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)
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)
Penafian
Maklumat dan penerbitan adalah tidak dimaksudkan untuk menjadi, dan tidak membentuk, nasihat untuk kewangan, pelaburan, perdagangan dan jenis-jenis lain atau cadangan yang dibekalkan atau disahkan oleh TradingView. Baca dengan lebih lanjut di Terma Penggunaan.
Penafian
Maklumat dan penerbitan adalah tidak dimaksudkan untuk menjadi, dan tidak membentuk, nasihat untuk kewangan, pelaburan, perdagangan dan jenis-jenis lain atau cadangan yang dibekalkan atau disahkan oleh TradingView. Baca dengan lebih lanjut di Terma Penggunaan.