CSP Key Level Finder This script is designed for option sellers, particularly those using strategies like cash-secured puts (CSPs), to help automate the process of identifying key levels in the market. The core functionality is to calculate a specific price level where a 5% return can be achieved based on the historical volatility of the underlying asset. This level is visually plotted on a chart to guide traders in making more informed decisions without manually calculating the thresholds themselves.
The script incorporates implied volatility (IV) data to determine the volatility rank of the asset and calculates historical volatility (HV) based on price movements. These volatility measures help assess market conditions. The resulting key level is drawn as a line on the chart, along with a label that includes relevant information about volatility, making it easier for traders to evaluate potential option selling strategies.
Additionally, the script includes user input options, allowing users to control when to display the key level on the chart, offering flexibility based on individual needs. Overall, the script provides a visual aid for option sellers to streamline the process of identifying attractive entry points.
Wyszukaj w skryptach "implied volatility"
MOVE/VXTLT CorrelationMany know of the VIX for equity trading. Yet, many are unaware that there is the same kind of volatility measure for trading bonds, called the MOVE Index.
"The Merrill Lynch Option Volatility Estimate (MOVE) Index is a yield curve weighted index of the normalized implied volatility on 1-month Treasury options which are weighted on the 2, 5, 10, and 30 year contracts."
With this script one can see the the correlation and divergences between bonds and its volatility measure to make educated decisions in trading or hedging.
The idea of this script comes from NicTheMajestic.
σ-Based SL/TP (Long & Short). Statistical Volatility (Quant Upgrade of ATR)
Instead of ATR’s simple moving average, use standard deviation of returns (σ), realized volatility, or implied volatility (options data).
SL = kσ, TP = 2kσ (customizable).
Why better than ATR: more precise reflection of actual distribution tails, not just candle ranges.
Goldman Sachs Risk Appetite ProxyRisk appetite indicators serve as barometers of market psychology, measuring investors' collective willingness to engage in risk-taking behavior. According to Mosley & Singer (2008), "cross-asset risk sentiment indicators provide valuable leading signals for market direction by capturing the underlying psychological state of market participants before it fully manifests in price action."
The GSRAI methodology aligns with modern portfolio theory, which emphasizes the importance of cross-asset correlations during different market regimes. As noted by Ang & Bekaert (2002), "asset correlations tend to increase during market stress, exhibiting asymmetric patterns that can be captured through multi-asset sentiment indicators."
Implementation Methodology
Component Selection
Our implementation follows the core framework outlined by Goldman Sachs research, focusing on four key components:
Credit Spreads (High Yield Credit Spread)
As noted by Duca et al. (2016), "credit spreads provide a market-based assessment of default risk and function as an effective barometer of economic uncertainty." Higher spreads generally indicate deteriorating risk appetite.
Volatility Measures (VIX)
Baker & Wurgler (2006) established that "implied volatility serves as a direct measure of market fear and uncertainty." The VIX, often called the "fear gauge," maintains an inverse relationship with risk appetite.
Equity/Bond Performance Ratio (SPY/IEF)
According to Connolly et al. (2005), "the relative performance of stocks versus bonds offers significant insight into market participants' risk preferences and flight-to-safety behavior."
Commodity Ratio (Oil/Gold)
Baur & McDermott (2010) demonstrated that "gold often functions as a safe haven during market turbulence, while oil typically performs better during risk-on environments, making their ratio an effective risk sentiment indicator."
Standardization Process
Each component undergoes z-score normalization to enable cross-asset comparisons, following the statistical approach advocated by Burdekin & Siklos (2012). The z-score transformation standardizes each variable by subtracting its mean and dividing by its standard deviation: Z = (X - μ) / σ
This approach allows for meaningful aggregation of different market signals regardless of their native scales or volatility characteristics.
Signal Integration
The four standardized components are equally weighted and combined to form a composite score. This democratic weighting approach is supported by Rapach et al. (2010), who found that "simple averaging often outperforms more complex weighting schemes in financial applications due to estimation error in the optimization process."
The final index is scaled to a 0-100 range, with:
Values above 70 indicating "Risk-On" market conditions
Values below 30 indicating "Risk-Off" market conditions
Values between 30-70 representing neutral risk sentiment
Limitations and Differences from Original Implementation
Proprietary Components
The original Goldman Sachs indicator incorporates additional proprietary elements not publicly disclosed. As Goldman Sachs Global Investment Research (2019) notes, "our comprehensive risk appetite framework incorporates proprietary positioning data and internal liquidity metrics that enhance predictive capability."
