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Indicator Connector

Python integration module for technical indicators.

This module provides a bridge between Python and MT5 indicators through socket connections.

Indicator #

Indicator(address='localhost', port=9090, listen=1)

A class for connecting to and retrieving data from MetaTrader 5 technical indicators.

This class provides methods to connect to various technical indicators in MetaTrader 5 through a socket connection. Each method corresponds to a specific technical indicator and returns its calculated values.

Parameters:

Name Type Description Default
address str

The address to bind to. Defaults to "localhost".

 'localhost'
port int

The port to bind to. Defaults to 9090.

 9090
listen int

The number of connections to accept. Defaults to 1.

 1

Returns:

Type Description
None

None
Source code in mqpy\indicator_connector.py 
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def __init__(self, address: str = "localhost", port: int = 9090, listen: int = 1) -> None:
    """Initialize the Indicator connector.

    Args:
        address (str): The address to bind to. Defaults to "localhost".
        port (int): The port to bind to. Defaults to 9090.
        listen (int): The number of connections to accept. Defaults to 1.

    Returns:
        None
    """
    self.address = address
    self.port = port
    self.listen = listen
    self.location = (address, port)

    self.s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
    self.s.bind((self.address, self.port))
    self.s.listen(self.listen)

accelerator_oscillator #

accelerator_oscillator(symbol, time_frame=1, start_position=0)

Calculate the Accelerator Oscillator.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def accelerator_oscillator(
    self, symbol: str, time_frame: int = 1, start_position: int = 0
) -> dict[str, Any] | None:  # Change it if you want past values, zero is the most recent.
    """Calculate the Accelerator Oscillator.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = f"accelerator_oscillator,{symbol},{time_frame},{start_position}"

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)
        except ValueError:
            logger.exception("Connection lost to MQL5 Service")
    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")
    except ConnectionAbortedError:
        logger.exception("Connection aborted by MQL5 Service")
    finally:
        if "client_socket" in locals():
            client_socket.close()

    return None

accumulation_distribution #

accumulation_distribution(symbol, time_frame=1, start_position=0, applied_volume=0)

Calculate the Accumulation/Distribution.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
applied_volume int

The volume type for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def accumulation_distribution(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    # applied_volume:
    # 0 - VOLUME_TICK
    # 1 - VOLUME_REAL
    applied_volume: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Accumulation/Distribution.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        applied_volume (int): The volume type for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"accumulation_distribution," f"{symbol}," f"{time_frame}," f"{start_position}," f"{applied_volume}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)
        except ValueError:
            logger.exception("Connection lost to MQL5 Service")
    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")
    except ConnectionAbortedError:
        logger.exception("Connection aborted by MQL5 Service")
    finally:
        if "client_socket" in locals():
            client_socket.close()

    return None

adaptive_moving_average #

adaptive_moving_average(symbol, time_frame=1, start_position=0, ama_period=9, fast_ma_period=2, slow_ma_period=30, applied_price=4)

Calculate the Adaptive Moving Average.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
ama_period int

The period for the Adaptive Moving Average. Defaults to 9.

 9
fast_ma_period int

The fast moving average period. Defaults to 2.

 2
slow_ma_period int

The slow moving average period. Defaults to 30.

 30
applied_price int

The price type for calculations. Defaults to 4.

 4

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def adaptive_moving_average(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    ama_period: int = 9,
    fast_ma_period: int = 2,
    slow_ma_period: int = 30,
    # applied_price:
    # 0 - PRICE_CLOSE
    # 1 - PRICE_OPEN
    # 2 - PRICE_HIGH
    # 3 - PRICE_LOW
    # 4 - PRICE_MEDIAN
    # 5 - PRICE_TYPICAL
    # 6 - PRICE_WEIGHTED
    applied_price: int = 4,
) -> dict[str, Any] | None:
    """Calculate the Adaptive Moving Average.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        ama_period (int): The period for the Adaptive Moving Average. Defaults to 9.
        fast_ma_period (int): The fast moving average period. Defaults to 2.
        slow_ma_period (int): The slow moving average period. Defaults to 30.
        applied_price (int): The price type for calculations. Defaults to 4.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"adaptive_moving_average,"
            f"{symbol},"
            f"{time_frame},"
            f"{start_position},"
            f"{ama_period},"
            f"{fast_ma_period},"
            f"{slow_ma_period},"
            f"{applied_price}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)
        except ValueError:
            logger.exception("Connection lost to MQL5 Service")
    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")
    except ConnectionAbortedError:
        logger.exception("Connection aborted by MQL5 Service")
    finally:
        if "client_socket" in locals():
            client_socket.close()

    return None

alligator #

alligator(symbol, time_frame=1, start_position=0, jaw_period=13, teeth_period=8, lips_period=5, ma_method=2, applied_price=4)

Calculate the Alligator.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
jaw_period int

The period for the Alligator's jaw. Defaults to 13.

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teeth_period int

The period for the Alligator's teeth. Defaults to 8.

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lips_period int

The period for the Alligator's lips. Defaults to 5.

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ma_method int

The method for the Alligator's moving average. Defaults to 2.

 2
applied_price int

The price type for calculations. Defaults to 4.

