Web12/05/ · 1 Answer. Try using a boolean index to get and set the values of the arrays, which avoids the nested for loop. import numpy as np from numpy import random # Web29/02/ · Jan 30, at 1. That's normal. It returns a reference to the image axes. It's just telling you that imshow returns an AxesImage object. You can assign this Web19/08/ · @ap - The duplicate question was limiting the scope of the answer to the Python Imaging Library, when there are other ways to answer the question. – WebImage processing, as the name suggests, is a method of doing some operation (s) on the image. You would have also heard of another term called ‘Computer Vision. The Web05/06/ · I'm trying to send a dummy 2x2px binary image/png file through a POST request to an AWS API I created, which in turn relays it to AWS Lambda service. The ... read more

We can get some nice mathemetatical intuitions regarding option pricing through studying binaries, which I hope to share with you today. In this article we will give an explanation of the mathematics behind binary option pricing along with a Python implementation for closed form and Monte Carlo pricing techniques. To finish off this article we will then give an example of getting the implied distribution of the stock price at expiration using binary options. It is worth mentioning at this point, that Binary options have been the subject of much controversy with regulators having worries about protecting investors from what is often outright fraud.

Countries such as Canada, Germany and Israel have went as far as outright banning the sale of binary options to retail clients. In the United Kingdom, at one stage binary options were regulated by the Gambling Commission FCA regulated now hopefully this illustrates the point that the author does not recommend trading binary options unless serious due diligence is done.

This article should be viewed as an educational resource as opposed to a promotion of trading these instruments for real money. A possible rule of thumb for discriminating between options providers is : Do they offer products that with an expiry of less than 1 minute? If yes, then it might be better to find another broker.

Consider an option that pays a fixed amount x conditional upon some event occurring in the market. The reader may realize that it is useful to consider the question above as a probability question, in that we are asking how often would the stock finish above the strike. First we will calculate this by simulation as this is perhaps the most intuitive way to look at a problem of this nature.

Below are the steps to complete this pricing method. Note we are assuming a log-normal distribution of stock prices at expiry, which is rather unrealistic but should serve to illustrates the concept. See this article on where it comes from. Let N in the second line below be the number of draws to take from the distribution. Below we simulate 10 million terminal stock prices, this should be sufficient to get a good approximation of the true distribution of stock prices at expiration.

Imagine zipping along the x axis of the histogram above, and adding one to the total if the stock price from the draw is greater than the strike. We then count the number of ones and divide this sum by the number of draws which is 10 million in this case. The formula below represents the probability the stock is above the strike at expiration. Arguably we should we using an integral here as in the previous simulation but hopefully this way is more intuitive.

The script below shows that the simulation approximates this probability as This should not be confused with the risk-neutral probability. Although viewing the formula here should give a good intuition as to what exactly a risk-neutral probability actually is when we encounter it later on in the article. From the script above we see that the stock will be greater than the strike approximately We can also use the Black-Scholes formula to price binary options, for this we will need the d2 from the previous article.

The formulae for calls and puts are given below. Let's just take a moment to equate some concepts from the Monte-Carlo method we discussed. Notice that we can recover the probability value we got from the Monte-Carlo simulation by the following. And Pricing our example option we get approximately the same value. Connect and share knowledge within a single location that is structured and easy to search. I am new to python, I have a binary image file unsigned bit format and x pixel in size ,but while using a python code I am getting error "ValueError: The truth value of an array with more than one element is ambiguous.

Use a. any or a. all ". show does not take an array as an argument. It just shows what you've already plotted with imshow. Stack Overflow for Teams — Start collaborating and sharing organizational knowledge. Create a free Team Why Teams? Learn more about Collectives. Learn more about Teams. Read and display a binary image file in python Ask Question. Asked 8 years, 10 months ago. Modified 6 years, 9 months ago. Viewed 7k times.

fromfile 'raw. reshape [, ] pt. imshow I pt. show I can anybody please tell me where I am doing wrong? python image-processing binary. Like Article. Difficulty Level : Basic Last Updated : 17 Jul, Read Discuss Courses Practice Video. open Opens and identifies the given image file. from PIL import Image. open method used to open different extension image file.