Technical Limitations
Pine Script v6 imposes certain constraints that prevent full replication:
Structural Limitations: Functions like plot, hline, and bgcolor must be defined in the global scope rather than conditionally, requiring workarounds for dynamic visualization.
Statistical Processing: Advanced statistical methods used in the original model, such as Kalman filtering or regime-switching models described by Ang & Timmermann (2012), cannot be fully implemented within Pine Script's constraints.
Data Availability: As noted by Kilian & Park (2009), "the quality and frequency of market data significantly impacts the effectiveness of sentiment indicators." Our implementation relies on publicly available data sources that may differ from Goldman Sachs' institutional data feeds.
Empirical Performance
While a formal backtest comparison with the original GSRAI is beyond the scope of this implementation, research by Froot & Ramadorai (2005) suggests that "publicly accessible proxies of proprietary sentiment indicators can capture a significant portion of their predictive power, particularly during major market turning points."
References
Ang, A., & Bekaert, G. (2002). "International Asset Allocation with Regime Shifts." Review of Financial Studies, 15(4), 1137-1187.
Ang, A., & Timmermann, A. (2012). "Regime Changes and Financial Markets." Annual Review of Financial Economics, 4(1), 313-337.
Baker, M., & Wurgler, J. (2006). "Investor Sentiment and the Cross-Section of Stock Returns." Journal of Finance, 61(4), 1645-1680.
Baur, D. G., & McDermott, T. K. (2010). "Is Gold a Safe Haven? International Evidence." Journal of Banking & Finance, 34(8), 1886-1898.
Burdekin, R. C., & Siklos, P. L. (2012). "Enter the Dragon: Interactions between Chinese, US and Asia-Pacific Equity Markets, 1995-2010." Pacific-Basin Finance Journal, 20(3), 521-541.
Connolly, R., Stivers, C., & Sun, L. (2005). "Stock Market Uncertainty and the Stock-Bond Return Relation." Journal of Financial and Quantitative Analysis, 40(1), 161-194.
Duca, M. L., Nicoletti, G., & Martinez, A. V. (2016). "Global Corporate Bond Issuance: What Role for US Quantitative Easing?" Journal of International Money and Finance, 60, 114-150.
Froot, K. A., & Ramadorai, T. (2005). "Currency Returns, Intrinsic Value, and Institutional-Investor Flows." Journal of Finance, 60(3), 1535-1566.
Goldman Sachs Global Investment Research (2019). "Risk Appetite Framework: A Practitioner's Guide."
Kilian, L., & Park, C. (2009). "The Impact of Oil Price Shocks on the U.S. Stock Market." International Economic Review, 50(4), 1267-1287.
Mosley, L., & Singer, D. A. (2008). "Taking Stock Seriously: Equity Market Performance, Government Policy, and Financial Globalization." International Studies Quarterly, 52(2), 405-425.
Oppenheimer, P. (2007). "A Framework for Financial Market Risk Appetite." Goldman Sachs Global Economics Paper.
Rapach, D. E., Strauss, J. K., & Zhou, G. (2010). "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy." Review of Financial Studies, 23(2), 821-862.
Normal Distribution CurveThis Normal Distribution Curve is designed to overlay a simple normal distribution curve on top of any TradingView indicator. This curve represents a probability distribution for a given dataset and can be used to gain insights into the likelihood of various data levels occurring within a specified range, providing traders and investors with a clear visualization of the distribution of values within a specific dataset. With the only inputs being the variable source and plot colour, I think this is by far the simplest and most intuitive iteration of any statistical analysis based indicator I've seen here!
Traders can quickly assess how data clusters around the mean in a bell curve and easily see the percentile frequency of the data; or perhaps with both and upper and lower peaks identify likely periods of upcoming volatility or mean reversion. Facilitating the identification of outliers was my main purpose when creating this tool, I believed fixed values for upper/lower bounds within most indicators are too static and do not dynamically fit the vastly different movements of all assets and timeframes - and being able to easily understand the spread of information simplifies the process of identifying key regions to take action.
