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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@@ -2,16 +2,18 @@
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from __future__ import annotations
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import importlib
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import json
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import sqlite3
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from datetime import UTC, datetime
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from enum import StrEnum
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from pathlib import Path
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from typing import TYPE_CHECKING, Any, TypeGuard, cast
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from typing import TYPE_CHECKING, Any, TypeGuard
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import click
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if TYPE_CHECKING:
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from collections.abc import Sequence
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import pandas as pd
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# ---------------------------------------------------------------------------
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@@ -260,6 +262,50 @@ def detect_format(
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raise ValueError(msg)
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def export_dataframe_to_sqlite(
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df: pd.DataFrame,
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output_path: Path,
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table_name: str = "data",
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*,
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if_exists: IfExists = IfExists.APPEND,
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index: bool = False,
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index_label: str | None = None,
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deduplicate_on: Sequence[str] | None = None,
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) -> None:
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"""Write a DataFrame to SQLite with configurable append and deduplication.
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Args:
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df: DataFrame to export.
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output_path: SQLite database path.
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table_name: Target table name.
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if_exists: Conflict behavior when the table already exists.
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index: Whether to write the DataFrame index as a column.
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index_label: Column name for the index when ``index=True``.
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deduplicate_on: Optional key columns to deduplicate after writing,
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keeping the latest ``ROWID`` per key group. Deduplication scans the
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full table, so repeated appends cost O(table size); index the key
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columns when appending frequently.
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"""
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with sqlite3.connect(output_path) as conn:
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df.to_sql( # type: ignore[reportUnknownMemberType]
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table_name,
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conn,
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if_exists=if_exists.value,
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index=index,
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index_label=index_label,
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)
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if deduplicate_on:
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from .history import drop_duplicates_in_table # noqa: PLC0415
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drop_duplicates_in_table(
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conn.cursor(),
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table_name,
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list(deduplicate_on),
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keep="last",
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)
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conn.commit()
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def export_dataframe(
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df: pd.DataFrame,
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output_path: Path,
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@@ -289,14 +335,13 @@ def export_dataframe(
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elif output_format == "parquet":
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df.to_parquet(output_path, index=False)
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elif output_format == "sqlite3":
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sqlite3 = cast("Any", importlib.import_module("sqlite3"))
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with sqlite3.connect(output_path) as conn:
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df.to_sql( # type: ignore[reportUnknownMemberType]
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table_name,
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conn,
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if_exists="replace",
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index=False,
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)
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export_dataframe_to_sqlite(
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df,
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output_path,
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table_name,
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if_exists=IfExists.REPLACE,
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index=False,
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)
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else:
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msg = f"Unsupported output format: {output_format}"
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raise ValueError(msg)
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