Bidirectional lstm stock price

Machine learning has found its applications in many interesting fields over these years. Taming stock market is one of them.

I had been thinking of giving it a shot for quite some time now; mostly to solidify my working knowledge of LSTMs. And finally I have finished the project and quite excited to share my experience. I will write about my experience over a series of blogs.

But I will be sharing links to such articles, wherever I feel like background knowledge might be missing. So the real purpose of this article is to share such steps, my mistakes and some steps that I found very helpful.

As such, this article is not limited to Stock Price Prediction problem. Here are the things we will look at :. Please note that this first article talks about preprocessing steps and terminologies of LSTM. If you are fairly confident about these steps, you can skip to next article.

I will be using the historical stock price data for GE for this post.

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You can find the data in my kaggle site here. We can read the data into frame as shown below :. Lets see how does it look on a plot :. Did you see something interesting? There is quite a surge in the number of transactions around th day on the timeline, which happens to coincide with the sudden drop of stock price.

May be we can go back to that particular date and dig up old news articles to find what caused it. The data is not normalized and the range for each column varies, especially Volume. Normalizing data helps the algorithm in converging i. But before that we have to split the dataset into training and testing datasets. Also I will convert the DataFrame to ndarray in the process. This is quite important and somewhat tricky. This is where the knowledge LSTM is needed. Samples 0 to 2 would be our first input and Close price of sample 3 would be its corresponding output value; both enclosed by green rectangle.

Similarly samples 1 to 3 would be our second input and Close price of sample 4 would be output value; represented by blue rectangle.In this notebook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results.

We use LSTM for the obvious reason that we are trying to predict time series data. That is a good question: there are special sections on that later. We will go into greater details for each step, of course, but the most difficult part is the GAN: very tricky part of successfully training a GAN is getting the right set of hyperparameters.

For that reason we will use Bayesian optimisation along with Gaussian processes and Reinforcement learning RL for deciding when and how to change the GAN's hyperparameters the exploration vs. In creating the reinforcement learning we will use the most recent advancements in the field, such as Rainbow and PPO.

We will use a lot of different types of input data. Along with the stock's historical trading data and technical indicators, we will use the newest advancements in NLP using 'Bidirectional Embedding Representations from Transformers', BERT, sort of a transfer learning for NLP to create sentiment analysis as a source for fundamental analysisFourier transforms for extracting overall trend directions, Stacked autoencoders for identifying other high-level features, Eigen portfolios for finding correlated assets, autoregressive integrated moving average ARIMA for the stock function approximation, and many more, in order to capture as much information, patterns, dependencies, etc, as possible about the stock.

As we all know, the more data the merrier. Predicting stock price movements is an extremely complex task, so the more we know about the stock from different perspectives the higher our changes are. The purpose is rather to show how we can use different techniques and algorithms for the purpose of accurately predicting stock price movements, and to also give rationale behind the reason and usefulness of using each technique at each step. Accurately predicting the stock markets is a complex task as there are millions of events and pre-conditions for a particilar stock to move in a particular direction.

bidirectional lstm stock price

So we need to be able to capture as many of these pre-conditions as possible. And, please, do read the Disclaimer at the bottom. For the purpose, we will use daily closing price from January 1st, to December 31st, seven years for training purposes and two years for validation purposes. We will use the terms 'Goldman Sachs' and 'GS' interchangeably. Before we continue, I'd like to thank my friends Nuwan and Thomas without whose ideas and support I wouldn't have been able to create this work.

We need to understand what affects whether GS's stock price will move up or down. It is what people as a whole think. Hence, we need to incorporate as much information depicting the stock from different aspects and angles as possible. Then we will compare the predicted results with a test hold-out data. Each type of data we will refer to it as feature is explained in greater detail in later sections, but, as a high level overview, the features we will use are:.

As a final step of our data preparation, we will also create Eigen portfolios using Principal Component Analysis PCA in order to reduce the dimensionality of the features created from the autoencoders. Note : The purpose of this section 3.

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The Data is to show the data preprocessing and to give rationale for using different sources of data, hence I will only use a subset of the full data that is used for training. Let's visualize the stock for the last nine years. The dashed vertical line represents the separation between training and test data. So what other assets would affect GS's stock movements?

