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MH l
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Initial commit: full project structure with strategy implementation, backtesting logic, and result analysis tools
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junit.xml

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test/test_mt5_connector.py

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import os
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import pytest
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from unittest import mock
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from datetime import datetime
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import pandas as pd
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from src import mt5_connector
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# ----------------------------
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# Test: load_mt5_credentials
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# ----------------------------
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def test_load_mt5_credentials(monkeypatch):
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monkeypatch.setenv("MT5_LOGIN", "123456")
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monkeypatch.setenv("MT5_PASSWORD", "testpass")
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monkeypatch.setenv("MT5_SERVER", "TestServer")
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monkeypatch.setenv("MT5_PATH", "/path/to/terminal.exe")
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creds = mt5_connector.load_mt5_credentials()
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assert creds["login"] == 123456
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assert creds["password"] == "testpass"
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assert creds["server"] == "TestServer"
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assert creds["path"] == "/path/to/terminal.exe"
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# ----------------------------
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# Test: initialize_mt5_connection
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# ----------------------------
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@mock.patch("src.mt5_connector.mt5.initialize")
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@mock.patch("src.mt5_connector.load_mt5_credentials")
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def test_initialize_mt5_connection_success(mock_creds, mock_mt5_init):
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mock_creds.return_value = {
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"login": 123456,
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"password": "testpass",
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"server": "TestServer",
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"path": "/fake/path"
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}
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mock_mt5_init.return_value = True
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result = mt5_connector.initialize_mt5_connection()
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assert result is True
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# ----------------------------
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# Test: get_historical_data
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# ----------------------------
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@mock.patch("src.mt5_connector.mt5.copy_rates_range")
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@mock.patch("src.mt5_connector.mt5.initialize")
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@mock.patch("src.mt5_connector.mt5.shutdown")
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@mock.patch("src.mt5_connector.load_mt5_credentials")
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def test_get_historical_data_returns_dataframe(mock_creds, mock_shutdown, mock_init, mock_copy_rates):
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mock_creds.return_value = {
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"login": 123456,
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"password": "testpass",
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"server": "TestServer",
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"path": "/fake/path"
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}
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mock_init.return_value = True
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# Mocking MT5 copy_rates_range to return dummy data
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mock_copy_rates.return_value = [{
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"time": int(datetime(2023, 1, 1, 0, 0).timestamp()),
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"open": 1.1,
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"high": 1.2,
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"low": 1.0,
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"close": 1.15,
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"tick_volume": 1000,
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"spread": 10,
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"real_volume": 1000
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}]
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df = mt5_connector.get_historical_data("EURUSD", "H1", "2023-01-01", "2023-01-02")
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assert isinstance(df, pd.DataFrame)
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assert not df.empty
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assert "open" in df.columns
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# import os
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# import pytest
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# from unittest import mock
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# from datetime import datetime
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# import pandas as pd
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#
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# from src import mt5_connector
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#
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# # ----------------------------
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# # Test: load_mt5_credentials
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# # ----------------------------
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# def test_load_mt5_credentials(monkeypatch):
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# monkeypatch.setenv("MT5_LOGIN", "123456")
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# monkeypatch.setenv("MT5_PASSWORD", "testpass")
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# monkeypatch.setenv("MT5_SERVER", "TestServer")
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# monkeypatch.setenv("MT5_PATH", "/path/to/terminal.exe")
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#
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# creds = mt5_connector.load_mt5_credentials()
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#
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# assert creds["login"] == 123456
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# assert creds["password"] == "testpass"
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# assert creds["server"] == "TestServer"
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# assert creds["path"] == "/path/to/terminal.exe"
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#
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# # ----------------------------
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# # Test: initialize_mt5_connection
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# # ----------------------------
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# @mock.patch("src.mt5_connector.mt5.initialize")
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# @mock.patch("src.mt5_connector.load_mt5_credentials")
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# def test_initialize_mt5_connection_success(mock_creds, mock_mt5_init):
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# mock_creds.return_value = {
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# "login": 123456,
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# "password": "testpass",
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# "server": "TestServer",
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# "path": "/fake/path"
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# }
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# mock_mt5_init.return_value = True
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#
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# result = mt5_connector.initialize_mt5_connection()
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# assert result is True
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#
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# # ----------------------------
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# # Test: get_historical_data
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# # ----------------------------
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# @mock.patch("src.mt5_connector.mt5.copy_rates_range")
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# @mock.patch("src.mt5_connector.mt5.initialize")
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# @mock.patch("src.mt5_connector.mt5.shutdown")
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# @mock.patch("src.mt5_connector.load_mt5_credentials")
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# def test_get_historical_data_returns_dataframe(mock_creds, mock_shutdown, mock_init, mock_copy_rates):
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# mock_creds.return_value = {
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# "login": 123456,
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# "password": "testpass",
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# "server": "TestServer",
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# "path": "/fake/path"
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# }
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# mock_init.return_value = True
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#
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# # Mocking MT5 copy_rates_range to return dummy data
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# mock_copy_rates.return_value = [{
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# "time": int(datetime(2023, 1, 1, 0, 0).timestamp()),
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# "open": 1.1,
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# "high": 1.2,
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# "low": 1.0,
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# "close": 1.15,
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# "tick_volume": 1000,
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# "spread": 10,
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# "real_volume": 1000
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# }]
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#
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# df = mt5_connector.get_historical_data("EURUSD", "H1", "2023-01-01", "2023-01-02")
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#
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# assert isinstance(df, pd.DataFrame)
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# assert not df.empty
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# assert "open" in df.columns

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