This Project demonstrates a market forecasting analysis using various technical indicators. The analysis is performed on cryptocurrency and forex market data using Python programming language.
Before running the notebook, ensure you have the following prerequisites:
The notebook performs the following steps: Data Loading: Import necessary libraries and load three CSV datasets related to currency and cryptocurrency markets.
Perform basic data exploration by displaying the first few rows of each dataset, checking the length, and displaying dataset information.
Calculate price changes for the currency dataset. Calculate the label column based on the direction of price changes. Calculate the typical price and moving average (MA) for the currency dataset. Calculate the mean deviation (MD) for the MA indicator. Calculate the Commodity Channel Index (CCI) indicator. Calculate the Relative Strength Index (RSI) indicator. Calculate the tether-to-Toman exchange rate relative to the dollar.
Create a new DataFrame containing selected features for analysis.
Check for missing values in the new DataFrame.
Compute the correlation matrix and sort the correlations with the label.
Split the dataset into training and testing sets. Scale the features using StandardScaler. Build an Extra Tree Classifier model. Evaluate the model's performance using the F1 score on the testing set.
Apply the trained model on the entire dataset to generate predictions.