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Good understanding of the company, its lines of businesses, competitive landscape, dependencies, suppliers and client type, etc is very important for picking the right set of correlated assets:. We already covered what are technical indicators and why we use them so let's jump straight to the code. We will create technical indicators only for GS.

We have in total 12 technical indicators. For fundamental analysis we will perform sentiment analysis on all daily news about GS.Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Althelaya and El-Sayed M. AlthelayaEl-Sayed M. Recently, there has been a rapidly growing interest in deep learning research and their applications to real-world problems.

The evaluations are conducted using a publicly available dataset for stock market closing prices.

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Predicting Stock Price with LSTM

A deep learning framework for financial time series using stacked autoencoders and long-short term memory Wei Ning BaoJ.

Bidirectional recurrent neural networks Mike SchusterKuldip K. Signal Process. Related Papers. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License.In this tutorial, you will see how you can use a time-series model known as Long Short-Term Memory. LSTM models are powerful, especially for retaining a long-term memory, by design, as you will see later.

bidirectional lstm stock price

You'll tackle the following topics in this tutorial:. If you're not familiar with deep learning or neural networks, you should take a look at our Deep Learning in Python course. It covers the basics, as well as how to build a neural network on your own in Keras. This is a different package than TensorFlow, which will be used in this tutorial, but the idea is the same.

You would like to model stock prices correctly, so as a stock buyer you can reasonably decide when to buy stocks and when to sell them to make a profit. This is where time series modelling comes in. You need good machine learning models that can look at the history of a sequence of data and correctly predict what the future elements of the sequence are going to be. Warning : Stock market prices are highly unpredictable and volatile.

This means that there are no consistent patterns in the data that allow you to model stock prices over time near-perfectly. Don't take it from me, take it from Princeton University economist Burton Malkiel, who argues in his book, "A Random Walk Down Wall Street," that if the market is truly efficient and a share price reflects all factors immediately as soon as they're made public, a blindfolded monkey throwing darts at a newspaper stock listing should do as well as any investment professional.

However, let's not go all the way believing that this is just a stochastic or random process and that there is no hope for machine learning. Let's see if you can at least model the data, so that the predictions you make correlate with the actual behavior of the data.

In other words, you don't need the exact stock values of the future, but the stock price movements that is, if it is going to rise of fall in the near future.

Alpha Vantage. Before you start, however, you will first need an API key, which you can obtain for free here. Use the data from this page. You will need to copy the Stocks folder in the zip file to your project home folder.

You will first load in the data from Alpha Vantage. Since you're going to make use of the American Airlines Stock market prices to make your predictions, you set the ticker to "AAL". You'll use the ticker variable that you defined beforehand to help name this file. However, if the data is already there, you'll just load it from the CSV.

Data found on Kaggle is a collection of csv files and you don't have to do any preprocessing, so you can directly load the data into a Pandas DataFrame. Here you will print the data you collected in to the DataFrame. You should also make sure that the data is sorted by date, because the order of the data is crucial in time series modelling.

Now let's see what sort of data you have. You want data with various patterns occurring over time. This graph already says a lot of things. The specific reason I picked this company over others is that this graph is bursting with different behaviors of stock prices over time. This will make the learning more robust as well as give you a change to test how good the predictions are for a variety of situations.

Another thing to notice is that the values close to are much higher and fluctuate more than the values close to the s. Therefore you need to make sure that the data behaves in similar value ranges throughout the time frame.

You will take care of this during the data normalization phase. You will use the mid price calculated by taking the average of the highest and lowest recorded prices on a day. Now you can split the training data and test data. The training data will be the first 11, data points of the time series and rest will be test data. Now you need to define a scaler to normalize the data. MinMaxScalar scales all the data to be in the region of 0 and 1.

Due to the observation you made earlier, that is, different time periods of data have different value ranges, you normalize the data by splitting the full series into windows.For profit maximization, the model-based stock price prediction can give valuable guidance to the investors.

bidirectional lstm stock price

However, due to the existence of the high noise in financial data, it is inevitable that the deep neural networks trained by the original data fail to accurately predict the stock price. To address the problem, the wavelet threshold-denoising method, which has been widely applied in signal denoising, is adopted to preprocess the training data. In this method, a novel threshold-denoising function is presented to reduce the degree of distortion in signal reconstruction.

