/tat

This collection includes additional Python scripts that extend the functionality of the Trading Automation Toolbox (TAT). Each script in this collection is designed to provide specialized analysis and insight into trading data.

Primary LanguageJupyter Notebook

Trading Analysis Tools (TAT)

Welcome to the GitHub repository for additional tools for the Trading Automation Toolbox (TAT). This repository contains two main components that are essential for analyzing and updating trading data. Below is a brief overview of each.

Components

1. Trading Analysis Python Program (ipynb)

Overview

This is a Python program developed to analyze trading data from a CSV file. Utilizing libraries such as Pandas, Matplotlib, and Seaborn, it generates insightful heatmaps that reveal patterns in trading profits based on the time and day of trade entries.

Key Features

  • Data Analysis: Leverages Pandas for data manipulation and analysis.
  • Heatmap Visualization: Utilizes Matplotlib and Seaborn to create visual representations of trading data.
  • Functionality: Includes functions for filtering data, ranking trading days and times, and generating comprehensive heatmaps.

Workflow

  • Importing necessary libraries.
  • Defining functions for data handling and visualization.
  • Loading and preparing CSV data.
  • Analyzing data and generating HTML content with heatmaps.
  • Producing an HTML report containing visual and tabular representations of analyzed data.

2. Trade Plan Database Updater (tradeplan2db3)

Overview

This component is a Python script designed to update trading parameters in the SQLite database for the "Trade Automation Toolbox." It focuses on modifying records in the TradeTemplate table and manages the ScheduleMaster table's active status.

Key Features

  • Automated Database Updates: Updates trading parameters based on CSV input.
  • Backup Protocol: Implements a timestamped backup system for data integrity.
  • Customizable: Tailored for specific naming conventions in trade templates.

Functionality

  • Identifies and updates trading templates based on CSV file input.
  • Manages IsActive status in the ScheduleMaster table.
  • Automatically generates a database backup before updating.

Usage

  • Requires Python 3, Pandas, sqlite3, shutil, and os.
  • The CSV file structure should include columns for time, premium, spread, and stop.
  • To use, place tradeplan.csv and data.db3 in the script's directory and execute with Python 3.

3. TAT Profit/Loss Visualization Script (pnl)

Overview

This is a Python script that connects to a SQLite database containing trading data, fetches the data for the current day, and visualizes it using Plotly. The visualization includes metrics such as premium sold and profit/loss (PnL) over time.

Example plot

Key Features

  • ** Real-Time Data Visualization: Generates visual representations of trading data for immediate analysis.
  • ** Comprehensive Metrics: Showcases premium sold alongside profit and loss over the course of the day.
  • ** Interactive Graphs: Utilizes Plotly for dynamic and interactive charting capabilities.

Prerequisites

  • Python 3.x
  • SQLite database (data.db3)
  • Required Python packages: sqlite3, logging, datetime, timedelta, plotly.graph_objects

Contributions and License

Contributions are welcome. Please ensure any changes are well-tested before submission. This project is released under the MIT License.