mehrabkalantary / lstm-cryptocurrency

Cryptocurrency using multi-state LSTM

Cryptocurrency using Multi-State LSTM

In this notebook, we analyze cryptocurrency datasets and define a problem to solve using multi-state LSTM

Contents

Data Understanding

There are four currencies and each currency has 6 features such as time, low, high, open, close and volume.

Currencies: BCH-USD, BTC-USD, ETH-USD, LTC-USD

Data Cleaning

• Joining 4 datasets
• Handling missing values

Here we define a new dataframe and join 4 datasets into 1 dataset on time feature, then handle created NaN values.

Problem Definition

We have 2 columns for each currency, close and volume, and we create a new column named "future" to store the LTC-USD_close value after 3 days.

Problem: Is it beneficial to buy LTC currency or not?

Data Preprocessing

• Train-test split

  • We use the last 10 percent of data for testing and others for training

• Data to sequence

  • We divide the data into sequences with 30 time steps

• Under-sampling

  • The data is imbalanced, so we use this method to handle it

• Percentage change

• Min-max scaling

Modeling

Evaluation

The best val_accuracy is around 57%