The curve's tails, representing the extreme percentiles, can help identify outliers and potential areas of price reversal or trend acceleration. For example using the RSI which typically has static levels of 70 and 30, which will be breached considerably more on a less liquid or more volatile asset and therefore reduce the actionable effectiveness of the indicator, likewise for an asset with little to no directional volatility failing to ever reach this overbought/oversold areas. It makes considerably more sense to look for the top/bottom 5% or 10% levels of outlying data which are automatically calculated with this indicator, and may be a noticeable distance from the 70 and 30 values, as regions to be observing for your investing.
This normal distribution curve employs percentile linear interpolation to calculate the distribution. This interpolation technique considers the nearest data points and calculates the price values between them. This process ensures a smooth curve that accurately represents the probability distribution, even for percentiles not directly present in the original dataset; and applicable to any asset regardless of timeframe. The lookback period is set to a value of 5000 which should ensure ample data is taken into calculation and consideration without surpassing any TradingView constraints and limitations, for datasets smaller than this the indicator will adjust the length to just include all data. The labels providing the percentile and average levels can also be removed in the style tab if preferred.
Additionally, as an unplanned benefit is its applicability to the underlying price data as well as any derived indicators. Turning it into something comparable to a volume profile indicator but based on the time an assets price was within a specific range as opposed to the volume. This can therefore be used as a tool for identifying potential support and resistance zones, as well as areas that mark market inefficiencies as price rapidly accelerated through. This may then give a cleaner outlook as it eliminates the potential drawbacks of volume based profiles that maybe don't collate all exchange data or are misrepresented due to large unforeseen increases/decreases underlying capital inflows/outflows.
Thanks to @ALifeToMake, @Bjorgum, vgladkov on stackoverflow (and possibly some chatGPT!) for all the assistance in bringing this indicator to life. I really hope every user can find some use from this and help bring a unique and data driven perspective to their decision making. And make sure to please share any original implementaions of this tool too! If you've managed to apply this to the average price change once you've entered your position to better manage your trade management, or maybe overlaying on an implied volatility indicator to identify potential options arbitrage opportunities; let me know! And of course if anyone has any issues, questions, queries or requests please feel free to reach out! Thanks and enjoy.
VIX MTF MomentumSweet little momentum gadget to track the VIX Index.
What is the VIX?
The CBOE S&P 500 Volatility Index (VIX) is known as the 'Fear Index' which can measure how worried traders are that the S&P 500 might suddenly drop within the next 30 days.
When the VIX starts moving higher, it is telling you that traders are getting nervous. When the VIX starts moving lower, it is telling you that traders are gaining confidence.
VIX calculation?
The Chicago Board of Options Exchange Market Volatility Index (VIX) is a measure of implied volatility (Of the S&P 500 securities options), based on the prices of a basket of S&P 500 Index options with 30 days to expiration.
How to use:
If VIX Momentum is above 0 (RED) traders are getting nervous.
If VIX Momentum is below 0 (GREEN) traders are gaining confidence.
Follow to get updates and new scripts: www.tradingview.com
Risk RangeThis indicator creates risk ranges using implied volatility (VIX) or historical volatility, skewness ( Cboe SKEW or estimate ) and kurtosis.
Realized Variables for Options ComparisonThese variables can be used in comparison with the implied volatility of options.
Variables:
Realized Volatility
mathematical notation lowercase 'sigma'
Realized Variance
mathematical notation lowercase 'sigma' squared
Realized Beta
mathematical notation lowercase 'beta'
Timeframes:
Yearly = 250 or 365
Quarterly = 50 or 90
Monthly = 20 or 30
Important Note:
Options Contract Expiry = barmerge.lookahead_on
"Merge strategy for the requested data position. Requested barset is merged with current barset in the order of sorting bars by their opening time. This merge strategy can lead to undesirable effect of getting data from "future" on calculation on history. This is unacceptable in backtesting strategies, but can be useful in indicators."
[ All other timeframes barmerge.lookahead is disabled.
5EMA BollingerBand Nifty Stock Scanner
What ?
We all heard about (well: over-heard) 5-EMA strategy. Which falls into the broader category of mean reversal type of trading setup.
What is mean reversal?
Price (or any time series, in fact) tries to follow a mean . Whenever price diverges from the mean it tries to meet it back.
It is empirically observed by some traders (I honestly don't know who first time observed it) that in Indian context specially, 5 Exponential Moving Average (5-EMA) works pretty good as that mean.
So whenever price moves away from that 5-EMA, it ultimately comes back and attain total nirvana :) Means: if price moved way higher than the 5EMA without touching it, then price will correct to meet it's 5-EMA and if price moved way lower, it will be uplifted to meet it's 5-EMA. Funny - but it works !
