading strategy. Mathematically, the mean reversion property means that the price change in the next time period is proportional to the difference between the average price and the current price. The purpose of the ADF test is to check if this proportionality constant is zero. Accordingly, the ADF test statistic is defined as the estimated proportionality constant divided by the corresponding standard error. In this script, the ADF test is applied in a rolling window with a user-defined lookback length. The calculated values ​​of the ADF test statistic are plotted as a time series. The more negative the test statistic, the stronger the rejection of the hypothesis that there is no mean reversion. If the calculated test statistic is less than the critical value calculated at a certain confidence level (90%, 95%, or 99%), then the hypothesis of a mean reversion is accepted (strictly speaking, the opposite hypothesis is rejected). Input parameters: Source - The source of the time series being tested. Length - The number of points in the rolling lookback window. The larger sample length makes the ADF test results more reliable. Maximum lag - The maximum lag included in the test, that defines the order of an autoregressive process being implied in the model. Generally, a non-zero lag allows taking into account the serial correlation of price changes. When dealing with price data, a good starting point is lag 0 or lag 1. Confidence level - The probability level at which the critical value of the ADF test statistic is calculated. If the test statistic is below the critical value, it is concluded that the sample of the price series is mean-reverting. Confidence level is calculated based on MacKinnon (2010) . Show Infobox - If True, the results calculated for the last price bar are displayed in a table on the left. More formal background: Formally, the ADF test is a test for a unit root in an autoregressive process. The model implemented in this script involves a non-zero constant and zero time trend. The zero lag corresponds to the simple case of the AR(1) process, while higher order autoregressive processes AR(p) can be approached by setting the maximum lag of p. The null hypothesis is that there is a unit root, with the alternative that there is no unit root. The presence of unit roots in an autoregressive time series is characteristic for a non-stationary process. Thus, if there is no unit root, the time series sample can be concluded to be stationary, i.e., manifesting the mean-reverting property. A few more comments: It should be noted that the ADF test tells us only about the properties of the price series now and in the past. It does not directly say whether the mean-reverting behavior will retain in the future. The ADF test results don't directly reveal the direction of the next price move. It only tells wether or not a mean-reverting trading strategy can be potentially applicable at the given moment of time. The ADF test is related to another statistical test, the Hurst exponent. The latter is available on TradingView as implemented by balipour , QuantNomad and DonovanWall . The ADF test statistics is a negative number. However, it can take positive values, which usually corresponds to trending markets (even though there is no statistical test for this case). Rigorously, the hypothesis about the mean reversion is accepted at a given confidence level when the value of the test statistic is below the critical value. However, for practical trading applications, the values which are low enough - but still a bit higher than the critical one - can be still used in making decisions. Examples: The VIX volatility index is known to exhibit mean reversion properties (volatility spikes tend to fade out quickly). Accordingly, the statistics of the ADF test tend to stay below the critical value of 90% for long time periods. The opposite case is presented by BTCUSD. During the same time range, the bitcoin price showed strong momentum - the moves away from the mean did not follow by the counter-move immediately, even vice versa. This is reflected by the ADF test statistic that consistently stayed above the critical value (and even above 0). Thus, using a mean reversion strategy would likely lead to losses.
Indicatore Pine Script®
di tbtkg
47
pricing_tableThis script helps you evaluate the fair value of an option. It poses the question "if I bought or sold an option under these circumstances in the past, would it have expired in the money, or worthless? What would be its expected value, at expiration, if I opened a position at N standard deviations, given the volatility forecast, with M days to expiration at the close of every previous trading day?" The default (and only) "hv" volatility forecast is based on the assumption that today's volatility will hold for the next M days. To use this script, only one step is mandatory. You must first select days to expiration. The script will not do anything until this value is changed from the default (-1). These should be CALENDAR days. The script will convert to these to business days for forecasting and valuation, as trading in most contracts occurs over ~250 business days per year. Adjust any other variables as desired: model: the volatility forecasting model window: the number of periods for a lagged model (e.g. hv) filter: a filter to remove forecasts from the sample filter type: "none" (do not use the filter), "less than" (keep forecasts when filter < volatility), "greater than" (keep forecasts when filter > volatility) filter value: a whole number percentage. see example below discount rate: to discount the expected value to present value precision: number of decimals in output trim outliers: omit upper N % of (generally itm) contracts The theoretical values are based on history. For example, suppose days to expiration is 30. On every bar, the 30 days ago N deviation forecast value is compared to the present price. If the price is above the forecast value, the contract has expired in the money; otherwise, it has expired worthless. The theoretical value is the average of every such sample. The itm probabilities are calculated the same way. The default (and only) volatility model is a 20 period EWMA derived historical (realized) volatility. Feel free to extend the script by adding your own. The filter parameters can be used to remove some forecasts from the sample. Example A: filter: filter type: none filter value: Default: the filter is not used; all forecasts are included in the the sample. Example B: filter: model filter type: less than filter value: 50 If the model is "hv", this will remove all forecasts when the historical volatility is greater than fifty. Example C: filter: rank filter type: greater than filter value: 75 If the model volatility is in the top 25% of the previous year's range, the forecast will be included in the sample apart from "model" there are some common volatility indexes to choose from, such as Nasdaq (VXN), crude oil (OVX), emerging markets (VXFXI), S&P; (VIX) etc. Refer to the middle-right table to see the current forecast value, its rank among the last 252 days, and the number of business days until expiration. NOTE: This script is meant for the daily chart only.
Indicatore Pine Script®
di taylor_o
4
Bank Nifty DashboardThis shows a performance glance of Dow, India Vix and Major Constituents of Bank Nifty. Which will help to take quick decision. Style settings Normalized Change Mode: Allows the user to access a different interpretation of the indicator by showing the normalized first differences of each indicator in the dashboard instead of their sign Dashboard Location: Location of the dashboard on the chart Dashboard Size: Size of the dashboard on the chart Text/Frame Color: Determines the color of the frame grid as well as the text color Bullish Cell Color: Determines the color of cell associated with a rising indicator direction Bearish Cell Color: Determines the color of cell associated with a decreasing indicator direction Cell Transparency: Transparency of each cell Usage This will help to monitor the banks Performance on various time frames . You can change the stock list according to your usage/Index. All showing in green indication strong momentum.
Indicatore Pine Script®
di cvsk123
47
VixFixLinReg-IndicatorSame as VixFixLinearRegression strategy published earlier - but as indicator for those who want to use it as indicator. Strategy can be found here: Concept is simple: Based on VixFix script by Chris Moody. VIX-Fix can sometime give early signal. Hence, apply linear regression for better estimation of market bottom. Area above 0 shows VixFix whereas the below 0 area is linear regression of VixFix. To estimate market bottom: First wait for VixFix to turn lime Then wait for linear regression to turn lime from green. VixFix may no longer be lime by linear regression chages. But, that's ok. Have also added option candle color to highlight bottom and alert condition for those who want to use it.
Indicatore Pine Script®
di Trendoscope
5