Conjexure is a machine learning web app for forecasting the stock prices of certain companies into the future. Stock prediction using machine learning is one of the ho topic. In stock prediction model we basically train a model using reccurent neural networks such as an Long Short Term Memory Model. We use RNNs as they were designed to work with sequence prediction problems. Sequence prediction problems come in many forms and are best described by the types of inputs and outputs supported. LSTM is a type of RNN which is good at handling long term dependecies which is benifitable to us in predicting stock prices based on last 30 or 60 days for further 7-14 days.
Conjexure utilizes the stock prices for Alphabet Inc. (GOOG) and Apple Inc (AAPL) for training. The reason behind this is as there closing stock price graph over the time period is quite smooth if seen overall and avoids any sudden jumps or falls and follows a upward trend. We can forecast into a user-specified future window of 1-5 weeks. We leave this upto user to select the period of time he/she wants to predict the stock values for. It also displays the raw data for the user to inspect. For the stock price, the closing price of a stock on any given day is chosen.
The technical stack we have used is as follows:
- Tensorflow(Keras) Building Model
- Pandas data reader Collecting stock data of the companies
- Matplotlib Visualization and Plotting graphs
- Streamlit Model Deployment
For deployment of Conjexure as an interactive web app, the Streamlit framework was chosen for its robustness and ease of scripting. You may go through the documentation for Streamlit here.
Let's visualize the stock prices value for Alphabet Inc. (GOOG)
As you can see from the above graph that it follows a upward trend overall. These are the stock values of Alphabet Inc. from 1st of April 2003 to 15th July 2023.
To play around with Conjexure yourself, head on to our web app at https://conjexure.herokuapp.com.
To run Conjexure on your local machine, first ensure that the following relevant dependencies are installed.
- To install the required dependencies and modules, please execute the provided pip command.
pip install streamlit yfinance numpy pandas tensorflow keras scikit-learn matplotlib pandas_datareader- Clone this repository either through GitHub Desktop or your command line interface, based on your personal preference.
Executing Streamlit scripts is remarkably straightforward. Simply navigate to your cloned directory and input the following command into your terminal.
streamlit run stockpricepred.pyConjexure uses the Tensorflow and Keras libraries to build 5 stacked LSTM models complemented by a convolutional as well as a lambda layer.
There are 5 models to forecast into 5 different future windows (from 1-5 weeks) depending on user specification. Each model utilizes a proportionately larger training set to compensate for the increasing prediction window. For example, the model to forecast one week into the future uses the last 30 days of stock price data, the model to forecast two weeks uses the last 60 days of data and so on.
For any stock symbol, up to 5 different price metrics are available, including the opening price, the closing price, the high, the low, etc. Conjexure uses the closing price as the standard stock price. We came to this decision because the closing price accounts for news related to the company and the market's general mood on the day (which can then be predicted by our model).
Model that we have used for deployment have following architecture:
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv1d (Conv1D) (None, None, 128) 768
_________________________________________________________________
lstm (LSTM) (None, None, 128) 131584
_________________________________________________________________
lstm_1 (LSTM) (None, None, 128) 131584
_________________________________________________________________
lstm_2 (LSTM) (None, 128) 131584
_________________________________________________________________
dense (Dense) (None, 64) 8256
_________________________________________________________________
dense_1 (Dense) (None, 64) 4160
_________________________________________________________________
dense_2 (Dense) (None, 7) 455
=================================================================
Total params: 408,391
Trainable params: 408,391
Non-trainable params: 0
The above model is for forecasting 7 days stock values. In the other models just the output shape is altered which is (None, 14) for two weeks, (None, 21) for three weeks and so on.
The following is the validation and training loss os the above model.
From above we can see that our validation loss is noisy this might be due to the fact that we are using stochastic gradient descent which calculate loss based on each sample. But if you look at the y axis values which represent the loss value You will see that out loss value is almost in 10^(-3) which is pretty low loss so even if it's noisy it's quite low so it does not effect our model performance.
Following is the mean squarred error graph:
The above statement regarding the validation loss can be proved by the above mean squarred error plot of validation data.
Our model gives great performace in predicting future validation set values and the same can be seen from the plot below:
We are using past 30 days stock values to predict next 7 days values. For this purpose each of the 7 days value is diretly dependent on the 30 days stock values and not on the values of the day just before them. As we are directly outputting 7 days stock values without considering the stock values of t-1 day for t day.
Below is the forecast of the stock values for 21st July 2020 to 27th July 2020 based on the data of past 98 days.
- Aditya mangla
- Harsh Sharma
- Abhishek Saxena
- Tarushi Pathak
- Aditya Shukla
- Sheel Patel
- Ankita Kokkera
- Aditya Verma
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.