# Importing Streamlit for building the web-based interactive application framework
import streamlit as st
# Function to display the InfluxDB Database section
def display_project_database():
st.markdown(
"""
Project Database Overview
We are using InfluxDB Database for efficient data storage and management.
InfluxDB Database
InfluxDB is a powerful time-series database optimized for storing and querying both historical and real-time data,
particularly in high-volume environments. It offers exceptional performance for managing time-stamped data, making it
an ideal choice for our project.
InfluxDB Architecture
The InfluxDB architecture utilizes a bucket system, which is pivotal for organizing data. In our project, we have a
primary bucket named Stock Price. Within this bucket, measurements help categorize and store data efficiently.
The first measurement is stock_price, which includes data points such as open, high, low, close, RSI (Relative
Strength Index), volume, and moving average, all indexed by time.
Another key measurement is model_prediction, containing values such as predicted high, low, open, volume,
predicted close, predicted moving average, and predicted RSI.
Both measurements include a Ticker column that holds the company ticker symbol, covering a total of eight
companies: AAPL, AMZN, MSFT, NVDA, NFLX, META, TCS, and GOOG.
Initially, data is stored in InfluxDB, then queried and visualized using Grafana. The dashboard in Grafana is built on
top of this architecture, allowing dynamic data visualization. The diagram below depicts the general architecture of
InfluxDB.
Database Overview
This section displays database snapshots from the project, organized by categories.
1. Numerical Analysis
This section displays numerical data from the stock market, including graphs for open, high, low, and close prices. It also features volume bar plots, RSI, and moving averages.
2. Model Prediction
This section displays the model predictions, comparing predicted and actual values for stock prices, RSI, and moving averages.
3. Textual Analysis
This section visualizes sentiment analysis from news headlines, including positive, negative, and neutral sentiment scores.
4. Hybrid Model
The hybrid model combines numerical and textual data to provide a more comprehensive analysis of stock prices.
""",
unsafe_allow_html=True,
)