 4

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def alligator(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    jaw_period: int = 13,
    teeth_period: int = 8,
    lips_period: int = 5,
    # method:
    # 0 - MODE_SMA
    # 1 - MODE_EMA
    # 2 - MODE_SMMA
    # 3 - MODE_LWMA
    ma_method: int = 2,
    # applied_price:
    # 0 - PRICE_CLOSE
    # 1 - PRICE_OPEN
    # 2 - PRICE_HIGH
    # 3 - PRICE_LOW
    # 4 - PRICE_MEDIAN
    # 5 - PRICE_TYPICAL
    # 6 - PRICE_WEIGHTED
    applied_price: int = 4,
) -> dict[str, Any] | None:
    """Calculate the Alligator.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        jaw_period (int): The period for the Alligator's jaw. Defaults to 13.
        teeth_period (int): The period for the Alligator's teeth. Defaults to 8.
        lips_period (int): The period for the Alligator's lips. Defaults to 5.
        ma_method (int): The method for the Alligator's moving average. Defaults to 2.
        applied_price (int): The price type for calculations. Defaults to 4.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"alligator,"
            f"{symbol},"
            f"{time_frame},"
            f"{start_position},"
            f"{jaw_period},"
            f"{teeth_period},"
            f"{lips_period},"
            f"{ma_method},"
            f"{applied_price}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)
        except ValueError:
            logger.exception("Connection lost to MQL5 Service")
    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")
    except ConnectionAbortedError:
        logger.exception("Connection aborted by MQL5 Service")
    finally:
        if "client_socket" in locals():
            client_socket.close()

    return None

average_directional_index #

average_directional_index(symbol, time_frame=1, period=14, start_position=0)

Calculate the Average Directional Index.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
period int

The period for the Average Directional Index. Defaults to 14.

 14
start_position int

Starting position for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def average_directional_index(
    self,
    symbol: str,
    time_frame: int = 1,
    period: int = 14,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
) -> dict[str, Any] | None:
    """Calculate the Average Directional Index.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        period (int): The period for the Average Directional Index. Defaults to 14.
        start_position (int): Starting position for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = f"average_directional_index," f"{symbol}," f"{time_frame}," f"{period}," f"{start_position}"

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)
        except ValueError:
            logger.exception("Connection lost to MQL5 Service")
    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")
    except ConnectionAbortedError:
        logger.exception("Connection aborted by MQL5 Service")
    finally:
        if "client_socket" in locals():
            client_socket.close()

    return None

average_directional_index_wilder #

average_directional_index_wilder(symbol, time_frame=1, period=14, start_position=0)

Calculate the Average Directional Index Wilder.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
period int

The period for the Average Directional Index Wilder. Defaults to 14.

 14
start_position int

Starting position for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def average_directional_index_wilder(
    self,
    symbol: str,
    time_frame: int = 1,
    period: int = 14,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
) -> dict[str, Any] | None:
    """Calculate the Average Directional Index Wilder.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        period (int): The period for the Average Directional Index Wilder. Defaults to 14.
        start_position (int): Starting position for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"average_directional_index_wilder," f"{symbol}," f"{time_frame}," f"{period}," f"{start_position}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)
        except ValueError:
            logger.exception("Connection lost to MQL5 Service")
    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")
    except ConnectionAbortedError:
        logger.exception("Connection aborted by MQL5 Service")
    finally:
        if "client_socket" in locals():
            client_socket.close()

    return None

average_true_range #

average_true_range(symbol, time_frame=1, start_position=0, ma_period=14)

Calculate the Average True Range.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
ma_period int

The period for the Average True Range. Defaults to 14.

 14

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def average_true_range(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    ma_period: int = 14,
) -> dict[str, Any] | None:
    """Calculate the Average True Range.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        ma_period (int): The period for the Average True Range. Defaults to 14.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = f"average_true_range," f"{symbol}," f"{time_frame}," f"{start_position}," f"{ma_period}"

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)
        except ValueError:
            logger.exception("Connection lost to MQL5 Service")
    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")
    except ConnectionAbortedError:
        logger.exception("Connection aborted by MQL5 Service")
    finally:
        if "client_socket" in locals():
            client_socket.close()

    return None

awesome_oscillator #

awesome_oscillator(symbol, time_frame=1, start_position=0)

Calculate the Awesome Oscillator.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def awesome_oscillator(
    self, symbol: str, time_frame: int = 1, start_position: int = 0
) -> dict[str, Any] | None:  # Change it if you want past values, zero is the most recent.
    """Calculate the Awesome Oscillator.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = f"awesome_oscillator," f"{symbol}," f"{time_frame}," f"{start_position}"

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)
        except ValueError:
            logger.exception("Connection lost to MQL5 Service")
    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")
    except ConnectionAbortedError:
        logger.exception("Connection aborted by MQL5 Service")
    finally:
        if "client_socket" in locals():
            client_socket.close()

    return None

bollinger_bands #

bollinger_bands(symbol, time_frame=1, period=20, start_position=0, ma_shift=0, deviation=2.0, applied_price=0)

Calculate the Bollinger Bands.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
period int

The period for the Bollinger Bands. Defaults to 20.

 20
start_position int

Starting position for calculations. Defaults to 0.

 0
ma_shift int

The shift for the moving average. Defaults to 0.

 0
deviation float

The deviation for the Bollinger Bands. Defaults to 2.000.

