Here we'll get past forex data and apply a model to predict if the market will close red or green in the following timestamps. I want to credit hayatoy with the project ml-forex-prediction under the MIT License. I was inspired to use a Gradient Boosting Classifier by this project, which was implemented using Python 2 and Yahoo Finance.

Skip to content. Star Predicting forex binary options using time series data and machine learning 63 stars 28 forks. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.

Branches Tags. Could not load branches. Could not load tags. A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch? Local Codespaces. HTTPS GitHub CLI. 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. Shlomi Schwartz. asked Sep 26, at Shlomi Schwartz Shlomi Schwartz 9, 29 29 gold badges silver badges bronze badges. Add a comment. Sorted by: Reset to default. Highest score default Trending recent votes count more Date modified newest first Date created oldest first.

There is not a silver bullet, but some principles apply: The reason for normalization is so that no feature overly dominates the gradient of the loss function. Some algorithms are better at dealing with unnormalized features than others, I think, but in general if your features have vastly different scales you could get in trouble. So normalizing to the range 0 - 1 is sensible.

You want to maximize the entropy of your features, to help the algorithm seperate the examples. You achieve this by spreading the values as much as possible over the given range Sometimes it could be valuable to scale some parts of the feature space differently than others. For example, if there are ten versions, but 6 are essentially the same with the other four being very different among each other, then it might make sense to scale such that the first six versions are close together and the rest more spread out.

If you are doing cross-validation, scale the folds seperately or you will have trained a part of the whole with test data. Some algorithms Naive Bayes comes to mind don't work with continuous values at all, but rather categorical values. Make sure you know what your chosen algorithm can work with. Improve this answer. answered Sep 26, at kutschkem kutschkem 7, 3 3 gold badges 18 18 silver badges 51 51 bronze badges.

Hello, today I am going to try to explain some methods that we can use to identify which Machine Learning Model we can use to deal with binary classification. As you know there are plenty of machine learning models for binary classification, but which one to choose, well this is the scope of this blog, try to give you a solution. Let us follow some useful steps that may help you to choose the best machine learning model to use in you binary classification.

The first step to follow is understand the data that you will use to create you machine learning model. Until you have understood very well you source data you can identify which model should be the best. In order to analyze the data you should clean the data, this allows you identify patterns of the data. A very simple way to understand better the data is through pictures. But attention, not redundant data. This is important, because , it is common that in Data Science, people likes to do a lot a plots but some plots are unnecessary or they repeat the same information several times.

Use fancy plots does not mean that you can understand better. The data-ink ratio is the proportion of Ink that is used to present actual data compared to the total amount of ink or pixels used in the entire display. Good graphics should include only data-Ink. Non-Data-Ink is to be deleted everywhere where possible.

Here we need to remember some basic aspects of the possible machine learning candidates to use. And identify if your dataset features may satisfay the requirements of the machine learning model to be used. Binary variables are widely used in statistics to model the probability of a certain class or event taking place.

Analogous linear models for binary variables with a different sigmoid function instead of the logistic function to convert the linear combination to a probability. A sigmoid function is a bounded, differentiable, real function that is defined for all real input values and has a non-negative derivative at each point and exactly one inflection point. Well, if the distribution of the data may be distributed this logistic function, or like the sigmoid function, the the outputs may behave as the previous two formulas then this may be a good candidate to test.

The logistic regression is a probabilistic approach. Support vector machine is based on statistical approaches. Her we try to find a hyperplane that best separates the two classes. SVM finding the maximum margin between the hyperplanes that means maximum distances between the two classes. When the data is not linearly separable then we can use Non-Linear SVM, which means when the data points cannot be separated into 2 classes by using a a linear approach.

SVM is helpful when you have a simple pattern of data, and you can find this hyperplane that allows this separation of the 2 classes. An interesting point of SVM that you can use Non-Linear SVM that can be used to separate the classses by using a kernel, and with a Decision surface we can obtain this separation of the 2 classes. We can use SVM when a number of features are high compared to a number of data points in the dataset. By using the correct kernel and setting an optimum set of parameters.

It is effective in high dimensional spaces. Still effective in cases where number of dimensions is greater than the number of samples. Uses a subset of training points in the decision function called support vectors , so it is also memory efficient. Naïve Bayes is a probabilistic machine learning algorithm based on the Bayes Theorem, used in a wide variety of classification tasks.

Conditional probability is a measure of the probability of an event occurring given that another event has by assumption, presumption, assertion, or evidence occurred. The fundamental Naïve Bayes assumption is that each feature makes an: independent and equal contribution to the outcome. What it does mean that? This mean that when you have several features and they are independent, they are not correlated, and none of the attributes are irrelevant and assumed to be contributing Equally to the outcome.

Due to the independence assumption is never correct we call Naive. This model works particularly well with natural language processing NLP problems. Because we can assume. a Multinomial Naïve Bayes Classifier Feature vectors represent the frequencies with which certain events have been generated by a multinomial distribution.