Now there are already enough social media coverage on this 5-EMA strategy/setup. Even TradingView has some excellent work done on these setups. Kudos to all those great souls.
So when we came to know about this, we were thinking what we should do for the community. Because it is well cover topic (specially in Indian context). Also, there are public indicators.
Then we thought why not come up with a scanner which will scan all the Nifty-50 constituent stocks and find out on the fly, real-time which all stocks are matching this 5-EMA setup and causing a Buy/Sell trade recommendation.
Hence here we are with the first version of our first scanner on the 5EMA setup (well it has some more masala than merely a 5-EMA setup).
Why?
Parts of why is already covered up.
Now instead of blindly following 5-EMA setup, we added the Bollinger band as well. Again: it's also not new. There are enough coverage in social media about the 5-EMA+BB strategy/setup. We mercilessly borrowed from all of these.
Suppose you have an indicator.
Now you apply the indicator in your chart. And then you need to (rock) and roll through your watchlist of Nifty-50 stocks (note: TradingView has no default watchlist of Nifty-50 stock by default - you have to create one custom watchlist to list all manually) to find out which all are matching the setup, need to take a note about the trade recomendations (entry, SL, target) and other stuffs like VWAP, Volume, volatility (Bollinger Band Width).
Not any more.
This scanner will track all the Nifty-50 stocks (technically: 40 stocks other than Banking stocks) and provide which one to Buy or Sell (if any), what's the entry, SL, target, where is the VWAP of the day, what's the picture in volume (high, low, rising, falling) and the implied volatility (using Bolling band width). Also it has a naive alerting mechanism as well.
In fact the code is there to monitor the (Future) OI also and all the OI drama (OI vs price and all the 4 stuffs like long build up, long unwinding, short covering, short buildup). But unfortunately, due to some limitations of the TradingView (that one can not monitor more than 40 `ta.security` call) we have to comment out the code. If you wish you can monitor only 20 stocks and enable the OI monitoring also (20 for stocks + 20 for their OI monitoring .. total 40 `ta.security` call).
How?
To know the divergence from 5-EMA we just check if the high of the candle (on closing) is below the 5-EMA. Then we check if the closing is inside the Bollinger Band (BB). That's a Buy signal. SL: low of the candle, T: middle and higher BB.
Just opposite for selling. 5-EMA low should be above 5-EMA and closing should be inside BB (lesser than BB higher level). That's a Sell signal. SL: high of the candle, T: middle and lower BB.
Along with we compare the current bar's volume with the last-20 bar VWMA (volume weighted moving average) to determine if the volume is high or low.
Present bar's volume is compared with the previous bar's volume to know if it's rising or falling.
VWAP is also determined using `ta.vwap` built-in support of TradingView.
The Bolling Band width is also notified, along with whether it is rising or falling (comparing with previous candle).
Simple, but effective.
Customization
As usual the EMA setup (5 default), the BB setup (20 SMA with 1.5 standard deviation), we provided option wherther to include or exclude BB role in the 5-EMA setup (as we found out there are two schools of thought .. some people use BB some don't. Lets make all happy :))
We also provide options to choose other symbols using Settings if they wish so. We have the default 40 non banking Nifty stocks (why non-banking? - Bank Nifty is in ATH :) .. enough :)). But if user wishes can monitor others too (provided the symbol is there in TradingView).
Although we strongly recommend the timeframe as 30 minutes , you can choose what's fit you most.
The output of the scanner is a table. By default the table is placed in the right-bottom (as we are most comfortable with that). However you can change per your wish. We have the option to choose that.
What is unique in it ?
This is more of an indicator. This is a scanner (of Nifty-50 stocks). So you can apply (our recommendation is in 30m timeframe) it to any chart (does not matter which chart it is) and it will show every 30 mins (which is also configurable) which all stocks (along with trade levels) to Buy and Sell according to the setup.
It will ease your trading activity.
You can concentrate only on the execution, the filtering you can leave it to this one.
Limitations
There is a build in limitation of the TradingView platform is that one can call only upto 40 securities API. Not beyond that. So naturally we are constraint by that. Otherwise we could monitor 190 Nifty F&O stocks itself.
30m is the recommended timeframe. In very lower (say 5m) this script tends to go out of heap (out of memory). Please note that also.
How to trade using this?
Put any chart in 30m (recommended) timeframe.
