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Black-Scholes Model and Greeks for European OptionsThe Black-Scholes model is a mathematical model used for pricing options. From this model you can derive the theoretical fair value of a European option (an option where you have to wait until expiry to exercise). Additionally, you can derive various risk parameters called Greeks. This indicator includes three types of data: Theoretical Option Price (blue), the Greeks (green), and implied volatility (red); their values are presented in that order. 1) Theoretical Option Price: This first value gives only the theoretical fair value of an option with a given strike based on the Black-Scholes framework. Remember this is a model and does not reflect actual option prices, just the theoretical price based on the Black-Scholes model and its parameters and assumptions. 2)Greeks (all of the Greeks included in this indicator are listed below): a)Delta is the rate of change of the theoretical option price with respect to the change in the underlying's price. This can also be used to approximate the probability of your option expiring in the money. For example, if you have an option with a delta of 0.62, then it has about a 62% chance of expiring in-the-money. This number runs from 0 to 1 for Calls, and 0 to -1 for Puts. b)Gamma is the rate of change of delta with respect to the change in the underlying's price. c)Theta, aka "time decay", is the rate of change in the theoretical option price with respect to the change in time. Theta tells you how much an option will lose its value day by day. d)Vega is the rate of change in the theoretical option price with respect to change in implied volatility. e)Rho is the rate of change in the theoretical option price with respect to change in the risk-free rate. Rho is rarely used because it is the parameter that options are least effected by, it is more useful for longer term options, like LEAPs. f)Vanna is the sensitivity of delta to changes in implied volatility. Vanna is useful for checking the effectiveness of delta-hedged and vega-hedged portfolios. g)Charm, aka "delta decay", is the instantaneous rate of change of delta over time. Charm is useful for monitoring delta-hedged positions. h)Vomma measures the sensitivity of vega to changes in implied volatility. i)Veta measures the rate of change in vega with respect to time. j)Vera measures the rate of change of rho with respect to implied volatility. k)Speed measures the rate of change in gamma with respect to changes in the underlying's price. Speed can be used when evaluating delta-hedged and gamma hedged portfolios. l)Zomma measures the rate of change in gamma with respect to changes in implied volatility. Zomma can be used to evaluate the effectiveness of a gamma-hedged portfolio. m)Color, aka "gamma decay", measures the rate of change of gamma over time. This can also be used to evaluate the effectiveness of a gamma-hedged portfolio. n)Ultima measures the rate of change in vomma with respect to implied volatility. o)Probability of Touch, is not a Greek, but a metric that I included, which tells you the probability of price touching your strike price before expiry. 3) Implied Volatility: This is the market's forecast of future volatility. Implied volatility is directionless, it cannot be used to forecast future direction. All it tells you is the forecast for future volatility. How to use this indicator: 1st. Input the strike price of your option. If you input a strike that is more than 3 standard deviations away from the current price, the model will return a value of n/a. 2nd. Input the current risk-free rate.(Including this is optional, because the risk-free rate is so small, you can just leave this number at zero.) 3rd. Input the time until expiry. You can enter this in terms of days, hours, and minutes. 4th.Input the chart time frame you are using in terms of minutes. For example if you're using the 1min time frame input 1, 4 hr time frame input 480, daily time frame input 1440, etc. 5th. Pick what type of option you want data for, Long Call or Long Put. 6th. Finally, pick which Greek you want displayed from the drop-down list. *Remember the Option price presented, and the Greeks presented, are theoretical in nature, and not based upon actual option prices. Also, remember the Black-Scholes model is just a model based upon various parameters, it is not an actual representation of reality, only a theoretical one.
Pine Script® göstergesi
SegaRKO tarafından
5
Trend Pulse Pro V2Trend Pulse Pro V2 (Non Repaint) TradingView Account Needed: Free You don't need to be an expert to use Trend Pulse Pro. Just follow the signals and that's all and use simple Fibonacci levels to find stop loss and take profit! You can increase the win rate even more by following some simple technical analysis, for example, when trend breaks (price break signal level) you can use that as support become resistance level (sell) or resistance becomes support (buy): No complex things. No waste of time. And although you won't win 100% of the trades (no signals can guarantee that), your trading will surely improve a lot! Even someone with almost no trading experience can read the simple trading rules given in the included user guide, watch the how-to videos and follow Trend Pulse Pro’s signals to consistently make smarter trades. With Trend Pulse Pro you will get accurate buy and sell signals every time there is a new trading opportunity so that you never miss any big price movements and makes your trading both easier and more profitable. How to use the signals and alerts: Trend Pulse Pro automatically analyze trend and price action to give you a signal when there's a good trade. These trend signals and alerts are derived from live data but the stability of our code allows it to not repaint. Trend Pulse Pro allow you to easily determine the trend and will give you buy and sell entry levels. Works on all markets on all time frames so it's suitable for scalpers, day and swing traders. If you want more details, the link is in the signature. Disclaimer: Past performance is not indicative of future performance. No representation is being made that any results discussed within the service and its related media content will be achieved. All opinions, news, research, analyses, prices or other information is provided as general market commentary and not as investment advice. TradingWalk, their members, shareholders, employees, agents, representatives and resellers do not warrant the completeness, accuracy or timeliness of the information supplied, and they shall not be liable for any loss or damages, consequential or otherwise, which may arise from the use or reliance of the TradingWalk service and its content. © 2019 TradingWalk. TradingWalk indicators are built for TradingView. TradingWalk is on no way a part of TradingView.
