Add rate view resolution and downstream SDK helpers (#18)
* Add public helpers to resolve rate compatibility view names. Expose resolve_rate_view_name and resolve_rate_view_names in mt5cli.history so consumers can derive mt5cli-managed SQLite view names from stored rates metadata without reimplementing the naming rules. Co-authored-by: Cursor <cursoragent@cursor.com> * Add reusable export, tick-window, and margin helpers for downstream tools. Expose SQLite append/dedup export, recent tick retrieval, and minimum margin summary through the SDK and CLI so projects like mteor can depend on mt5cli instead of duplicating MT5 data plumbing. Co-authored-by: Cursor <cursoragent@cursor.com> * Bump version to 0.4.3. Co-authored-by: Cursor <cursoragent@cursor.com> * Address PR review feedback for rate view resolution and SDK helpers. Harden SQLite read-only connections, tighten view discovery, improve recent_ticks fetch efficiency, default SQLite export to append, and expand tests and docs. Co-authored-by: Cursor <cursoragent@cursor.com> * Fix read-only SQLite URI construction on Windows. Use Path.as_uri() so encoded file URIs work cross-platform with mode=ro. Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: Cursor <cursoragent@cursor.com>
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@@ -21,8 +21,10 @@ from mt5cli.utils import (
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TIMEFRAME_MAP,
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TIMEFRAME_TYPE,
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Dataset,
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IfExists,
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detect_format,
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export_dataframe,
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export_dataframe_to_sqlite,
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parse_datetime,
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parse_request,
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parse_tick_flags,
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@@ -130,6 +132,112 @@ class TestExportDataframe:
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export_dataframe(sample_df, tmp_path / "out.txt", "xml")
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class TestExportDataframeToSqlite:
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"""Tests for export_dataframe_to_sqlite."""
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def test_append_preserves_existing_rows(self, tmp_path: Path) -> None:
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"""Test append mode keeps prior rows in the SQLite table."""
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output = tmp_path / "append.db"
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first = pd.DataFrame({"id": [1], "value": ["a"]})
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second = pd.DataFrame({"id": [2], "value": ["b"]})
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export_dataframe_to_sqlite(first, output, "items", if_exists=IfExists.REPLACE)
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export_dataframe_to_sqlite(second, output, "items", if_exists=IfExists.APPEND)
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with sqlite3.connect(output) as conn:
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result = pd.read_sql( # type: ignore[reportUnknownMemberType]
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"SELECT id, value FROM items ORDER BY id",
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conn,
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)
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pd.testing.assert_frame_equal(
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result,
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pd.DataFrame({"id": [1, 2], "value": ["a", "b"]}),
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)
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def test_deduplicate_keeps_latest_row(self, tmp_path: Path) -> None:
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"""Test deduplication keeps the latest ROWID for key columns."""
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output = tmp_path / "dedup.db"
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first = pd.DataFrame({
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"symbol": ["EURUSD", "EURUSD"],
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"time": ["2024-01-01", "2024-01-01"],
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"bid": [1.0, 1.1],
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})
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second = pd.DataFrame({
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"symbol": ["EURUSD"],
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"time": ["2024-01-01"],
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"bid": [1.2],
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})
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export_dataframe_to_sqlite(
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first,
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output,
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"ticks",
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if_exists=IfExists.REPLACE,
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deduplicate_on=("symbol", "time"),
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)
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export_dataframe_to_sqlite(
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second,
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output,
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"ticks",
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if_exists=IfExists.APPEND,
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deduplicate_on=("symbol", "time"),
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)
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with sqlite3.connect(output) as conn:
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result = pd.read_sql( # type: ignore[reportUnknownMemberType]
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"SELECT symbol, time, bid FROM ticks",
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conn,
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)
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pd.testing.assert_frame_equal(
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result.reset_index(drop=True),
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pd.DataFrame({
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"symbol": ["EURUSD"],
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"time": ["2024-01-01"],
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"bid": [1.2],
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}),
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)
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def test_default_if_exists_appends_without_dropping_rows(
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self,
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tmp_path: Path,
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) -> None:
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"""Test the default append mode keeps prior rows."""
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output = tmp_path / "default-append.db"
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first = pd.DataFrame({"id": [1], "value": ["a"]})
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second = pd.DataFrame({"id": [2], "value": ["b"]})
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export_dataframe_to_sqlite(first, output, "items")
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export_dataframe_to_sqlite(second, output, "items")
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with sqlite3.connect(output) as conn:
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result = pd.read_sql( # type: ignore[reportUnknownMemberType]
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"SELECT id, value FROM items ORDER BY id",
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conn,
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)
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pd.testing.assert_frame_equal(
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result,
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pd.DataFrame({"id": [1, 2], "value": ["a", "b"]}),
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)
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def test_writes_index_with_label(self, tmp_path: Path) -> None:
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"""Test optional index export with a custom label."""
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output = tmp_path / "index.db"
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frame = pd.DataFrame(
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{"value": [1.0]}, index=pd.Index(["EURUSD"], name="symbol")
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)
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export_dataframe_to_sqlite(
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frame,
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output,
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"margins",
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if_exists=IfExists.REPLACE,
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index=True,
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index_label="symbol",
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)
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with sqlite3.connect(output) as conn:
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result = pd.read_sql( # type: ignore[reportUnknownMemberType]
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"SELECT symbol, value FROM margins",
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conn,
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)
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pd.testing.assert_frame_equal(
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result,
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pd.DataFrame({"symbol": ["EURUSD"], "value": [1.0]}),
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)
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# ---------------------------------------------------------------------------
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# Parse helpers
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# ---------------------------------------------------------------------------
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