 2.0
applied_price int

The price type for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def bollinger_bands(
    self,
    symbol: str,
    time_frame: int = 1,
    period: int = 20,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    ma_shift: int = 0,
    deviation: float = 2.000,
    # applied_price:
    # 0 - PRICE_CLOSE
    # 1 - PRICE_OPEN
    # 2 - PRICE_HIGH
    # 3 - PRICE_LOW
    # 4 - PRICE_MEDIAN
    # 5 - PRICE_TYPICAL
    # 6 - PRICE_WEIGHTED
    applied_price: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Bollinger Bands.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        period (int): The period for the Bollinger Bands. Defaults to 20.
        start_position (int): Starting position for calculations. Defaults to 0.
        ma_shift (int): The shift for the moving average. Defaults to 0.
        deviation (float): The deviation for the Bollinger Bands. Defaults to 2.000.
        applied_price (int): The price type for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"bollinger_bands,"
            f"{symbol},"
            f"{time_frame},"
            f"{period},"
            f"{start_position},"
            f"{ma_shift},"
            f"{deviation},"
            f"{applied_price}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)
        except ValueError:
            logger.exception("Connection lost to MQL5 Service")
    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")
    except ConnectionAbortedError:
        logger.exception("Connection aborted by MQL5 Service")
    finally:
        if "client_socket" in locals():
            client_socket.close()

    return None

bears_power #

bears_power(symbol, time_frame=1, start_position=0, ma_period=13)

Calculate the Bears Power.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
ma_period int

The period for the Bears Power. Defaults to 13.

 13

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def bears_power(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    ma_period: int = 13,
) -> dict[str, Any] | None:
    """Calculate the Bears Power.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        ma_period (int): The period for the Bears Power. Defaults to 13.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = f"bears_power," f"{symbol}," f"{time_frame}," f"{start_position}," f"{ma_period}"

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)
        except ValueError:
            logger.exception("Connection lost to MQL5 Service")
    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")
    except ConnectionAbortedError:
        logger.exception("Connection aborted by MQL5 Service")
    finally:
        if "client_socket" in locals():
            client_socket.close()

    return None

bulls_power #

bulls_power(symbol, time_frame=1, start_position=0, ma_period=13)

Calculate the Bulls Power.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
ma_period int

The period for the Bulls Power. Defaults to 13.

 13

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def bulls_power(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    ma_period: int = 13,
) -> dict[str, Any] | None:
    """Calculate the Bulls Power.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        ma_period (int): The period for the Bulls Power. Defaults to 13.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = f"bulls_power," f"{symbol}," f"{time_frame}," f"{start_position}," f"{ma_period}"

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)
        except ValueError:
            logger.exception("Connection lost to MQL5 Service")
    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")
    except ConnectionAbortedError:
        logger.exception("Connection aborted by MQL5 Service")
    finally:
        if "client_socket" in locals():
            client_socket.close()

    return None

chaikin_oscillator #

chaikin_oscillator(symbol, time_frame=1, start_position=0, fast_ma_period=3, slow_ma_period=10, ma_method=1, applied_volume=0)

Calculate the Chaikin Oscillator.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
fast_ma_period int

The period for the fast moving average. Defaults to 3.

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slow_ma_period int

The period for the slow moving average. Defaults to 10.

 10
ma_method int

The method for the moving average. Defaults to 1.

 1
applied_volume int

The volume type for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def chaikin_oscillator(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    fast_ma_period: int = 3,
    slow_ma_period: int = 10,
    # method:
    # 0 - MODE_SMA
    # 1 - MODE_EMA
    # 2 - MODE_SMMA
    # 3 - MODE_LWMA
    ma_method: int = 1,
    # applied_volume:
    # 0 - VOLUME_TICK
    # 1 - VOLUME_REAL
    applied_volume: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Chaikin Oscillator.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        fast_ma_period (int): The period for the fast moving average. Defaults to 3.
        slow_ma_period (int): The period for the slow moving average. Defaults to 10.
        ma_method (int): The method for the moving average. Defaults to 1.
        applied_volume (int): The volume type for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"chaikin_oscillator,"
            f"{symbol},"
            f"{time_frame},"
            f"{start_position},"
            f"{fast_ma_period},"
            f"{slow_ma_period},"
            f"{ma_method},"
            f"{applied_volume}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)
        except ValueError:
            logger.exception("Connection lost to MQL5 Service")
    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")
    except ConnectionAbortedError:
        logger.exception("Connection aborted by MQL5 Service")
    finally:
        if "client_socket" in locals():
            client_socket.close()

    return None

commodity_channel_index #

commodity_channel_index(symbol, time_frame=1, start_position=1, ma_period=14, applied_price=0)

Calculate the Commodity Channel Index.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 1.

 1
ma_period int

The period for the Commodity Channel Index. Defaults to 14.

 14
applied_price int

The price type for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def commodity_channel_index(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 1,  # Change it if you want past values, zero is the most recent.
    ma_period: int = 14,
    # For this Indicator, the applied price is shifted.
    # applied_price:
    # 1 - PRICE_CLOSE
    # 2 - PRICE_OPEN
    # 3 - PRICE_HIGH
    # 4 - PRICE_LOW
    # 5 - PRICE_MEDIAN
    # 6 - PRICE_TYPICAL
    # 7 - PRICE_WEIGHTED
    applied_price: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Commodity Channel Index.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 1.
        ma_period (int): The period for the Commodity Channel Index. Defaults to 14.
        applied_price (int): The price type for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"commodity_channel_index,"
            f"{symbol},"
            f"{time_frame},"
            f"{start_position},"
            f"{ma_period},"
            f"{applied_price}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)
        except ValueError:
            logger.exception("Connection lost to MQL5 Service")
    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")
    except ConnectionAbortedError:
        logger.exception("Connection aborted by MQL5 Service")
    finally:
        if "client_socket" in locals():
            client_socket.close()

    return None

demarker #

demarker(symbol, time_frame=1, start_position=0, period=14)

Calculate the DeMarker.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
period int

The period for the DeMarker. Defaults to 14.