Pine Script® göstergesi
tradingwalk tarafından
192
MM-Microtrend-Reversal-IndicatorThis indicator detects microtrend reversals based on crossing of the DMI+ and DMI- + signals It indicates the trigger event on the chart as well as the reversal candle In addition to that it shows a countdown above the bars This is helping when multiple reversals occur Important: when the Bollinger Bands are crossed in the previous bars it's possible that the prognosted reversal will NOT show up Recommended timeframes are M1 and M2 Avoid trading sideway trends, the reverals are low and reversal triggers and assumed reversal candles could get chaotic
Pine Script® göstergesi
Meunier tarafından
28
MM-Microtrend-Reversal-IndicatorThis indicator detects microtrend reversals based on crossings of the DMI+ and DMI- signals It indicates the trigger event on the chart as well as the reversal candle In addition to that it shows a countdown above the bars This is helping when multiple reversals occur. Important: when the Bollinger Bands are crossed in the previous bars it's possible that the prognosted reversal will NOT show up Recommended timeframes are M1 and M2 Avoid trading sideway trends, the reversals are low and reversal triggers and assumed reversal candles could get chaotic sometimes
Pine Script® göstergesi
Meunier tarafından
2
Chatterjee CorrelationThis is my first attempt on implementing a statistical method. This problem was given to me by @lejmer (who also helped me later on building more efficient code to achieve this) when we were debating on the need for higher resource allocation to run scripts so it can run longer and faster. The major problem faced by those who want to implement statistics based methods is that they run out of processing time or need to limit the data samples. My point was that such things need be implemented with an algorithm which suits pine instead of trying to port a python code directly. And yes, I am able to demonstrate that by using this implementation of Chatterjee Correlation. 🎲 What is Chatterjee Correlation? The Chatterjee rank Correlation Coefficient (CCC) is a method developed by Sourav Chatterjee which can be used to study non linear correlation between two series. Full documentation on the method can be found here: arxiv.org In short, the formula which we are implementing here is: Algorithm can be simplified as follows: 1. Get the ranks of X 2. Get the ranks of Y 3. Sort ranks of Y in the order of X (Lets call this SortedYIndices) 4. Calculate the sum of adjacent Y ranks in SortedYIndices (Lets call it as SumOfAdjacentSortedIndices) 5. And finally the correlation coefficient can be calculated by using simple formula CCC = 1 - (3*SumOfAdjacentSortedIndices)/(n^2 - 1) 🎲 Looks simple? What is the catch? Mistake many people do here is that they think in Python/Java/C etc while coding in Pine. This makes code less efficient if it involves arrays and loops. And the simple code may look something like this. var xArray = array.new() var yArray = array.new() array.push(xArray, x) array.push(yArray, y) sortX = array.sort_indices(xArray) sortY = array.sort_indices(yArray) SumOfAdjacentSortedIndices = 0.0 index = array.get(xSortIndices, 0) for i=1 to n > 1? n -1 : na indexNext = array.get(sortX, i) SumOfAdjacentSortedIndices += math.abs(array.get(sortY, indexNext)-array.get(sortY, index)) index := indexNext correlation := 1 - 3*SumOfAdjacentSortedIndices/(math.pow(n,2)-1) But, problem here is the number of loops run. Remember pine executes the code on every bar. There are loops run in array.sort_indices and another loop we are running to calculate SumOfAdjacentSortedIndices. Due to this, chances of program throwing runtime errors due to script running for too long are pretty high. This limits greatly the number of samples against which we can run the study. The options to overcome are Limit the sample size and calculate only between certain bars - this is not ideal as smaller sets are more likely to yield false or inconsistent results. Start thinking in pine instead of python and code in such a way that it is optimised for pine. - This is exactly what we have done in the published code. 🎲 How to think in Pine? In order to think in pine, you should try to eliminate the loops as much as possible. Specially on the data which is continuously growing. My first thought was that sorting takes lots of time and need to find a better way to sort series - specially when it is a growing data set. Hence, I came up with this library which implements Binary Insertion Sort. Replacing array.sort_indices with binary insertion sort will greatly reduce the number of loops run on each bar. In binary insertion sort, the array will remain sorted and any item we add, it will keep adding it in the existing sort order so that there is no need to run separate sort. This allows us to work with bigger data sets and can utilise full 20,000 bars for calculation instead of few 100s. However, last loop where we calculate SumOfAdjacentSortedIndices is not replaceable easily. Hence, we only limit these iterations to certain bars (Even though we use complete sample size). Plots are made for only those bars where the results need to be printed. 🎲 Implementation Current implementation is limited to few combinations of x and fixed y. But, will be converting this into library soon - which means, programmers can plug any x and y and get the correlation. Our X here can be Average volume ATR And our Y is distance of price from moving average - which identifies trend. Thus, the indicator here helps to understand the correlation coefficient between volume and trend OR volatility and trend for given ticker and timeframe. Value closer to 1 means highly correlated and value closer to 0 means least correlated. Please note that this method will not tell how these values are correlated. That is, we will not be able to know if higher volume leads to higher trend or lower trend. But, we can say whether volume impacts trend or not. Please note that values can differ by great extent for different timeframes. For example, if you look at 1D timeframe, you may get higher value of correlation coefficient whereas lower value for 1m timeframe. This means, volume to trend correlation is higher in 1D timeframe and lower in lower timeframes.
Pine Script® göstergesi
Trendoscope tarafından
10