OptionGreeks is an educational tool to help users understand option pricing. Options are derivative instruments, which can be traded on stock markets / exchanges around the world. They are derivatives in that their values and contracts are derived from the “price” of some other financial instrument (including individual shares, bonds, commodities, exchange rates, interest rates etc., or indices of these). Options come in many variants, but this app focuses on European options (only exercisable at expiry). It covers Calls and Puts, from the buyers (long) and sellers (short) perspectives.
The calculator will calculate the theoretical price of options, given the users choice of the relevant parameters which include the spot price of the underlying instrument, the strike price of the option, the risk-free interest rate (discount rate), the volatility of the underlying spot price, the yield of the underlying instrument, and the term to maturity (or expiry) of the option. It will also calculate the values of the “Greeks”, which includes delta, gamma, theta, rho, vega, and epsilon. It also displays all of these in charts against each of the underlying parameters.
Finally, it also includes many different option strategies (a combination of one or more options, as well as the underlying asset), so that you can see the payoff profile, profit and the greeks for these combinations.
MLFotoFun uses state of the art Machine Learning models that use Convolutional Neural Networks (CNNs) to classify photos taken with your camera or from your library using one of eight state of the art models. The classification includes the two most probable descriptive labels, as well as the probability associated with each label.
The eight models include: AgeNet (that classifies the age of the human subject); GenderNet (that classifies the gender of the subject); CNN Emotions (that classifies the emotion of the person); VisualSentiment (that classifies the human subject’s sentiment as positive or negative); Food101 (that classifies the food), Oxford102 (that classifies flowers); CarRecognition (that classifies the make of car); and GoogLeNetPlaces (that classifies the category of place in the image).
This app is for entertainment purposes only, and clearly demonstrates how bad such models can be, as well as the biases that they may contain, so no offence is intended with age or gender classification. It may however also surprise you in how far image recognition has come in the five years.
Photo Classifier uses state of the art Machine Learning models that use Convolutional Neural Networks (CNNs) to classify photos taken with your camera or from your library. The classification includes both a descriptive label of the scene, as well as the probability associated with the label.
Six models have been included, and the app provides the output from each of the models in a fraction of a second each. You can see more detail on the top five predictions for each model, along with the probability of each prediction.
This is a simple but very powerful tool that demonstrates how far artificial intelligence and machine learning has come, especially the power of deep neural networks and specifically, convolutional neural networks. These models are all freely available from the internet under the licenses provided by the links below.
GoogLeNetPlaces: Creative Common License. More information available at http://places.csail.mit.edu
Resnet50: MIT License. More information available at https://github.com/fchollet/keras/blob/master/LICENSE
Inceptionv3: MIT License. More information available at https://github.com/fchollet/keras/blob/master/LICENSE
VGG16: Creative Commons Attribution 4.0 International(CC BY 4.0). More information available at https://creativecommons.org/licenses/by/4.0/
SqueezeNet: BSD License. More information available at https://github.com/DeepScale/SqueezeNet/blob/master/LICENSE
MobileNet: Apache License. Version 2.0 http://www.apache.org/licenses/LICENSE-2.0
This app calculates the probabilities of certain performance outcomes in investment management, based on some simplifying assumptions.
It calculates the probability of a single manage outperforming the benchmark over various time periods (from 1 month to 20 years), given a user selected manager skill level (given by the Information Ratio).
It also calculates the joint probability of none, or at least one manager, underperforming the benchmark. The user can select the skill level (equal for all the managers), the time period, and the number of managers.
It also calculates the probability of a single manager outperforming the benchmark by a certain alpha target (user selected) given a user selected tracking error, under the assumption that the manager has no skill i.e. an Information Ratio of 0.
It now includes probability density and mass functions, to help visualise the distributions of the outcomes i.e. the probabilities.
This app provides a timer for High Intensity Interval Training. It allows you to select the training time and resting time for each set, and allows you to select the sets per exercise. The timer provides both a visual countdown display of the time remaining, for each set, and an audible beeping for the last five seconds of each set. You therefore know when a set ends without even looking at the display, allowing you to focus on your training.
There are displays for the number of exercises, and sets, as well as the total time of exercising. There is a start and pause button, and you can change the training time and resting time while exercising, allowing you to mix and match exercise and resting times.