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MQPy Trading Strategy Examples#

This directory contains various example trading strategies implemented using the MQPy framework for MetaTrader 5 integration.

Getting Started#

If you're new to MQPy, start with the getting_started.py example which demonstrates basic concepts:

  • Initializing the trading environment
  • Fetching market data
  • Making trading decisions
  • Executing trades

Available Examples#

Basic Strategies#

  1. Getting Started (getting_started.py)
  2. A simple introduction to the MQPy framework
  3. Demonstrates basic data retrieval and trading operations

  4. Perfect for beginners

  5. Moving Average Crossover (basic_moving_average_strategy.py)

  6. Uses crossovers between short and long moving averages
  7. Implements proper crossover detection logic
  8. Includes logging and exception handling

Technical Indicator Strategies#

  1. RSI Strategy (rsi_strategy.py)
  2. Implements the Relative Strength Index (RSI) indicator
  3. Trades based on overbought and oversold conditions

  4. Shows how to calculate and use technical indicators

  5. Bollinger Bands Strategy (bollinger_bands_strategy.py)

  6. Uses Bollinger Bands for trading range breakouts
  7. Demonstrates mean reversion trading principles
  8. Includes volatility-based entry and exit logic

Advanced Strategies#

  1. Fibonacci Retracement Strategy (fibonacci_retracement_eurusd.py)
  2. Implements the FiMathe strategy for EURUSD
  3. Uses Fibonacci retracement levels for entries and exits

  4. Includes dynamic stop-loss adjustment based on price action

  5. Multi-Timeframe Analysis (rate_converter_example.py)

  6. Demonstrates how to convert between different timeframes using the RateConverter
  7. Implements multi-timeframe analysis by calculating moving averages across timeframes
  8. Visualizes price data and indicators across 1-minute, 5-minute, and 1-hour charts

Fibonacci Retracement Strategy#

The Fibonacci Retracement strategy (fibonacci_retracement_eurusd.py) demonstrates how to implement a trading system based on Fibonacci retracement levels. This strategy:

  1. Identifies swing points: The algorithm detects significant market swing highs and lows within a specified window.
  2. Calculates Fibonacci levels: Standard Fibonacci ratios (0, 0.236, 0.382, 0.5, 0.618, 0.786, 1.0) are applied between swing points to generate potential support and resistance levels.
  3. Generates trading signals: The strategy produces buy signals when price bounces off key retracement levels during uptrends and sell signals during downtrends.
  4. Visualizes analysis: Creates charts showing price action with identified swing points and Fibonacci levels to aid in trading decisions.

This approach is popular among technical traders who believe that markets frequently retrace a predictable portion of a move before continuing in the original direction.

Market Depth Analysis#

The Market Depth Analysis tool (market_depth_analysis.py) provides insights into order book data (DOM - Depth of Market) to understand supply and demand dynamics. Key features include:

  1. Real-time market depth monitoring: Captures and analyzes order book snapshots at regular intervals.
  2. Buy/sell pressure analysis: Calculates metrics such as buy/sell volume ratio, percentage distribution, and order concentration.
  3. Support/resistance identification: Detects potential support and resistance levels based on unusual volume concentration at specific price points.
  4. Visual representation: Creates horizontal bar charts showing the distribution of buy (bid) and sell (ask) orders, with highlighted support/resistance zones.

This analysis helps traders understand current market sentiment and identify price levels where significant buying or selling interest exists. The tool is particularly valuable for short-term traders and those interested in order flow analysis.

Detailed Strategy Documentation#

For an in-depth explanation of advanced strategies including theoretical background, implementation details, and potential customizations, see the detailed strategy documentation.

Running the Examples#

  1. Make sure you have MQPy installed: 

    pip install mqpy

  2. Ensure MetaTrader 5 is installed and running on your system

  3. Run any example with Python: 

    python getting_started.py

Strategy Development Best Practices#

When developing your own strategies with MQPy, consider the following best practices:

  1. Error Handling: Implement proper exception handling to catch network issues, data problems, or unexpected errors

  2. Logging: Use Python's logging module to record important events and debug information

  3. Testing: Test your strategy on historical data before deploying with real money

  4. Risk Management: Always implement proper stop-loss and take-profit levels

  5. Architecture: Separate your trading logic, indicators, and execution code for better maintainability

Contributing#

If you've developed an interesting strategy using MQPy, consider contributing it to this examples collection by submitting a pull request.

Disclaimer#

These example strategies are for educational purposes only and are not financial advice. Always perform your own analysis and risk assessment before trading with real money.