This method will show image in any image viewer. Please Login to comment Previous Working with Images in Python. Next Python PIL Image. save method. Python PIL GaussianBlur method. Python PIL Kernel method. Python PIL BoxBlur method. Python PIL getbands method.

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A binary option is a type of derivative in which a fixed payoff is received should the asset reach a certain level at expiration. A binary option with a payoff of 1 is often known as a digital option. These options are very similar to bets due to their relative simplicity. We can get some nice mathemetatical intuitions regarding option pricing through studying binaries, which I hope to share with you today.

In this article we will give an explanation of the mathematics behind binary option pricing along with a Python implementation for closed form and Monte Carlo pricing techniques. To finish off this article we will then give an example of getting the implied distribution of the stock price at expiration using binary options.

It is worth mentioning at this point, that Binary options have been the subject of much controversy with regulators having worries about protecting investors from what is often outright fraud. Countries such as Canada, Germany and Israel have went as far as outright banning the sale of binary options to retail clients.

In the United Kingdom, at one stage binary options were regulated by the Gambling Commission FCA regulated now hopefully this illustrates the point that the author does not recommend trading binary options unless serious due diligence is done.

This article should be viewed as an educational resource as opposed to a promotion of trading these instruments for real money. A possible rule of thumb for discriminating between options providers is : Do they offer products that with an expiry of less than 1 minute? If yes, then it might be better to find another broker. Consider an option that pays a fixed amount x conditional upon some event occurring in the market. The reader may realize that it is useful to consider the question above as a probability question, in that we are asking how often would the stock finish above the strike.

First we will calculate this by simulation as this is perhaps the most intuitive way to look at a problem of this nature. Below are the steps to complete this pricing method. Note we are assuming a log-normal distribution of stock prices at expiry, which is rather unrealistic but should serve to illustrates the concept.

See this article on where it comes from. Let N in the second line below be the number of draws to take from the distribution. Below we simulate 10 million terminal stock prices, this should be sufficient to get a good approximation of the true distribution of stock prices at expiration. Imagine zipping along the x axis of the histogram above, and adding one to the total if the stock price from the draw is greater than the strike.

We then count the number of ones and divide this sum by the number of draws which is 10 million in this case. The formula below represents the probability the stock is above the strike at expiration. Arguably we should we using an integral here as in the previous simulation but hopefully this way is more intuitive. The script below shows that the simulation approximates this probability as This should not be confused with the risk-neutral probability.

Although viewing the formula here should give a good intuition as to what exactly a risk-neutral probability actually is when we encounter it later on in the article. From the script above we see that the stock will be greater than the strike approximately We can also use the Black-Scholes formula to price binary options, for this we will need the d2 from the previous article.

The formulae for calls and puts are given below. Let's just take a moment to equate some concepts from the Monte-Carlo method we discussed. Notice that we can recover the probability value we got from the Monte-Carlo simulation by the following. And Pricing our example option we get approximately the same value. Increasing the Ndraws parameter will reduce this error, however we see below it is fairly accurate and they are in fact measuring the same quantity.

The formula for pricing a binary put option is given below, in this case we are measuring the probability of the stock being below the strike price. Let's try that formula out on pricing a put option with the same parameters as the call we have used throughout this article. Now consider if we could have inferred this value without actually using either formula.

Since we know that the problem is binary i. one of the two events must occur, the stock is either above the strike or below it, the following relationship must hold. To adjust this for a risk neutrality argument we can state the equality shown below.

Clearly once we know the price of a binary call option we can then infer the price of the put. In this mini project we will take some of the things we have learned about binary options and apply them to some real market data.

It may be useful to read this article on implied volatility if you are unfamiliar with the concept. The goal of this section is to create a cdf and pdf of the market's expectations regarding the price of Apple stock on the 19 th of February. To follow along you can either download the market data yourself from github here or you can simply download it using Pandas as shown below. Could be more accurate admittedly. Feel free to try it on different data. Here we use a polynomial fit with degree 5 to get our new implied volatility values.