 14

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def demarker(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    period: int = 14,
) -> dict[str, Any] | None:
    """Calculate the DeMarker.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        period (int): The period for the DeMarker. Defaults to 14.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = f"demarker," f"{symbol}," f"{time_frame}," f"{start_position}," f"{period}"

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)
        except ValueError:
            logger.exception("Connection lost to MQL5 Service")
    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")
    except ConnectionAbortedError:
        logger.exception("Connection aborted by MQL5 Service")
    finally:
        if "client_socket" in locals():
            client_socket.close()

    return None

double_exponential_moving_average #

double_exponential_moving_average(symbol, time_frame=1, start_position=0, ma_period=14, applied_price=0)

Calculate the Double Exponential Moving Average.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
ma_period int

The period for the Double Exponential Moving Average. Defaults to 14.

 14
applied_price int

The price type for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def double_exponential_moving_average(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    ma_period: int = 14,
    # applied_price:
    # 0 - PRICE_CLOSE
    # 1 - PRICE_OPEN
    # 2 - PRICE_HIGH
    # 3 - PRICE_LOW
    # 4 - PRICE_MEDIAN
    # 5 - PRICE_TYPICAL
    # 6 - PRICE_WEIGHTED
    applied_price: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Double Exponential Moving Average.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        ma_period (int): The period for the Double Exponential Moving Average. Defaults to 14.
        applied_price (int): The price type for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"double_exponential_moving_average,"
            f"{symbol},"
            f"{time_frame},"
            f"{start_position},"
            f"{ma_period},"
            f"{applied_price}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

envelopes #

envelopes(symbol, time_frame=1, start_position=0, ma_period=14, ma_method=0, applied_price=1, deviation=0.1)

Calculate the Envelopes.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
ma_period int

The period for the Envelopes. Defaults to 14.

 14
ma_method int

The method for the Envelopes. Defaults to 0.

 0
applied_price int

The price type for calculations. Defaults to 1.

 1
deviation float

The deviation for the Envelopes. Defaults to 0.100.

 0.1

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def envelopes(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    ma_period: int = 14,
    # method:
    # 0 - MODE_SMA
    # 1 - MODE_EMA
    # 2 - MODE_SMMA
    # 3 - MODE_LWMA
    ma_method: int = 0,
    # For this Indicator, the applied price is shifted.
    # applied_price:
    # 1 - PRICE_CLOSE
    # 2 - PRICE_OPEN
    # 3 - PRICE_HIGH
    # 4 - PRICE_LOW
    # 5 - PRICE_MEDIAN
    # 6 - PRICE_TYPICAL
    # 7 - PRICE_WEIGHTED
    applied_price: int = 1,
    deviation: float = 0.100,
) -> dict[str, Any] | None:
    """Calculate the Envelopes.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        ma_period (int): The period for the Envelopes. Defaults to 14.
        ma_method (int): The method for the Envelopes. Defaults to 0.
        applied_price (int): The price type for calculations. Defaults to 1.
        deviation (float): The deviation for the Envelopes. Defaults to 0.100.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"envelopes,"
            f"{symbol},"
            f"{time_frame},"
            f"{start_position},"
            f"{ma_period},"
            f"{ma_method},"
            f"{applied_price},"
            f"{deviation}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

force_index #

force_index(symbol, time_frame=1, start_position=0, ma_period=13, ma_method=0, applied_volume=0)

Calculate the Force Index.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
ma_period int

The period for the Force Index. Defaults to 13.

 13
ma_method int

The method for the Force Index. Defaults to 0.

 0
applied_volume int

The volume type for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def force_index(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    ma_period: int = 13,
    # method:
    # 0 - MODE_SMA
    # 1 - MODE_EMA
    # 2 - MODE_SMMA
    # 3 - MODE_LWMA
    ma_method: int = 0,
    # applied_volume:
    # 0 - VOLUME_TICK
    # 1 - VOLUME_REAL
    applied_volume: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Force Index.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        ma_period (int): The period for the Force Index. Defaults to 13.
        ma_method (int): The method for the Force Index. Defaults to 0.
        applied_volume (int): The volume type for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"force_index,"
            f"{symbol},"
            f"{time_frame},"
            f"{start_position},"
            f"{ma_period},"
            f"{ma_method},"
            f"{applied_volume}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

fractal_adaptive_moving_average #

fractal_adaptive_moving_average(symbol, time_frame=1, start_position=0, ma_period=14, applied_price=0)

Calculate the Fractal Adaptive Moving Average.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
ma_period int

The period for the Fractal Adaptive Moving Average. Defaults to 14.

 14
applied_price int

The price type for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def fractal_adaptive_moving_average(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    ma_period: int = 14,
    # applied_price:
    # 0 - PRICE_CLOSE
    # 1 - PRICE_OPEN
    # 2 - PRICE_HIGH
    # 3 - PRICE_LOW
    # 4 - PRICE_MEDIAN
    # 5 - PRICE_TYPICAL
    # 6 - PRICE_WEIGHTED
    applied_price: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Fractal Adaptive Moving Average.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        ma_period (int): The period for the Fractal Adaptive Moving Average. Defaults to 14.
        applied_price (int): The price type for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"fractal_adaptive_moving_average,"
            f"{symbol},"
            f"{time_frame},"
            f"{start_position},"
            f"{ma_period},"
            f"{applied_price}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

fractals #

fractals(symbol, time_frame=1, start_position=0)

Calculate the Fractals.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def fractals(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
) -> dict[str, Any] | None:
    """Calculate the Fractals.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = f"fractals," f"{symbol}," f"{time_frame}," f"{start_position}"

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

gator_oscillator #

gator_oscillator(symbol, time_frame=1, start_position=0, jaw_period=13, jaw_shift=8, teeth_period=8, teeth_shift=5, lips_period=5, lips_shift=3, ma_method=2, applied_price=4)

Calculate the Gator Oscillator.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
jaw_period int

The period for the jaw. Defaults to 13.

