Financial_Forecast

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.

Prerequisites

Before running the notebook, ensure you have the following prerequisites:

Description

The notebook performs the following steps: Data Loading: Import necessary libraries and load three CSV datasets related to currency and cryptocurrency markets.

Exploratory Data Analysis (EDA)

Perform basic data exploration by displaying the first few rows of each dataset, checking the length, and displaying dataset information.

Data Preprocessing

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.

Feature Selection

Create a new DataFrame containing selected features for analysis.

Data Cleaning

Check for missing values in the new DataFrame.

Correlation Analysis

Compute the correlation matrix and sort the correlations with the label.

Model Building

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.

Final Predictions

Apply the trained model on the entire dataset to generate predictions.