The experimental results clearly showed that the proposed MOCWT outperforms the traditional methods in the term of prediction accuracy. Stock price prediction is a typical problem based on time series forecasting, and various stock forecasting methods emerge in an endless stream. Stock price prediction means to predict the stock price after a certain time, so as to help investors realize the maximum benefit.

The methods proposed in the literatures can be roughly divided into two categories containing traditional mathematical methods and economic methods.

Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)

Many previous works are based on traditional statistical methods [ 1 ]. Typically, Kalman filter and autoregressive model are very classic statistical methods, which are often used for financial sequence prediction. With the development of artificial intelligence, the deep learning methods are increasingly applied to the field of stock price prediction [ 2 ].

The deep learning method has a very superior performance compared with the traditional statistical method. One of the main reasons is that with the direct analysis, deep learning can map the original data to a nonlinear model thereby giving a better fitting effect through the multilayer neural network. In addition, deep learning has the advantage of self-selection in the application of the financial field. Most financial data is highly noisy and unstable. With deep learning, various events that have a significant impact on finance can be expressed by knowledge maps.

And then features are selected automatically through deep networks to adjust parameters and weights. The results obtained in this way may be more accurate and objective. Recurrent neural networks [ 3 ] RNN have been widely used in the field of natural language processing [ 4 ] and have achieved great success.

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With the Advantages of neural networks, it is possible to grasp the public sentiment more accurately. It can save long-term memory more effectively.

In addition, due to the huge depth of the neural network in RNN, the gradient is calculated from the higher power of the matrix, sometimes accompanied by the problem of gradient explosion or gradient disappearance. In this paper, the wavelet denoising method is introduced into data preprocessing. The data with wavelet preprocessed were used as the training data. Our main contributions are as follows. First, we have improved the traditional wavelet denoising method and its performance is better than the traditional wavelet denoising method.

Compared with the traditional wavelet method and the improved wavelet method, MOCWT has the best performance. The remainder of this paper is organized as follows. Some related works are reviewed in Section 2. In Section 3the LSTM model is utilized to handle the stock price forecasting task and a new method named multioptimal combination wavelet transform MOCWT is proposed for the aim of data denoising.

Finally, the conclusion is given in Section 5. The issue of stock forecasting has been widely concerned by researchers. Various stock forecasting methods emerge in an endless stream. For example, some researchers use a news-oriented approach to predict stock trends. Ziniu Hu et al. Xi Zhang et al.Use the model to predict the future Bitcoin price. Complete source code in Google Colaboratory Notebook.

You can use the model however you want, but you carry the risk for your actions. You might be asking yourself something along the lines:. Can I still get rich with cryptocurrency? Of course, the answer is fairly nuanced.

bidirectional lstm stock price

Or you might be having money problems? Here is one possible solution:. Here is the plan:. Our dataset comes from Yahoo! Finance and covers all available at the time of this writing data on Bitcoin-USD price. Note that we sort the data by Date just in case. Of course, Bitcoin made some people really rich and for some went really poor. The question remains though, will it happen again? Shall we? Our dataset is somewhat different from our previous examples.

The data is sorted by time and recorded at equal intervals 1 day. Such a sequence of data is called Time Series. Temporal datasets are quite common in practice. You might be interested in a plethora of properties regarding your Time Series — stationarityseasonality and autocorrelation are some of the most well known.I will describe the following steps: dataset creation, RNN training and prediction. The dataset can be downloaded from Kaggle. Data found on Kaggle is a collection of CSV files.

You can directly load the data into a Pandas DataFrame. Stock prices come in several different flavours. They are, Open, Close, High and Low. We will give it a sequence of stock prices and ask it to predict the next day price using GRU cells.

We put our sequence of stock prices on the inputs. It will produce some kind of number on the output. To teach it we force a sequence on the outputs which is the same sequence shifted by one number. This is what we will be teaching.

Using the latest advancements in AI to predict stock market movements

The dataset was split into training and test data. The data was not shuffled but sequentially sliced. You provide a function that returns inputs and labels. The inputs are a dictionary of all your inputs and the labels is a tensor. It starts with a GRU cell.

This defines all the weights and biases. The only parameter you need to specify is this internal size of the vectors in this cell. If you want to deep neural network.

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In TensorFlow you can unroll cell using the dynamic RNN function you give it a stacked cell that you just produced. How many times do you unroll?


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