 13
jaw_shift int

The shift for the jaw. Defaults to 8.

 8
teeth_period int

The period for the teeth. Defaults to 8.

 8
teeth_shift int

The shift for the teeth. Defaults to 5.

 5
lips_period int

The period for the lips. Defaults to 5.

 5
lips_shift int

The shift for the lips. Defaults to 3.

 3
ma_method int

The method for the moving average. Defaults to 2.

 2
applied_price int

The price type for calculations. Defaults to 4.

 4

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def gator_oscillator(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    jaw_period: int = 13,
    jaw_shift: int = 8,
    teeth_period: int = 8,
    teeth_shift: int = 5,
    lips_period: int = 5,
    lips_shift: int = 3,
    # method:
    # 0 - MODE_SMA
    # 1 - MODE_EMA
    # 2 - MODE_SMMA
    # 3 - MODE_LWMA
    ma_method: int = 2,
    # applied_price:
    # 0 - PRICE_CLOSE
    # 1 - PRICE_OPEN
    # 2 - PRICE_HIGH
    # 3 - PRICE_LOW
    # 4 - PRICE_MEDIAN
    # 5 - PRICE_TYPICAL
    # 6 - PRICE_WEIGHTED
    applied_price: int = 4,
) -> dict[str, Any] | None:
    """Calculate the Gator Oscillator.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        jaw_period (int): The period for the jaw. Defaults to 13.
        jaw_shift (int): The shift for the jaw. Defaults to 8.
        teeth_period (int): The period for the teeth. Defaults to 8.
        teeth_shift (int): The shift for the teeth. Defaults to 5.
        lips_period (int): The period for the lips. Defaults to 5.
        lips_shift (int): The shift for the lips. Defaults to 3.
        ma_method (int): The method for the moving average. Defaults to 2.
        applied_price (int): The price type for calculations. Defaults to 4.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"gator_oscillator,"
            f"{symbol},"
            f"{time_frame},"
            f"{start_position},"
            f"{jaw_period},"
            f"{jaw_shift},"
            f"{teeth_period},"
            f"{teeth_shift},"
            f"{lips_period},"
            f"{lips_shift},"
            f"{ma_method},"
            f"{applied_price}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

ichimoku_kinko_hyo #

ichimoku_kinko_hyo(symbol, time_frame=1, start_position=0, tenkan_sen=9, kijun_sen=26, senkou_span_b=52)

Calculate the Ichimoku Kinko Hyo.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
tenkan_sen int

The period for the Tenkan-sen. Defaults to 9.

 9
kijun_sen int

The period for the Kijun-sen. Defaults to 26.

 26
senkou_span_b int

The period for the Senkou Span B. Defaults to 52.

 52

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def ichimoku_kinko_hyo(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    tenkan_sen: int = 9,
    kijun_sen: int = 26,
    senkou_span_b: int = 52,
) -> dict[str, Any] | None:
    """Calculate the Ichimoku Kinko Hyo.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        tenkan_sen (int): The period for the Tenkan-sen. Defaults to 9.
        kijun_sen (int): The period for the Kijun-sen. Defaults to 26.
        senkou_span_b (int): The period for the Senkou Span B. Defaults to 52.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"ichimoku_kinko_hyo,"
            f"{symbol},"
            f"{time_frame},"
            f"{start_position},"
            f"{tenkan_sen},"
            f"{kijun_sen},"
            f"{senkou_span_b}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

macd #

macd(symbol, time_frame=1, fast_ema_period=12, slow_ema_period=26, signal_period=9, start_position=0, applied_price=0)

Calculate the Moving Average Convergence Divergence.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
fast_ema_period int

The period for the fast EMA. Defaults to 12.

 12
slow_ema_period int

The period for the slow EMA. Defaults to 26.

 26
signal_period int

The period for the signal line. Defaults to 9.

 9
start_position int

Starting position for calculations. Defaults to 0.

 0
applied_price int

The price type for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def macd(
    self,
    symbol: str,
    time_frame: int = 1,
    fast_ema_period: int = 12,
    slow_ema_period: int = 26,
    signal_period: int = 9,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    # applied_price:
    # 0 - PRICE_CLOSE
    # 1 - PRICE_OPEN
    # 2 - PRICE_HIGH
    # 3 - PRICE_LOW
    # 4 - PRICE_MEDIAN
    # 5 - PRICE_TYPICAL
    # 6 - PRICE_WEIGHTED
    applied_price: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Moving Average Convergence Divergence.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        fast_ema_period (int): The period for the fast EMA. Defaults to 12.
        slow_ema_period (int): The period for the slow EMA. Defaults to 26.
        signal_period (int): The period for the signal line. Defaults to 9.
        start_position (int): Starting position for calculations. Defaults to 0.
        applied_price (int): The price type for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"macd,"
            f"{symbol},"
            f"{time_frame},"
            f"{fast_ema_period},"
            f"{slow_ema_period},"
            f"{signal_period},"
            f"{start_position},"
            f"{applied_price}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

market_facilitation_index #

market_facilitation_index(symbol, time_frame=1, start_position=0, applied_volume=0)

Calculate the Market Facilitation Index.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
applied_volume int

Volume type to use in calculations. Defaults to 0 (VOLUME_TICK).

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def market_facilitation_index(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    # applied_volume:
    # 0 - VOLUME_TICK
    # 1 - VOLUME_REAL
    applied_volume: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Market Facilitation Index.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        applied_volume (int): Volume type to use in calculations. Defaults to 0 (VOLUME_TICK).