Apply this screener from Indicators (shortcut to launch indicators is just type / in your keyboard).
This will provide the Buy (shown in green color) or Sell (shown in red color) recommendations in a table, at every 30m candle closing.
Note the volume and BB width as well.
Wait for at least 2 5-minutes candles to close above/below the recommended level .
Take the trade with the SL and target mentioned.
Mentions
@QuantNomad. The whole implementation concept we mercilessly borrowed from him, even some of his code snippet we took it (after asking him through one of his videos comment section and seeking explicit permission which he readily granted within an hour). Thank You sir @QuantNomad. Indebted to you.
Monika (Rawat) ji: for reviewing, correcting, providing real time examples during live market hours, often compromising her own trading activities, about the effectiveness and usefulness of this setup. Thank You madam ji. Indebted to you.
There are innumerable contents in social media about this. Don't even know whom all we checked. Thanks to all of them.
Happy Trading (in stocks - isn't enough of Indices already?)
Disclaimer
This piece of software does not come up with any warrantee or any rights of not changing it over the future course of time.
We are not responsible for any trading/investment decision you are taking out of the outcome of this indicator.
Options Price CalculatorIn the team, we continue to explore and expand the boundaries of TradingView.
For now, there is not much an options trader can do with options in TradingView.
We wanted to change that and created a simple option pricer.
You can set up in parameters a set of strikes, implied volatility, and days to expiry.
The indicators will take a risk-free rate from US01Y and the underlying price from your current chart.
It will compute prices and greeks for both put and call options.
Thanks to @MUQWISHI for helping code it.
Disclaimer
Please remember that past performance may not indicate future results.
Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting.
This post and the script don’t provide any financial advice.
VIX Rule of 16There’s an interesting aspect of VIX that has to do with the number 16. (approximately the square root of the number of trading days in a year).
In any statistical model, 68.2% of price movement falls within one standard deviation (1 SD ). The rest falls into the “tails” outside of 1 SD .
When you divide any implied volatility (IV) reading (such as VIX ) by 16, the annualized number becomes a daily number
The essence of the “rule of 16.” Once you get it, you can do all sorts of tricks with it.
If the VIX is trading at 16, then one-third of the time, the market expects the S&P 500 Index (SPX) to trade up or down by more than 1% (because 16/16=1). A VIX at 32 suggests a move up or down of more than 2% a third of the time, and so on.
• VIX of 16 – 1/3 of the time the SPX will have a daily change of at least 1%
• VIX of 32 – 1/3 of the time the SPX will have a daily change of at least 2%
• VIX of 48 – 1/3 of the time the SPX will have a daily change of at least 3%
MS VIX Bull ReversalThis script measures the rebound of the implied volatility of the S&P 500 index options from an excessive panic zone. The IV starts a reversion to the mean as soon as profit taking from the hedge begins. The assumption behind it: this rebound indicates at least the beginning of a countermovement, in uptrends the end of the correction and the trend continuation.
Daily Deviations (Self Input Version)
Plots the standard deviation resistance/support levels.
Input the previous settlement price and the implied volatility.
credit to u/UberBotMan and u/Living_Granger for the idea and formulas
(preview example is using settlement of 2420 and IV of 11)
Bionic -- Expected Weekly Levels (Public)This script will draw lines for Expected Weekly Levels based upon Previous Friday Close, Implied Volatility (EOD Friday), and the square root of Days to Expire (always 7) / 365.
Script will draw 2 high and low levels:
*1st levels are 1 standard deviation from the Previous Friday Close.
* 2nd levels are 2 standard deviation from the Previous Friday Close.
There are also a 1/2 Low and 1/2 Low 1st level. These are 1/2 a standard deviation and act more as a point of interest level. 1/2 levels have 34% probability.
Configurations:
* All lines styles are individually configurable
* All lines can individually be turned on/off
* Text for all lines can be changed
* Global config allows for the
* Lines to show the price on the label
* Lines to have text in the label
* Hide or show all labels
* Lines offset from price is configurable
* Label size is configurable
SPY Expected Move by VIXThis indicator shows 1 and 2 standard deviation price move from the VWAP based on VIX. Implied Volatility (IV) is being used extensively in the Option world to project the Expected Move for the underlying instrument. VIX is used as a proxy for SPY's IV for 30 days.
This indicator is meaningful only for SPY but can be used in any other instrument which has a strong correlation to SPY.
