MACD Trading Strategy Visualisation 📊

Project Overview 🛠️

This project implements a trading strategy based on the MACD (Moving Average Convergence Divergence) indicator. The algorithm analyzes financial market data, identifies buy and sell signals, and simulates trading decisions, presenting results through visualizations.

How Does MACD Work? 📈

MACD helps assess market trends by analyzing two exponential moving averages:

• MACD Line: The difference between the 12-period and 26-period EMA (Exponential Moving Average)
• SIGNAL Line: A 9-period EMA of the MACD values

The crossovers of these lines generate trading signals:

• 📈 Buy – when MACD crosses SIGNAL from below
• 📉 Sell – when MACD crosses SIGNAL from above

Features 💸

• Reads market data from a .csv file
• Calculates EMA, MACD, and SIGNAL
• Detects buy and sell signals
• Simulates trading activity and tracks portfolio value
• Generates charts with transaction markers and performance analysis

Requirements 🔧

• Python 3.x
• matplotlib (for data visualization)

Install dependencies:

pip install matplotlib

Usage ▶️

1. Prepare Data

   • The .csv file should contain market data with closing prices in the 5th column (index 4).
   • Example data can be obtained from forexsb.com/historical-forex-data.

2. Run the Script

   python main.py

3. Analyze Results

   • Price Chart – shows asset value fluctuations
   • MACD & SIGNAL – displays buy and sell signals
   • Portfolio Simulation – visualizes how the portfolio value evolves

Important Notes 📜

• The first 2N days of MACD and SIGNAL values may be unstable.
• The simulation does not account for transaction fees.
• The strategy works best in long-term market trends.

Example Output 🏆

Data file: ./data/GOLD-USD-1D.csv

Total transactions: 207
Profitable transactions: 78
Failed transactions: 129

Initial capital: $1000.00
Final capital: $2080.92
Net profit: $1080.92
Return on investment: 208.09%

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Functions 🔍

# Reads market data and extracts closing prices.
def read_data(filename)

# Computes the exponential moving average for a given period `n`.
def EMA(n, data)

# Calculates MACD values based on EMA(12) and EMA(26) of the closing prices.
def MACD(close)

# Computes the SIGNAL line using EMA(9) of the MACD values.
def SIGNAL(macd)

# Identifies buy and sell signals.
def get_intersections(macd, signal)

# Simulates investment decisions and calculates financial performance.
def simulate(intersections, close, start_money=1000):

# Generates a comprehensive visualization of all analytical data.
def draw_everything(close, macd, signal, balance):