📈 Trade Trendz

Interactive Stock Market Analysis & Visualization Dashboard

Trade Trendz is an interactive stock-market analysis application built with Python, Pandas, Streamlit, Plotly, and Seaborn.

Users can upload their own stock-market datasets, preprocess and validate the data, select a custom time period, and explore stock trends through interactive visualizations and basic technical-analysis tools.

🔴 Live Demo: https://tradetrendz.streamlit.app/

🟢 Source Code: https://github.com/INDIAN123456890/Trade_Trendz_Updated

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🎯 Features

• 📂 Upload CSV, XLSX, and XLS stock-market datasets
• 🧹 Automatic data preprocessing and validation
• 🔢 Numerical type conversion
• 🧮 Mean-based missing-value handling
• 🔍 Duplicate and invalid-value detection
• 📊 OHLC data validation
• 📅 Dynamic date-range selection based on the uploaded dataset
• 🗓️ Daily, weekly, and monthly analysis
• 📈 Multiple interactive visualizations
• ⚙️ Customizable chart and analysis settings

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🧹 Data Preprocessing

Before visualization, the uploaded dataset goes through a preprocessing pipeline.

The application handles:

• Completely empty rows
• Duplicate records
• Invalid dates
• Invalid numerical values
• Missing numerical values
• Non-positive stock prices
• Invalid trading volume
• Inconsistent OHLC relationships

Missing numerical values are replaced using the mean of their respective column.

The application also provides a preprocessing report so users can see what changes were made to their data.

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📊 Visualizations

Trade Trendz currently supports:

Visualization	Purpose
Line Plot	Analyze trends over time
Scatter Plot	Explore relationships between variables
Histogram	Understand data distributions
Box Plot	Examine distributions and extreme values
Candlestick	Visualize OHLC price movement
Area Chart	Show price trends over time
Volume Chart	Analyze trading activity
Daily Returns	Analyze percentage price changes
Moving Average	Identify smoothed price trends
Bollinger Bands	Explore price volatility
Correlation Heatmap	Analyze relationships between numerical variables
ECDF	Examine cumulative distributions
Funnel Plot	Additional exploratory visualization

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⚙️ Customization

The sidebar allows users to customize their analysis through options such as:

• Chart type

• Date range

• Analysis frequency

  • Original
  • Daily
  • Weekly
  • Monthly

• Feature selection

• Moving-average period

• Chart height

• Markers

• Logarithmic Y-axis

The date range is automatically determined from the uploaded dataset, so users can perform analysis on their own timeline rather than being restricted to a predefined period.

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🏗️ Workflow

Upload Dataset
      ↓
Data Validation
      ↓
Data Preprocessing
      ↓
Missing & Invalid Value Handling
      ↓
OHLC Validation
      ↓
Chronological Sorting
      ↓
Custom Date Filtering
      ↓
Frequency Selection
      ↓
Interactive Visualization

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🛠️ Tech Stack

• Python – Application development
• Pandas – Data manipulation and preprocessing
• Streamlit – Interactive web application
• Plotly – Interactive charts
• Seaborn – Visualization support
• OpenPyXL – XLSX file handling
• xlrd – XLS file handling

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📁 Project Structure

Trade_Trendz/
│
├── app.py
├── requirements.txt
├── README.md
└── GOOGL.xlsx

Main Files

app.py Contains the Streamlit application, preprocessing pipeline, validation logic, analysis controls, and visualizations.

requirements.txt Contains the dependencies required to run the application.

GOOGL.xlsx Sample stock-market dataset for testing the application.

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💻 Run Locally

1. Clone the Repository

git clone https://github.com/INDIAN123456890/Trade_Trendz_Updated.git
cd Trade_Trendz_Updated

2. Install Dependencies

pip install -r requirements.txt

3. Run the Application

streamlit run app.py

The application will open in your browser at the local Streamlit URL.

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☁️ Deployment

Trade Trendz is deployed using Streamlit Community Cloud.

🔴 Live Application: https://tradetrendz.streamlit.app/

The deployment uses the GitHub repository as the source and runs the Streamlit application through app.py.

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📚 Learning Outcomes

This project helped build practical experience in:

• Data cleaning and preprocessing
• Missing-value handling
• Data validation
• Time-series analysis
• Exploratory Data Analysis
• Data visualization
• Interactive dashboard development
• Python and Pandas
• Streamlit deployment

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👨‍💻 Author

Sahil Salunke

🔗 GitHub: https://github.com/INDIAN123456890

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⭐ If you find the project useful, consider giving the repository a star.