Predicting Equity Prices for multiple asset classes. This type of model works for different types of Equities once data is fetched in the form required. Pricing equities has been a tough target for most people who try stock markets causing them enormous amount of pilling losses. Here we have used a complex Neural Network to predict equity prices based on previous historical data.
LSTM stands for Long Short Term Memory. Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) that is specifically designed to handle sequential data, such as time series, speech, and text. LSTM networks are capable of learning long-term dependencies in sequential data, which makes them well suited for tasks such as language translation, speech recognition, and time series forecasting.
A traditional RNN has a single hidden state that is passed through time, which can make it difficult for the network to learn long-term dependencies. LSTMs address this problem by introducing a memory cell, which is a container that can hold information for an extended period of time. The memory cell is controlled by three gates: the input gate, the forget gate, and the output gate. These gates decide what information to add to, remove from, and output from the memory cell.
- Step 1- Importing Python Libraries
- Step 2 - Dataset Reading
- Step 3 - Pre Processing the data and getting the Overall Structure of the data to start with data modelling
- Step 4 - Plotting the chart against the historical prices.
- Step 5 - Plot the Closing prices in different charts - Trend || Seasonal || Resid
- Step 6 - Splitting the data into the ratio of 70% Training Dataset and 30%Test Dataset
- Step 7 - Windowing Dataset
- Step 8 - Implementing LSTM will start from here || The above steps can be considered as Pre-Processing and Splitting of the Dataset
- Step 9 - Import Keras and Tensorflow libraries for LSTM Network
- Step 10 - Fitting data to the model and Selecting the number of ePochs required
- Step 11 - Get a set of values and then evaluate the model and calculate Root Mean Square Error and R-Squared Score
- Step 12 - Plot the stock price and Predicted values || The difference between the predicted value and stock prices is called the Root Mean Square Error (RMSE)
- Step 13 - Predicting Future Price Update - For making it as realtime as possible, We have predicted 30 day trend
- Step 14 - Plotting Past Prices and Predicted Values for the next 30 days
- Apple
- HDFC Bank
- India VIX
- Nifty FMCG
- Nifty IT
- Nifty 100
- Nifty 50
- Nifty 500
- Nifty Auto
- Nifty Bank
- Nifty Metal
- Nifty Midcap
- Nifty Pharma
- Nifty Smallcap
- Reliance Industries
- Tata Global Products
- Download "Index" Jupyter Notebook file of main
- Fetch equity Database from exchange
- Assign the database in the file [A comment has been mentioned for the same]
- Run the file
- Results are then fetched