Daichi Narushima
b2bb2ad0a0
* 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>
121 lines
3.5 KiB
Markdown
121 lines
3.5 KiB
Markdown
# API Reference
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This section contains the complete API documentation for mt5cli.
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## Modules
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The mt5cli package consists of the following modules:
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### [CLI](cli.md)
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Command-line interface module providing typer-based commands for exporting MetaTrader 5 data to CSV, JSON, Parquet, and SQLite3 formats.
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### [Utils](utils.md)
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Utility module providing constants, enums, Click parameter types, and helper functions for parsing and exporting data.
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### [SDK](sdk.md)
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Programmatic SDK for read-only MetaTrader 5 data collection. Returns pandas DataFrames and provides `collect_history` for SQLite bulk collection.
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### [History Collection (SQLite)](history.md)
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SQLite storage helpers for the `collect-history` command schema, incremental updates, deduplication, indexes, and optional views.
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## Architecture Overview
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The package follows a simple architecture built on top of pdmt5:
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1. **CLI Layer** (`cli.py`): Typer application with subcommands that delegate to the SDK and export results.
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2. **SDK Layer** (`sdk.py`): Read-only data access functions, `Mt5CliClient`, and `collect_history` orchestration.
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3. **Utils Layer** (`utils.py`): Constants, enums, custom Click parameter types, parsing helpers, and format detection/export utilities.
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4. **Data Layer** (via `pdmt5`): Uses `Mt5DataClient` and `Mt5Config` from the pdmt5 package for all MetaTrader 5 data access.
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## Usage Guidelines
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All modules follow these conventions:
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- **Type Safety**: All functions include comprehensive type hints
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- **Error Handling**: User-friendly error messages via typer
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- **Documentation**: Google-style docstrings with examples
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- **Validation**: Custom Click parameter types for input validation
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## Quick Start
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```bash
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# Export account information to CSV
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mt5cli -o account.csv account-info
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# Export EURUSD H1 rates to Parquet
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mt5cli -o rates.parquet rates-from --symbol EURUSD --timeframe H1 \
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--date-from 2024-01-01 --count 1000
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# Export ticks to JSON
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mt5cli -o ticks.json ticks-from --symbol EURUSD \
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--date-from 2024-01-01 --count 500 --flags ALL
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# Export to SQLite3 with custom table name
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mt5cli -o data.db --table symbols symbols --group "*USD*"
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```
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## Python API
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```python
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from datetime import UTC, datetime
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from pathlib import Path
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from mt5cli import (
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Dataset,
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IfExists,
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Mt5CliClient,
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collect_history,
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copy_rates_range,
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detect_format,
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export_dataframe,
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export_dataframe_to_sqlite,
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minimum_margins,
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recent_ticks,
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)
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from mt5cli.history import resolve_rate_view_name
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# Fetch rates programmatically
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rates = copy_rates_range(
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"EURUSD",
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timeframe="H1",
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date_from="2024-01-01",
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date_to="2024-02-01",
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)
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# Detect output format from file extension
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fmt = detect_format(Path("output.parquet")) # Returns "parquet"
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# Export a DataFrame
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export_dataframe(rates, Path("output.csv"), "csv")
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# Append to SQLite with deduplication
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export_dataframe_to_sqlite(
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rates,
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Path("history.db"),
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"rates",
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if_exists=IfExists.APPEND,
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deduplicate_on=("symbol", "timeframe", "time"),
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)
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# Resolve rate compatibility views and fetch recent ticks
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view = resolve_rate_view_name(Path("history.db"), "EURUSD", "M1")
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ticks = recent_ticks("EURUSD", seconds=300)
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margins = minimum_margins("EURUSD")
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# Collect history into SQLite
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collect_history(
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Path("history.db"),
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symbols=["EURUSD"],
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date_from=datetime(2024, 1, 1, tzinfo=UTC),
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date_to=datetime(2024, 2, 1, tzinfo=UTC),
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)
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```
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## Examples
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See individual module pages for detailed usage examples and code samples.
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