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"market_facilitation_index," f"{symbol}," f"{time_frame}," f"{start_position}," f"{applied_volume}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)
        except ValueError:
            logger.exception("Connection lost to MQL5 Service")
    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")
    finally:
        if "client_socket" in locals():
            client_socket.close()

    return None

momentum #

momentum(symbol, time_frame=1, start_position=0, mom_period=14, applied_price=0)

Calculate the Momentum.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
mom_period int

The period for the Momentum. Defaults to 14.

 14
applied_price int

The price type for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def momentum(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    mom_period: int = 14,
    # applied_price:
    # 0 - PRICE_CLOSE
    # 1 - PRICE_OPEN
    # 2 - PRICE_HIGH
    # 3 - PRICE_LOW
    # 4 - PRICE_MEDIAN
    # 5 - PRICE_TYPICAL
    # 6 - PRICE_WEIGHTED
    applied_price: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Momentum.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        mom_period (int): The period for the Momentum. Defaults to 14.
        applied_price (int): The price type for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"momentum," f"{symbol}," f"{time_frame}," f"{start_position}," f"{mom_period}," f"{applied_price}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

money_flow_index #

money_flow_index(symbol, time_frame=1, start_position=0, ma_period=14, applied_volume=0)

Calculate the Money Flow Index.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
ma_period int

The period for the Money Flow Index. Defaults to 14.

 14
applied_volume int

The volume type for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def money_flow_index(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    ma_period: int = 14,
    # applied_volume:
    # 0 - VOLUME_TICK
    # 1 - VOLUME_REAL
    applied_volume: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Money Flow Index.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        ma_period (int): The period for the Money Flow Index. Defaults to 14.
        applied_volume (int): The volume type for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"money_flow_index,"
            f"{symbol},"
            f"{time_frame},"
            f"{start_position},"
            f"{ma_period},"
            f"{applied_volume}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)
        except ValueError:
            logger.exception("Connection lost to MQL5 Service")
    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")
    finally:
        if "client_socket" in locals():
            client_socket.close()

    return None

moving_average #

moving_average(symbol, time_frame=1, period=20, start_position=0, method=0, applied_price=0)

Calculate the Moving Average.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
period int

The period for the Moving Average. Defaults to 20.

 20
start_position int

Starting position for calculations. Defaults to 0.

 0
method int

The method for the Moving Average. Defaults to 0.

 0
applied_price int

The price type for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def moving_average(
    self,
    symbol: str,
    time_frame: int = 1,
    period: int = 20,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    # method:
    # 0 - MODE_SMA
    # 1 - MODE_EMA
    # 2 - MODE_SMMA
    # 3 - MODE_LWMA
    method: int = 0,
    # applied_price:
    # 0 - PRICE_CLOSE
    # 1 - PRICE_OPEN
    # 2 - PRICE_HIGH
    # 3 - PRICE_LOW
    # 4 - PRICE_MEDIAN
    # 5 - PRICE_TYPICAL
    # 6 - PRICE_WEIGHTED
    applied_price: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Moving Average.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        period (int): The period for the Moving Average. Defaults to 20.
        start_position (int): Starting position for calculations. Defaults to 0.
        method (int): The method for the Moving Average. Defaults to 0.
        applied_price (int): The price type for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"moving_average,"
            f"{symbol},"
            f"{time_frame},"
            f"{period},"
            f"{start_position},"
            f"{method},"
            f"{applied_price}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

moving_average_of_oscillator #

moving_average_of_oscillator(symbol, time_frame=1, start_position=0, fast_ema_period=12, slow_ema_period=26, macd_sma_period=9, applied_price=0)

Calculate the Moving Average of Oscillator.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
fast_ema_period int

The period for the fast EMA. Defaults to 12.

 12
slow_ema_period int

The period for the slow EMA. Defaults to 26.

 26
macd_sma_period int

The period for the MACD SMA. Defaults to 9.

 9
applied_price int

The price type for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def moving_average_of_oscillator(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    fast_ema_period: int = 12,
    slow_ema_period: int = 26,
    macd_sma_period: int = 9,
    # applied_price:
    # 0 - PRICE_CLOSE
    # 1 - PRICE_OPEN
    # 2 - PRICE_HIGH
    # 3 - PRICE_LOW
    # 4 - PRICE_MEDIAN
    # 5 - PRICE_TYPICAL
    # 6 - PRICE_WEIGHTED
    applied_price: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Moving Average of Oscillator.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        fast_ema_period (int): The period for the fast EMA. Defaults to 12.
        slow_ema_period (int): The period for the slow EMA. Defaults to 26.
        macd_sma_period (int): The period for the MACD SMA. Defaults to 9.
        applied_price (int): The price type for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"moving_average_of_oscillator,"
            f"{symbol},"
            f"{time_frame},"
            f"{start_position},"
            f"{fast_ema_period},"
            f"{slow_ema_period},"
            f"{macd_sma_period},"
            f"{applied_price}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

obv #

obv(symbol, time_frame=1, start_position=0, applied_volume=0)

Calculate the On Balance Volume.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
applied_volume int

The volume type for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def obv(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    # applied_volume:
    # 0 - VOLUME_TICK
    # 1 - VOLUME_REAL
    applied_volume: int = 0,
) -> dict[str, Any] | None:
    """Calculate the On Balance Volume.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        applied_volume (int): The volume type for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = f"obv," f"{symbol}," f"{time_frame}," f"{start_position}," f"{applied_volume}"

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

parabolic_sar #

parabolic_sar(symbol, time_frame=1, start_position=0, step=0.02, maximum=0.2)

Calculate the Parabolic SAR.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
step float

The step for the Parabolic SAR. Defaults to 0.02.

 0.02
maximum float

The maximum for the Parabolic SAR. Defaults to 0.2.