Since the highest and lowest strike available is and 55 respectively we are going to extrapolate for values between 1 - While we do suspect that values towards the end of this distribution are highly likely to be much higher in real life, we will use the following model simply for illustrative purposes.

So what we have now is a method to approximate the appropriate volatility values from the data we collected from Yahoo Finance. The reader is encouraged to play around with the function below and compare it with the plot above. Create Risk Neutral Cumulative Distribution Function for Stock Price at Expiration. To create a cdf we will want to calculate the weight to the left of the given point, the aforementioned point here is the strike.

Referring back to the examples at the beginning of the document we know to calculate this value we can use a digital put option. However, it is useful for illustrative purposes. We will also add a constant volatility distribution i. However, the market doesn't agree with this idea, perhaps we can interpret this as the risk rare events such as war , natural disaster etc. Let's explore what we can do with this distribution now that we have it.

Let's see how we can calculate the probability that the stock is within a certain interval on the expiration date. So according to the market there is a Recall the strategies illustrated in previous articles here and here.

Hopefully this article has helped you make a connection between probabilities implied by option prices and also an intuitive understanding of risk-neutral probabilities and what they actually mean. Menu Binary Options and Implied Distributions with Python John December 28, A binary option is a type of derivative in which a fixed payoff is received should the asset reach a certain level at expiration.

Contents In this article we will give an explanation of the mathematics behind binary option pricing along with a Python implementation for closed form and Monte Carlo pricing techniques.

Warning It is worth mentioning at this point, that Binary options have been the subject of much controversy with regulators having worries about protecting investors from what is often outright fraud. With that said let's begin! Simulation Method Consider an option that pays a fixed amount x conditional upon some event occurring in the market. So the question is now how to price such as instrument?

xlim [50,] plt. ylabel 'Frequency' plt. title 'Stock Simulation' 2 Calculate how often The stock is greater than the strike price. setp p, 'facecolor', 'green' else: plt. cdf return np. Implied Probability Distribution from Market Data In this mini project we will take some of the things we have learned about binary options and apply them to some real market data.

csv' print df. Could be more accurate admittedly 4 It is not clear which value Yahoo Finance uses to calculate implied volatility, however, we won't be dealing with market prices and therefore are making some unrealistic assumptions in order to illustrate the concept.

arange 1, ,0. poly1d poly newK fit model to new higher resolution strikes plt. plot newK, newVols plt. title 'Implied Volatility Function' plt. ylabel 'Implied Vol' So what we have now is a method to approximate the appropriate volatility values from the data we collected from Yahoo Finance.

ylabel 'cdf' plt. title 'Cdf of Apple on 19th Feb ' plt. append p plt. legend plt. sum 0. title 'Probability Interval' plt. xticks np. arange 0,,

Web17/07/ · blogger.com () Opens and identifies the given image file. This is a lazy operation; this function identifies the file, but the file remains open and the actual image Web28/12/ · Binary Options and Implied Distributions with Python. A binary option is a type of derivative in which a fixed payoff is received should the asset reach a certain Web29/02/ · Jan 30, at 1. That's normal. It returns a reference to the image axes. It's just telling you that imshow returns an AxesImage object. You can assign this Web05/06/ · I'm trying to send a dummy 2x2px binary image/png file through a POST request to an AWS API I created, which in turn relays it to AWS Lambda service. The Web12/05/ · 1 Answer. Try using a boolean index to get and set the values of the arrays, which avoids the nested for loop. import numpy as np from numpy import random # WebImage processing, as the name suggests, is a method of doing some operation (s) on the image. You would have also heard of another term called ‘Computer Vision. The ... read more

Proposing a Community-Specific Closure Reason for non-English content. Previous Working with Images in Python. show print "LBP Program is finished". Easy Normal Medium Hard Expert. We use cookies to ensure you have the best browsing experience on our website. set it to 1.

Contents In this article we will give an explanation of the mathematics behind binary option pricing along with a Python implementation for closed form and Monte Carlo pricing techniques.