import pandas as pd
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import gdown
from sklearn.preprocessing import MinMaxScaler

file_id = "1XXkVwG0OUQqNxWVSdz3q6IMzAnxf2dnv" # Timeframe = 1 วัน
# https://drive.google.com/file/d/1XXkVwG0OUQqNxWVSdz3q6IMzAnxf2dnv/view?usp=drive_link
# ข้อมูลได้มาจากใน Kaggle ชื่อ "XAU/USD Gold Price Historical Data (2004-2025)" ของ Novandra Anugrah

# ช่วงเวลาของข้อมูล ระหว่าง 11 มิ.ย. 2547 จนถึง 20 กันยายน 2567

csv_path = "gold_prices.csv"

gdown.download(f"https://drive.google.com/uc?id={file_id}", csv_path, quiet=False)

df = pd.read_csv(csv_path)
df['Datetime'] = pd.to_datetime(df['Date'] + ' ' + df['Time'])
df.set_index('Datetime', inplace=True)
df = df[['High', 'Low']]

def create_dataset_with_scaling(data, time_step=5):
    X, Y = [], []
    for i in range(len(data) - time_step):
        window = data[i:i + time_step + 1]
        scaler = MinMaxScaler(feature_range=(0, 1))
        window_scaled = scaler.fit_transform(window)
        X.append(window_scaled[:-1])
        Y.append(window_scaled[-1])
    return np.array(X), np.array(Y)

time_step = 2 # เวลาก่อนหน้าที่นำมาใช้พยากรณ์

X, Y = create_dataset_with_scaling(df.values, time_step)
X = np.reshape(X, (X.shape[0], X.shape[1], X.shape[2]))

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, LSTM, Dense, Dropout

train_size = int(len(X) * 0.1) # ฝึก 10% ทดสอบ 90%
X_train, X_test = X[:train_size], X[train_size:]
Y_train, Y_test = Y[:train_size], Y[train_size:]

model = Sequential([
    Input(shape=(time_step, X.shape[2])),
    LSTM(16, return_sequences=True),
    LSTM(16, return_sequences=False),
    Dense(2)
])

model.compile(optimizer='adam', loss='mean_squared_error')
model.summary()

model.fit(X_train, Y_train, batch_size=1, epochs=3, validation_data=(X_test, Y_test))

from sklearn.metrics import r2_score

Y_pred = model.predict(X_test, verbose=0)

r2 = r2_score(Y_test, Y_pred)
# ค่า R-Square เป็นค่าที่อยู่ระหว่าง -1 กับ 1
# R-Square > 0 แปลว่า โมเดลพยากรณ์ได้ดีกว่าค่าเฉลี่ย
# R-Square = 0 แปลว่า โมเดลพยากรณ์ได้ไม่ต่างกับค่าเฉลี่ย
# R-Square < 0 แปลว่า โมเดลพยากรณ์ได้แย่กว่าค่าเฉลี่ย

print(f"✅ R² = {r2:.4f}")

def predict_next_day(model, data):
    last_days = data[-time_step:]
    scaler = MinMaxScaler(feature_range=(0, 1))
    last_days_scaled = scaler.fit_transform(last_days)
    last_days_scaled = last_days_scaled.reshape(1, time_step, 2)
    predicted_scaled = model.predict(last_days_scaled)
    predicted_actual = scaler.inverse_transform([predicted_scaled[0]])[0]
    return predicted_actual

predictions_actual = []
actual_values = []
test_range = 200
start_index = len(X_test) - test_range

for i in range(start_index, len(X_test)):
    scaler = MinMaxScaler(feature_range=(0, 1))
    absolute_index = train_size + i
    test_window = df.values[absolute_index: absolute_index + time_step + 1]
    if len(test_window) < time_step + 1:
        continue
    scaler.fit(test_window)
    pred_scaled = model.predict(X_test[i].reshape(1, time_step, 2), verbose=0)[/i]
[i]    pred_actual = scaler.inverse_transform([pred_scaled[0]])[0][/i]
[i]    predictions_actual.append(pred_actual)[/i]
[i]    actual_values.append(test_window[-1])[/i]

[i]predictions_actual = np.array(predictions_actual)[/i]

[i]actual_values = np.array(actual_values)[/i]

[i]plt.figure(figsize=(14, 7))[/i]

[i]x = np.arange(len(actual_values))[/i]

[i]plt.fill_between(x, actual_values[:, 0], actual_values[:, 1], color='skyblue', alpha=0.4, label="Actual Range")[/i]

[i]plt.fill_between(x, predictions_actual[:, 0], predictions_actual[:, 1], color='salmon', alpha=0.4, label="Predicted Range")[/i]

[i]plt.plot(x, actual_values[:, 0], color='blue', label="Actual High", linewidth=1)[/i]
[i]plt.plot(x, actual_values[:, 1], color='blue', label="Actual Low", linewidth=1, linestyle='dotted')[/i]
[i]plt.plot(x, predictions_actual[:, 0], color='red', label="Predicted High", linewidth=1)[/i]
[i]plt.plot(x, predictions_actual[:, 1], color='red', label="Predicted Low", linewidth=1, linestyle='dotted')[/i]
[i]plt.title(f"Gold Price Range Prediction (Last {test_range} Days)")[/i]
[i]plt.xlabel("Day Index")[/i]
[i]plt.ylabel("Price")[/i]
[i]plt.legend()[/i]
[i]plt.grid(True)[/i]
[i]plt.tight_layout()[/i]
[i]plt.show()