Automated Trading System for FNGU and FNGD ETFs

Project Overview

This project is an automated trading system designed to trade two highly volatile leveraged ETFs: FNGU and FNGD. The system leverages technical analysis to generate buy/sell signals based on market data and aims to automate the trading process for users with the goal of achieving at least a 10% annual return.

The system utilizes historical market data from Yahoo Finance and provides a user interface to interact with the data, select trading strategies, and backtest them.

Features

1. Data Management

• Data Download: The system downloads historical market data for FNGU and FNGD from Yahoo Finance, covering the period from 01/01/2021 to the latest market close.
• Data Storage: The downloaded data is stored in a local history.json file in JSON format, which includes:
  • Date
  • Open price
  • High price
  • Low price
  • Closing price
  • Volume

2. User Interface

The system provides a user interface with the following functionality:

• Select ETF: Choose between FNGU and FNGD.
• Download Data: Download the selected ETF's historical data to a local JSON file.
• Market Data Display: View the latest market date, price, and percentage price change compared to the previous day.
• Historical Price Graph: View a graph displaying the historical price for the selected date range.
• Trading Strategy Selection: Choose from various trading strategies based on technical indicators.

3. Technical Indicators

The system implements three common technical indicators to assist in generating trading signals:

• Simple Moving Average (SMA): Used for trend-following strategies. The system buys when the short-term moving average crosses above the long-term moving average and sells when the opposite occurs.
• Bollinger Bands (BB): Identifies overbought and oversold conditions based on the price relative to the moving average.
• MACD (Moving Average Convergence Divergence): Measures the momentum and trend of the market.

These indicators help automate the decision-making process for trading based on predefined strategies.

4. Backtesting

The system includes a backtesting feature to evaluate the effectiveness of trading strategies:

• Initial Account Balance: Set at $100,000.
• No Trading Fees: Assumed no trading fees for simplicity.
• Backtesting Metrics: The system tracks and displays key performance metrics, including:
  • Profit/Loss per trade
  • Total profit/loss
  • Return percentage compared to the initial balance
  • Trade log in trade_log.json, including:
    • Date of transaction
    • Trade type (buy/sell)
    • Number of shares traded
    • Amount spent on the trade
    • Account balance after the trade

5. Logging and Data Persistence

All trades and relevant metrics are logged in a JSON file (trade_log.json) for future analysis. The log includes detailed information about each transaction, which can be used for performance evaluation or auditing.

Technology Stack

• Programming Language: Python
• Libraries: pandas, matplotlib, Flask (for web interface), yfinance (for data retrieval)
• Data Storage: JSON file (for simplicity)
• Technical Indicators: Implemented using Python libraries and custom logic.

How to Use

Prerequisites

• Required Python libraries listed in requirements.txt.

Installation

1. Clone the repository:

   git clone https://github.com/nnayrps19/Stonks-Project.git