Code

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import matplotlib.pyplot as plt

def compute_rsi(data, window=14):
    """ Compute Relative Strength Index (RSI) for a DataFrame. """
    delta = data.diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
    rs = gain / loss
    rsi = 100 - (100 / (1 + rs))
    return rsi

# Load Data
data = pd.read_csv('tesla.csv')

# Feature Engineering
data['SMA_50'] = data['Close'].rolling(window=50).mean()
data['SMA_200'] = data['Close'].rolling(window=200).mean()
data['RSI'] = compute_rsi(data['Close'])

# Lagged Features
data['Close_lag1'] = data['Close'].shift(1)
data['Close_lag2'] = data['Close'].shift(2)

# Drop NA values created by rolling and shifting
data.dropna(inplace=True)

# Features and Target
X = data[['SMA_50', 'SMA_200', 'RSI', 'Close_lag1', 'Close_lag2']]
y = data['Close']

# Train-Test Split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Scaling
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Model Training
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train_scaled, y_train)

# Prediction
y_pred = model.predict(X_test_scaled)

# Evaluation
mae = mean_absolute_error(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
r2 = r2_score(y_test, y_pred)

# Percentage Accuracy
accuracy = 1 - np.mean(np.abs((y_test - y_pred) / y_test))

print(f'MAE: {mae}')
print(f'MSE: {mse}')
print(f'RMSE: {rmse}')
print(f'R-squared: {r2}')
print(f'Accuracy: {accuracy * 100:.2f}%')

# Plot Actual vs Predicted
plt.figure(figsize=(14, 7))
plt.plot(y_test.values, label='Actual')
plt.plot(y_pred, label='Predicted')
plt.legend()
plt.show()