Program for forecast prices open and close stocks of brazillian

📚 ref.:

• https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21
  
• https://keras.io/api/layers/recurrent_layers/lstm/
  

📊 Why do forecast price of opening/closing stocks?

Predicting opening/closing stock prices is an important task for investors and financial analysts, as it can provide valuable insights into market trends and help make informed investment decisions. In addition, forecasting stock opening/closing prices can also be useful for companies, as it can help them understand how their stocks are affected by economic and political events, and thus adjust their business strategies.

Forecasting stock opening/closing prices can also help identify investment opportunities and avoid risks. For example, if a forecasting model predicts that the price of a stock will rise, this could be an opportunity for an investor to buy the stock before the price increases. Conversely, if a forecasting model predicts that a stock's price will fall, this may be a reason for an investor to sell their stock before the price falls.

In summary, forecasting stock opening/closing prices is a valuable tool for investors and financial analysts as it can help make informed investment decisions, identify investment opportunities and avoid risks. Furthermore, it can be useful for companies as it can help them understand how their actions are affected by economic and political events and thus adjust their business strategies.

🗿 LSTM (Long Short-Term Memory)

The LSTM (Long Short-Term Memory) network is a recurrent neural network that is designed to handle time series of data. One of the key features of the LSTM network is its ability to maintain long-term information. This is achieved through the use of memory cells, which are capable of storing information for a long period of time. In addition, LSTM networks also have input, output and forget gates, which allow controlling the flow of information in and out of the memory cell.

LSTM is a Deep Learning technique, it is a variation of RNN (Recurrent Neural Network) that uses memory cells to remember important information over time, which helps to model time series and other data sequences. This allows the network to understand the temporal context and make accurate predictions, even when there is missing data or input noise.

In summary, the LSTM network is an advanced Machine Learning technique that is designed to deal with time series of data and has been widely used for tasks such as time series prediction, natural language processing and speech recognition. Its ability to retain long-term information is one of its key features, and this makes it ideal for stock price forecasting and other similar applications.

🔰 Steps of LSTM

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📌 Forget gate

📌 Input Gate

📌 Cell State

📌 Output Gate

🥳 How make forecast in program

1. Use a EC2 machine in AWS with allow for write on S3
2. Criete a bucket on S3 and replace on file app-programmatic.py your name in my-data-stocks
3. Install packges python in file requirements.txt
4. Open the file app-programmatic.py and configure variables days and list_stocks_names
   • list_stocks_names: is List of stocks for forecast
   • days: is how many days you forecast after