Untitled

import pandas as pd
from sklearn.linear_model import LinearRegression

# Define your currency pair (e.g., 'EUR/USD')
currency_pair = "EUR_USD"

# Fetch historical data
data = pd.read_csv(f"data/{currency_pair}_1d.csv")  # Assuming daily data is available

# Split the data into train and test sets
train_data = data[:int(0.8 * len(data))]
test_data = data[int(0.8 * len(data)) + 1:]

# Define feature columns (e.g., open, high, low, close) and target column (e.g., volume)
feature_cols = ["Open", "High", "Low", "Close"]
target_col = "Volume"

# Create a linear regression model
model = LinearRegression()

# Train the model on the train data
model.fit(train_data[feature_cols], train_data[target_col])

# Generate predictions for the test data
predictions = model.predict(test_data[feature_cols])

# Evaluate the model performance (e.g., mean absolute error)
from sklearn.metrics import mean_absolute_error

mae = mean_absolute_error(test_data[target_col], predictions)

print(f"Mean Absolute Error: {mae:.2f}")

# Use the trained model to predict future values and make trades based on your strategy