Reply To: Predicting upcoming stock prices using Python. Can you show me an example?

[Satya Mukherjee]

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Of course it is possible. Now the accuracy is somewhat debatable, but for the sake of it, let me demonstrate using yFinance data.

To plot the predicted stock price for Apple using yfinance data for the next 7 days, we will need to use a machine learning model that can make predictions based on historical data. One popular machine learning model used for predicting stock prices is the Long Short-Term Memory (LSTM) model.

To use the LSTM model, we will first need to retrieve historical data using the yfinance package. We will then use this data to train the LSTM model and use the trained model to make predictions for the next 7 days.

Here is some sample code to retrieve the historical data and train the LSTM model:

import yfinance as yf
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
from matplotlib import pyplot as plt
apple = yf.download('AAPL', period='1y')
data = apple.filter(['Close'])
data.index = pd.to_datetime(data.index).tz_localize('UTC')
dataset = data.values
training_data_len = int(len(dataset) * 0.8)
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(dataset)
x_train = []
y_train = []
for i in range(60, training_data_len):
    x_train.append(scaled_data[i - 60:i, 0])
    y_train.append(scaled_data[i, 0])
x_train, y_train = np.array(x_train), np.array(y_train)
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(x_train.shape[1], 1)))
model.add(LSTM(50, return_sequences=False))
model.add(Dense(25))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(x_train, y_train, batch_size=1, epochs=1)
test_data = scaled_data[training_data_len - 60:, :]
x_test = []
y_test = dataset[training_data_len:, :]
for i in range(60, len(test_data)):
    x_test.append(test_data[i - 60:i, 0])
x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions)
preds_df = pd.DataFrame(predictions, index=data.index[training_data_len + 60:], columns=['Prediction'])
actuals_df = pd.DataFrame(y_test, index=data.index[training_data_len:], columns=['Actual'])
merged_df = pd.merge(actuals_df, preds_df, how='left', left_index=True, right_index=True)
plt.figure(figsize=(16, 8))
plt.title('Predicted vs Actual Stock Price for Apple')
plt.xlabel('Date')
plt.ylabel('Price (USD)')
plt.plot(merged_df['Actual'], label='Actual')
plt.plot(merged_df['Prediction'], label='Prediction')
plt.legend()
plt.show()

• This reply was modified 5 months, 2 weeks ago by  Satya Mukherjee.
• This reply was modified 5 months, 2 weeks ago by  Satya Mukherjee.
• This reply was modified 5 months, 2 weeks ago by  Satya Mukherjee.
• This reply was modified 5 months, 2 weeks ago by  Satya Mukherjee.
• This reply was modified 5 months, 2 weeks ago by  Satya Mukherjee.
• This reply was modified 5 months, 2 weeks ago by  Satya Mukherjee.
• This reply was modified 5 months, 2 weeks ago by  Satya Mukherjee.
• This reply was modified 5 months, 2 weeks ago by  Satya Mukherjee.
• This reply was modified 5 months, 2 weeks ago by  Satya Mukherjee.