Ai trading bot

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Load historical data for gold and USD
gold_data = pd.read_csv('gold_data.csv')
usd_data = pd.read_csv('usd_data.csv')

# Merge the datasets based on date
merged_data = pd.merge(gold_data, usd_data, on='Date')

# Define features and target
X = merged_data[['Gold_Price', 'USD_Price']]
y = merged_data['Action'] # Action can be 'Buy', 'Sell', or 'Hold'

# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train a random forest classifier
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Predict on the test set
predictions = model.predict(X_test)

# Evaluate the accuracy
accuracy = accuracy_score(y_test, predictions)
print("Accuracy:", accuracy)