Stock Market Trend Analysis
1. Introduction
Objective: Analyze stock market trends using historical data, visualize
candlestick patterns, and predict future prices using an LSTM model.
Purpose: Enable investors to make informed decisions by leveraging data-driven
insights into market trends and patterns.
2. Project Workflow
1. Problem Definition:
- Identify stock price trends and
predict future movements.
- Key questions:
- What patterns emerge from
historical stock price data?
- How can we predict future stock
prices?
2. Data Collection:
- Source: Alpha Vantage API, Yahoo
Finance, or other financial data providers.
3. Data Preprocessing:
- Clean, organize, and transform stock
data for analysis.
4. Visualization:
- Generate candlestick charts for
trend analysis.
5. Modeling:
- Build and train an LSTM model for
stock price prediction.
6. Evaluation and Insights:
- Evaluate the model's performance and
provide actionable insights.
3. Technical Requirements
- Programming Language: Python
- Libraries/Tools:
- Data Handling: Pandas, NumPy
- Visualization: Matplotlib, Plotly,
Seaborn, mplfinance
- Machine Learning: TensorFlow/Keras
- Data Source: yfinance, Alpha Vantage
API
4. Implementation Steps
Step 1: Setup Environment
Install required libraries:
```
pip install pandas numpy matplotlib seaborn mplfinance tensorflow yfinance
```
Step 2: Collect and Explore Data
Download stock data using yfinance:
```
import yfinance as yf
data = yf.download('AAPL', start='2015-01-01', end='2023-01-01')
print(data.head())
```
Inspect the dataset:
```
print(data.info())
print(data.describe())
```
Step 3: Visualize Candlestick Patterns
Generate candlestick charts:
```
import mplfinance as mpf
mpf.plot(data, type='candle', volume=True, title='AAPL Candlestick Chart',
style='yahoo')
```
Step 4: Preprocess Data for LSTM Model
Normalize data and create sequences:
```
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
data_scaled = scaler.fit_transform(data['Close'].values.reshape(-1, 1))
# Create sequences
import numpy as np
sequence_length = 50
X, y = [], []
for i in range(sequence_length, len(data_scaled)):
X.append(data_scaled[i-sequence_length:i, 0])
y.append(data_scaled[i, 0])
X, y = np.array(X), np.array(y)
X = X.reshape((X.shape[0], X.shape[1], 1))
```
Step 5: Build and Train LSTM Model
Create and train the LSTM model:
```
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)))
model.add(Dropout(0.2))
model.add(LSTM(units=50))
model.add(Dropout(0.2))
model.add(Dense(units=1))
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(X, y, epochs=50, batch_size=32)
```
Step 6: Make Predictions and Evaluate Model
Predict future stock prices:
```
predicted_prices = model.predict(X)
predicted_prices = scaler.inverse_transform(predicted_prices)
```
Evaluate model performance:
```
import matplotlib.pyplot as plt
plt.plot(data['Close'][sequence_length:], label='Actual Prices')
plt.plot(predicted_prices, label='Predicted Prices')
plt.title('Stock Price Prediction')
plt.legend()
plt.show()
```
5. Expected Outcomes
1. Candlestick charts illustrating historical stock price patterns.
2. An LSTM model capable of predicting future stock prices.
3. Insights into stock market trends and actionable recommendations for
investors.
6. Additional Suggestions
- Experiment with different hyperparameters to optimize the LSTM model.
- Use additional technical indicators (e.g., RSI, MACD) for enhanced analysis.
- Develop a dashboard for real-time stock market monitoring and predictions.