Predicting the Future: A Simple Python Program for Stock Price Prediction
Having the ability to predict the future can give a significant advantage to businesses and investors. One of the most popular ways to predict the future is by analyzing historical stock prices and using them to make predictions about future prices. In this article, we’ll show you how to use Python to predict future stock prices for a given stock symbol.
The process of predicting future stock prices can be broken down into two main steps: acquiring historical stock prices and using them to train a prediction model. The first step is to acquire historical stock prices for a given stock symbol. We will use the pandas_datareader library to get historical stock prices from yahoo finance.
The second step is to use the historical stock prices to train a prediction model. We will use the prophet library, which is a popular open-source library for time series forecasting. The prophet library makes it easy to create and fit a model to historical data, and it also has built-in support for handling missing data and trend changes.
Here’s an example of a simple Python program that uses the pandas_datareader library to get historical stock prices for a given stock symbol and then uses the prophet library to predict future prices:
import pandas_datareader as pdr
from fbprophet import Prophet
def predict_stock_price(symbol):
# Get historical stock prices
df = pdr.get_data_yahoo(symbol)
# Rename columns for prophet library
df = df.rename(columns={'Close': 'y', 'Date': 'ds'})
# Create and fit prophet model
model = Prophet()
model.fit(df)
# Create future dataframe
future = model.make_future_dataframe(periods=365)
# Predict future prices
forecast = model.predict(future)
# Plot the predictions
model.plot(forecast)
# Example usage
predict_stock_price('AAPL')This program uses the pandas_datareader library to get historical stock prices for a given stock symbol using the get_data_yahoo() function. Then, it renames the columns to match the format required by the prophet library. The prophet library is used to create and fit a model to the historical data, and then it creates a future dataframe with 365 periods. Finally, it predicts future prices and plots the predictions.
It’s worth noting that this is a basic example, and you will probably need to adjust the code to handle any missing data or data that may not be in the format that you expect. Also, you should be aware that stock prices prediction is a complex task and it’s not guaranteed to be accurate. There are a lot of factors that influence stock prices such as economic situation, events, etc.
In conclusion, predicting future stock prices is a challenging task, but with the help of Python and its libraries, it can be made much easier. By using the pandas_datareader and prophet libraries, businesses and investors can quickly and easily analyze historical stock prices and make predictions about future prices. With this knowledge, businesses and investors can make better decisions and potentially increase their profits.
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