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{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"mt5cli \u00b6 Generic MT5 data and execution infrastructure for Python applications. Overview \u00b6 mt5cli provides a stable MT5Client Python API, standardized dataset schemas, storage helpers, and a CLI for exporting MetaTrader 5 data. It is built on top of pdmt5 , a pandas-based data handler for MetaTrader 5. Architecture \u00b6 pdmt5 \u2014 canonical MT5 client, DataFrame/trading primitives, and MT5 constant parsing ( TIMEFRAME_* , COPY_TICKS_* , order types). mt5cli \u2014 public MT5Client API, schema contracts, storage helpers, CLI commands, and SQLite history collection built on pdmt5. mt5api \u2014 sibling HTTP adapter for remote MT5 access; not a dependency of mt5cli. Features \u00b6 Multi-format export : CSV, JSON, Parquet, and SQLite3 output formats Auto-detection : Format detection from file extensions Comprehensive data access : Rates, ticks, account info, symbols, orders, positions, and trading history Flexible timeframes : Named timeframes (M1, H1, D1, etc.) and numeric values Connection management : Optional credentials, server, and timeout configuration SQLite rate loading : Load mt5cli-managed rate tables/views for offline workflows Installation \u00b6 pip install mt5cli Python API for downstream packages \u00b6 Import MT5Client for generic MT5 data access, schema normalization, and optional order primitives. Mt5CliClient remains available as a backward-compatible alias. from datetime import UTC , datetime from pathlib import Path from mt5cli import ( DataKind , Dataset , MT5Client , build_config , collect_history , export_dataframe , load_rate_data , minimum_margins , mt5_session , normalize_dataframe , recent_ticks , resolve_rate_view_name , ) # Persistent session for multiple calls with mt5_session ( build_config ( login = 12345 , server = \"Broker-Demo\" )) as client : rates = client . copy_rates_range ( \"EURUSD\" , timeframe = \"H1\" , date_from = \"2024-01-01\" , date_to = \"2024-02-01\" , ) positions = client . positions () check = client . order_check ({ \"action\" : 1 , \"symbol\" : \"EURUSD\" , \"volume\" : 0.1 }) # Normalize MT5 frames to the public schema contract before storage closed_rates = normalize_dataframe ( rates , DataKind . rates , symbol = \"EURUSD\" , timeframe = \"H1\" ) export_dataframe ( closed_rates , Path ( \"rates.csv\" ), \"csv\" ) # Offline rate loading from mt5cli-managed SQLite history view = resolve_rate_view_name ( Path ( \"history.db\" ), \"EURUSD\" , \"M1\" , require_existing = True ) offline_rates = load_rate_data ( Path ( \"history.db\" ), view , count = 1000 ) # One-off helpers still work without instantiating a client ticks = recent_ticks ( \"EURUSD\" , seconds = 300 ) margins = minimum_margins ( \"EURUSD\" ) collect_history ( Path ( \"history.db\" ), symbols = [ \"EURUSD\" , \"GBPUSD\" ], date_from = datetime ( 2024 , 1 , 1 , tzinfo = UTC ), date_to = datetime ( 2024 , 2 , 1 , tzinfo = UTC ), datasets = { Dataset . rates , Dataset . history_deals }, ) Schema contracts live in mt5cli.schemas ( DataKind , validate_schema , normalize_dataframe ). Storage helpers are re-exported from mt5cli.storage and the package root. MT5Client.order_send() is a live execution primitive: it can place real trades on the connected account. mt5cli does not implement strategy logic, signal generation, backtesting, or optimization \u2014 downstream applications must gate live execution explicitly (the CLI requires --yes for order-send ). MT5Client.mt5_summary() returns structured nested Python values. Use MT5Client.mt5_summary_as_df() when you need a one-row DataFrame for export. Quick Start \u00b6 # Export account information to CSV mt5cli -o account.csv account-info # Export EURUSD M1 rates to Parquet mt5cli -o rates.parquet rates-from --symbol EURUSD --timeframe M1 \\ --date-from 2024 -01-01 --count 1000 # Export ticks to JSON mt5cli -o ticks.json ticks-from --symbol EURUSD \\ --date-from 2024 -01-01 --count 500 --flags ALL # Export symbo
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