Stock-Prediction-App: A Stock Price Prediction System Based on LSTM

Stock-Prediction-App is a stock market prediction system based on LSTM neural networks. It predicts future trends by analyzing historical stock price data and provides a Streamlit visualization dashboard, serving as a practical reference for quantitative trading and fintech enthusiasts. This project covers the entire workflow of data acquisition, preprocessing, model training, evaluation, and deployment. However, it should be noted that this is primarily a learning project and is not recommended for direct use in real trading for profit.

Stock market prediction is a challenge in the fields of financial engineering and artificial intelligence, as it is difficult to predict accurately due to market complexity, randomness, and nonlinearity. Traditional methods are divided into two categories: fundamental analysis (assessing intrinsic value based on financial status, industry prospects, and macroeconomic factors) and technical analysis (looking for predictive patterns from historical price and trading volume data). In recent years, with the rise of machine learning and deep learning technologies, more and more researchers have tried AI methods to predict stock prices.

Long Short-Term Memory (LSTM) networks were proposed by Hochreiter and Schmidhuber in 1997 to solve the gradient vanishing problem of traditional RNNs and can learn long-term dependencies. Stock prices are typical time-series data with time dependence, nonlinear characteristics, and multi-scale patterns. The forget gate, input gate, and output gate of LSTM can selectively remember or forget historical information, making it suitable for capturing such dependencies.

Stock-Prediction-App obtains historical data from Yahoo Finance API, Alpha Vantage, or domestic data sources (such as tushare, akshare). The original features include opening price, highest price, lowest price, closing price, trading volume, and adjusted closing price. It also calculates technical indicators (moving average MA, relative strength index RSI, MACD, Bollinger Bands, KDJ, etc.) as additional features. Preprocessing includes normalization, sequence construction, and training/test set splitting.

The model usually includes an input layer (receiving time steps such as 60 days of data), multiple stacked LSTM layers, a Dropout regularization layer, and a fully connected layer (outputting predicted values). For training, mean squared error (MSE) or mean absolute error (MAE) is used as the loss function, with the Adam optimizer. Strategies like learning rate scheduling, early stopping, and model checkpointing are adopted to prevent overfitting.

The project uses Streamlit to build the interface, providing stock selection, data visualization, model training interface, prediction result display, and interactive functions. Users can input stock codes, select time ranges, adjust hyperparameters, and view training progress and prediction results.

Stock price prediction faces multiple challenges: 1. The efficient market hypothesis holds that prices reflect all information, making it difficult to obtain excess returns; 2. Stock price time series are non-stationary, requiring regular retraining, and black swan events (crises, pandemics, etc.) are hard to predict; 3. Data snooping and overfitting are common issues; 4. Actual trading needs to consider costs, slippage, etc., and short-term predictions are dominated by noise.

Improvement directions: Integrate fundamental, macroeconomic, and alternative data (news sentiment, social media), and optimize the architecture using attention mechanisms and hybrid models (CNN+LSTM, LSTM+Transformer). Application suggestions: Learners can master time-series processing, LSTM, and the full ML workflow through this project; researchers can explore new features, architectures, and text fusion; practitioners should first test with a simulation platform for 6-12 months, start with small funds, continuously update the model, and monitor performance.