The world of stock trading is both exhilarating and complex. Investors are constantly seeking new ways to predict stock prices in order to maximize their profits. One innovative approach involves using advanced machine learning models to forecast stock prices with greater accuracy. In this blog post, we will explore the fascinating world of predicting stock prices using an Encoder-Decoder model.
The project aims to use cutting-edge technology to analyze historical stock data and make predictions about future stock prices. By leveraging an EncoderRNN and a DecoderRNN, the model can learn from past trends and patterns to make informed forecasts. This sophisticated model is then put to the test on a dataset to evaluate its performance.
To measure the model’s accuracy and profitability, various evaluation metrics like mean squared error (MSE) loss, profitability performance (PP), and cumulative returns (CDR) are calculated. Additionally, we delve into the correlation between predicted and actual closing prices relative to the opening prices, providing valuable insights into the model’s effectiveness.
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import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
To analyze and visualize the stock data, we utilize key libraries such as NumPy for handling arrays, Pandas for data manipulation, and MinMaxScaler for data preprocessing. By importing these essential modules, we can process the data efficiently and create insightful visualizations to aid our predictions.