 0.2

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def parabolic_sar(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    step: float = 0.02,
    maximum: float = 0.2,
) -> dict[str, Any] | None:
    """Calculate the Parabolic SAR.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        step (float): The step for the Parabolic SAR. Defaults to 0.02.
        maximum (float): The maximum for the Parabolic SAR. Defaults to 0.2.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = f"parabolic_sar," f"{symbol}," f"{time_frame}," f"{start_position}," f"{step}," f"{maximum}"

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

relative_strength_index #

relative_strength_index(symbol, time_frame=1, start_position=0, ma_period=14, applied_price=0)

Calculate the Relative Strength Index.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
ma_period int

The period for the Relative Strength Index. Defaults to 14.

 14
applied_price int

The price type for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def relative_strength_index(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    ma_period: int = 14,
    # applied_price:
    # 0 - PRICE_CLOSE
    # 1 - PRICE_OPEN
    # 2 - PRICE_HIGH
    # 3 - PRICE_LOW
    # 4 - PRICE_MEDIAN
    # 5 - PRICE_TYPICAL
    # 6 - PRICE_WEIGHTED
    applied_price: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Relative Strength Index.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        ma_period (int): The period for the Relative Strength Index. Defaults to 14.
        applied_price (int): The price type for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"relative_strength_index,"
            f"{symbol},"
            f"{time_frame},"
            f"{start_position},"
            f"{ma_period},"
            f"{applied_price}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

relative_vigor_index #

relative_vigor_index(symbol, time_frame=1, start_position=0, ma_period=10)

Calculate the Relative Vigor Index.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
ma_period int

The period for the Relative Vigor Index. Defaults to 10.

 10

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def relative_vigor_index(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    ma_period: int = 10,
) -> dict[str, Any] | None:
    """Calculate the Relative Vigor Index.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        ma_period (int): The period for the Relative Vigor Index. Defaults to 10.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = f"relative_vigor_index," f"{symbol}," f"{time_frame}," f"{start_position}," f"{ma_period}"

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

standard_deviation #

standard_deviation(symbol, time_frame=1, start_position=0, ma_period=20, ma_method=0, applied_price=0)

Calculate the Standard Deviation.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
ma_period int

The period for the Standard Deviation. Defaults to 20.

 20
ma_method int

The method for the Standard Deviation. Defaults to 0.

 0
applied_price int

The price type for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def standard_deviation(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    ma_period: int = 20,
    # method:
    # 0 - MODE_SMA
    # 1 - MODE_EMA
    # 2 - MODE_SMMA
    # 3 - MODE_LWMA
    ma_method: int = 0,
    # applied_price:
    # 0 - PRICE_CLOSE
    # 1 - PRICE_OPEN
    # 2 - PRICE_HIGH
    # 3 - PRICE_LOW
    # 4 - PRICE_MEDIAN
    # 5 - PRICE_TYPICAL
    # 6 - PRICE_WEIGHTED
    applied_price: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Standard Deviation.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        ma_period (int): The period for the Standard Deviation. Defaults to 20.
        ma_method (int): The method for the Standard Deviation. Defaults to 0.
        applied_price (int): The price type for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"standard_deviation,"
            f"{symbol},"
            f"{time_frame},"
            f"{start_position},"
            f"{ma_period},"
            f"{ma_method},"
            f"{applied_price}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

stochastic #

stochastic(symbol, time_frame=1, k_period=5, d_period=3, slowing=3, start_position=0, method=0, applied_price=0)

Calculate the Stochastic.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
k_period int

The period for the Stochastic. Defaults to 5.

 5
d_period int

The period for the Stochastic. Defaults to 3.

 3
slowing int

The slowing for the Stochastic. Defaults to 3.

 3
start_position int

Starting position for calculations. Defaults to 0.

 0
method int

The method for the Stochastic. Defaults to 0.

 0
applied_price int

The price type for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def stochastic(
    self,
    symbol: str,
    time_frame: int = 1,
    k_period: int = 5,
    d_period: int = 3,
    slowing: int = 3,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    # method:
    # 0 - MODE_SMA
    # 1 - MODE_EMA
    # 2 - MODE_SMMA
    # 3 - MODE_LWMA
    method: int = 0,
    # applied_price
    # 0 - STO_LOWHIGH
    # 1 - STO_CLOSECLOSE
    applied_price: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Stochastic.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        k_period (int): The period for the Stochastic. Defaults to 5.
        d_period (int): The period for the Stochastic. Defaults to 3.
        slowing (int): The slowing for the Stochastic. Defaults to 3.
        start_position (int): Starting position for calculations. Defaults to 0.
        method (int): The method for the Stochastic. Defaults to 0.
        applied_price (int): The price type for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"stochastic,"
            f"{symbol},"
            f"{time_frame},"
            f"{k_period},"
            f"{d_period},"
            f"{slowing},"
            f"{start_position},"
            f"{method},"
            f"{applied_price}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

triple_exponential_ma_oscillator #

triple_exponential_ma_oscillator(symbol, time_frame, start_position=0, count=100, ma_period=14, applied_price=0)

Calculate the Triple Exponential Moving Average Oscillator.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations.

 required
start_position int

Starting position for calculations. Defaults to 0.

 0
count int

Number of elements to calculate. Defaults to 100.

 100
ma_period int

Moving average period. Defaults to 14.

 14
applied_price int

Price type to use in calculations. Defaults to 0 (PRICE_CLOSE).