This is the event model typically used for document classification. b Bernoulli Naïve Bayes Classifier In the multivariate Bernoulli event model, features are independent booleans binary variables describing inputs. Like the multinomial model, this model is popular for document classification tasks, where binary term occurrence i. a word occurs in a document or not features are used rather than term frequencies i. frequency of a word in the document.

c Gaussian Naïve Bayes Classifier In Gaussian Naïve Bayes, continuous values associated with each feature are assumed to be distributed according to a Gaussian distribution Normal distribution.

K-NN algorithm stores all the available data and classifies a new data point based on the similarity. This means when new data appears then it can be easily classified into a well suite category by using K- NN algorithm.

K-NN algorithm can be used for Regression as well as for Classification but mostly it is used for the Classification problems. K-NN is a non-parametric algorithm , which means it does not make any assumption on underlying data. It is also called a lazy learner algorithm because it does not learn from the training set immediately instead it stores the dataset and at the time of classification, it performs an action on the dataset.

KNN algorithm at the training phase just stores the dataset and when it gets new data, then it classifies that data into a category that is much similar to the new data. Suppose there are two categories, i.

To solve this type of problem, we need a K-NN algorithm. With the help of K-NN, we can easily identify the category or class of a particular dataset. Consider the below diagram:. It is calculated the Euclidean distance of K number of neighbors and taken the K nearest neighbors as per the calculated Euclidean distance.

Among these k neighbors, count the number of the data points in each category. And finally we assign the new data points to that category for which the number of the neighbor is maximum.

Otherwise we have to determine the value of K which may be complex some time and the computation cost is high because of calculating the distance between the data points for all the training samples. Decision trees is used to make predictions by going through each and every feature in the data set, one-by-one. The decision tree is like a tree with nodes. The branches depend on a number of factors. It splits data into branches like these till it achieves a threshold value. A decision tree consists of the root nodes, children nodes, and leaf nodes.

Random forests on the other hand are a collection of decision trees being grouped together and trained together that use random orders of the features in the given data sets. The goal of using a Decision Tree is to create a training model that can use to predict the class or value of the target variable by learning simple decision rules inferred from prior data training data. In Decision Trees, for predicting a class label for a record we start from the root of the tree.

On the basis of comparison, we follow the branch corresponding to that value and jump to the next node. However you should take into account that Decision tree models are often biased toward splits on features having a large number of levels. Small changes in the training data can result in large changes to decision logic and large trees can be difficult to interpret and the decisions they make may seem counter intuitive.

Deep learning can be used for binary classification, too. In fact, building a neural network that acts as a binary classifier is little different than building one that acts as a regressor. Neural networks are multi layer peceptrons. By stacking many linear units we get neural network. Neural Networks are remarkably good at figuring out functions from X to Y.

Computation of NN is done by forward propagation for computing outputs and Backward pass for computing gradients. Activation Functions: The following activation functions helps in transforming linear inputs to nonlinear outputs. If we apply linear activation function we will get linear seperable line for classifying the outputs. The main reason why we use sigmoid function is because it exists between 0 to 1.

Therefore, it is especially used for models where we have to predict the probability as an output. Since probability of anything exists only between the range of 0 and 1, sigmoid is the right choice. The advantage is that the negative inputs will be mapped strongly negative and the zero inputs will be mapped near zero in the tanh graph.

The ReLU is the most used activation function in the world right now. Since, it is used in almost all the convolutional neural networks or deep learning. In general we use softmax activation function when we have multiple ouput units.

For example for predicting hand written digits we have 10 possibilities. We have 10 output units, for getting the 10 probabilities of a given digit we use softmax. Regression : When actual Y values are numeric. Eg: Price of house as output variable, range of price of a house can vary within certain range. For regression problems: For regression problems we generally use RMSE as loss function. Classification binary : When the given y takes only two values. e 0 or 1 Eg: Whether the person will buy the house and each class is mutually exclusive.

For binary Classification problems: For binary classification proble we generally use binary cross entropy as loss function. A neural network topology with many layers offers more opportunity for the network to extract key features and recombine them in useful nonlinear ways. We can evaluate whether adding more layers to the network improves the performance easily by making another small tweak to the function used to create our model. Usually we use neural networks when we do forecasting and time series applications, sentiment analysis and other text applications.

Example: Banks generally will not use Neural Networks to predict whether a person is creditworthy because they need to explain to their customers why they denied them a loan. Long story short, when you need to provide an explanation to why something happened, Neural networks might not be your best bet.

Language: All Filter by language. js Collect. Improve this answer. Consider the below diagram: The K-NN is based on the K number of neighbors, where we select the number K of the neighbors It is calculated the Euclidean distance of K number of neighbors and taken the K nearest neighbors as per the calculated Euclidean distance. Spam Detection by using Natural Language Processing NLP.

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