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def triple_exponential_ma_oscillator(
    self,
    symbol: str,
    time_frame: int,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    count: int = 100,  # Number of elements to be calculated.
    ma_period: int = 14,
    # 0 - PRICE_CLOSE
    # 1 - PRICE_OPEN
    # 2 - PRICE_HIGH
    # 3 - PRICE_LOW
    # 4 - PRICE_MEDIAN
    # 5 - PRICE_TYPICAL
    # 6 - PRICE_WEIGHTED
    applied_price: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Triple Exponential Moving Average Oscillator.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations.
        start_position (int): Starting position for calculations. Defaults to 0.
        count (int): Number of elements to calculate. Defaults to 100.
        ma_period (int): Moving average period. Defaults to 14.
        applied_price (int): Price type to use in calculations. Defaults to 0 (PRICE_CLOSE).

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        try:
            data = f"TEMA_OSC,{symbol},{time_frame},{start_position},{count},{ma_period},{applied_price}"
            client_socket.send(data.encode())
            response = client_socket.recv(1024).decode()
            return ast.literal_eval(response)
        except ValueError:
            logger.exception("Connection lost to MQL5 Service")
    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")
    finally:
        client_socket.close()
    return None

triple_exponential_moving_average #

triple_exponential_moving_average(symbol, time_frame=1, start_position=0, ma_period=14, applied_price=0)

Calculate the Triple Exponential Moving Average.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
ma_period int

The period for the Triple Exponential Moving Average. Defaults to 14.

 14
applied_price int

The price type for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def triple_exponential_moving_average(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values,
    # zero is the most recent.
    ma_period: int = 14,
    # applied_price:
    # 0 - PRICE_CLOSE
    # 1 - PRICE_OPEN
    # 2 - PRICE_HIGH
    # 3 - PRICE_LOW
    # 4 - PRICE_MEDIAN
    # 5 - PRICE_TYPICAL
    # 6 - PRICE_WEIGHTED
    applied_price: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Triple Exponential Moving Average.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        ma_period (int): The period for the Triple Exponential Moving Average. Defaults to 14.
        applied_price (int): The price type for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"triple_exponential_moving_average,"
            f"{symbol},"
            f"{time_frame},"
            f"{start_position},"
            f"{ma_period},"
            f"{applied_price}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

variable_index_dynamic_average #

variable_index_dynamic_average(symbol, time_frame=1, start_position=0, cmo_period=9, ema_period=12, applied_price=0)

Calculate the Variable Index Dynamic Average.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
cmo_period int

The period for the CMO. Defaults to 9.

 9
ema_period int

The period for the EMA. Defaults to 12.

 12
applied_price int

The price type for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def variable_index_dynamic_average(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    cmo_period: int = 9,
    ema_period: int = 12,
    # applied_price:
    # 0 - PRICE_CLOSE
    # 1 - PRICE_OPEN
    # 2 - PRICE_HIGH
    # 3 - PRICE_LOW
    # 4 - PRICE_MEDIAN
    # 5 - PRICE_TYPICAL
    # 6 - PRICE_WEIGHTED
    applied_price: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Variable Index Dynamic Average.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        cmo_period (int): The period for the CMO. Defaults to 9.
        ema_period (int): The period for the EMA. Defaults to 12.
        applied_price (int): The price type for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = (
            f"variable_index_dynamic_average,"
            f"{symbol},"
            f"{time_frame},"
            f"{start_position},"
            f"{cmo_period},"
            f"{ema_period},"
            f"{applied_price}"
        )

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

volumes #

volumes(symbol, time_frame=1, start_position=0, applied_volume=0)

Calculate the Volumes.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
applied_volume int

The volume type for calculations. Defaults to 0.

 0

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def volumes(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    # applied_volume:
    # 0 - VOLUME_TICK
    # 1 - VOLUME_REAL
    applied_volume: int = 0,
) -> dict[str, Any] | None:
    """Calculate the Volumes.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        applied_volume (int): The volume type for calculations. Defaults to 0.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = f"volumes," f"{symbol}," f"{time_frame}," f"{start_position}," f"{applied_volume}"

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")

williams_percent_range #

williams_percent_range(symbol, time_frame=1, start_position=0, calc_period=14)

Calculate the Williams Percent Range.

Parameters:

Name Type Description Default
symbol str

The financial instrument symbol.

 required
time_frame int

The time frame for calculations. Defaults to 1.

 1
start_position int

Starting position for calculations. Defaults to 0.

 0
calc_period int

The period for the Williams Percent Range. Defaults to 14.

 14

Returns:

Type Description
dict[str, Any] | None

dict[str, Any] | None: The calculated values if successful, None otherwise.
Source code in mqpy\indicator_connector.py 
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def williams_percent_range(
    self,
    symbol: str,
    time_frame: int = 1,
    start_position: int = 0,  # Change it if you want past values, zero is the most recent.
    calc_period: int = 14,
) -> dict[str, Any] | None:
    """Calculate the Williams Percent Range.

    Args:
        symbol (str): The financial instrument symbol.
        time_frame (int): The time frame for calculations. Defaults to 1.
        start_position (int): Starting position for calculations. Defaults to 0.
        calc_period (int): The period for the Williams Percent Range. Defaults to 14.

    Returns:
        dict[str, Any] | None: The calculated values if successful, None otherwise.
    """
    try:
        client_socket, address = self.s.accept()
        message = f"williams_percent_range," f"{symbol}," f"{time_frame}," f"{start_position}," f"{calc_period}"

        client_socket.send(bytes(message, "utf-8"))
        data = client_socket.recv(1024)

        result = data.decode("utf-8")
        try:
            return json.loads(result)

        except ValueError:
            logger.exception("Connection lost to MQL5 Service")

    except ConnectionResetError:
        logger.exception("Connection reset by MQL5 Service")

    except ConnectionAbortedError:
        logger.exception("Connection reset by MQL5 Service")