mt5cli/README.md

mt5cli

Command-line tool for exporting MetaTrader 5 data to CSV, JSON, Parquet, and SQLite3.

Built on top of pdmt5, a pandas-based data handler for MetaTrader 5.

Features

• 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

pip install -U mt5cli MetaTrader5

Usage

# 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 symbols to SQLite3 with custom table name
mt5cli -o data.db --table symbols symbols --group "*USD*"

# Export with connection credentials
mt5cli --login 12345 --password mypass --server MyBroker-Demo \
  -o positions.csv positions

Run as a Python module:

python -m mt5cli -o account.csv account-info

Commands

Command	Description
rates-from	Export rates from a start date
rates-from-pos	Export rates from a start position
latest-rates	Export latest rates from a start position
rates-range	Export rates for a date range
ticks-from	Export ticks from a start date
ticks-range	Export ticks for a date range
ticks-recent	Export ticks from a recent trailing window
account-info	Export account information
terminal-info	Export terminal information
version	Export MetaTrader 5 version information
last-error	Export the last error information
symbols	Export symbol list
symbol-info	Export symbol details
symbol-info-tick	Export the last tick for a symbol
minimum-margins	Export minimum-volume buy and sell margin requirements
market-book	Export market depth (order book)
orders	Export active orders
positions	Export open positions
history-orders	Export historical orders
history-deals	Export historical deals
recent-history-deals	Export historical deals from a recent trailing window
mt5-summary	Export terminal/account status summary
order-check	Check funds sufficiency for a trade request
order-send	Send a trade request to the trade server (--yes required)
collect-history	Bundle rates, ticks, history-orders, and history-deals for one or more symbols into a single SQLite database

Use order-check to validate a request payload before running order-send --yes.

collect-history

Collect several historical datasets per symbol into one SQLite database in a single MT5 session. Pick datasets with repeatable --dataset (default: all four), choose conflict behavior with --if-exists append|replace|fail (default: fail), and optionally derive cash_events / positions_reconstructed views from history_deals via --with-views.

mt5cli -o history.db collect-history \
  --symbol EURUSD --symbol GBPUSD \
  --date-from 2024-01-01 --date-to 2024-02-01 \
  --dataset rates --dataset history-deals \
  --timeframe M1 --flags ALL --if-exists append --with-views

History orders and deals are fetched per symbol and concatenated, so the symbol filter is applied consistently across all datasets. The cash_events view is derived from symbol-filtered history_deals, so account-level cash events with empty or non-matching symbols may be excluded. The rates table records the requested timeframe so appended runs at different timeframes remain distinguishable. The positions_reconstructed view aggregates trade deals by position_id, excludes positions without closing-side entries, and uses volume-weighted open/close prices; reversal deals (DEAL_ENTRY_INOUT) are reported via volume_reversal / reversal_count columns.

Incremental history SDK

For automated pipelines, use the importable incremental API instead of re-fetching fixed date ranges:

from pdmt5 import Mt5Config, Mt5DataClient
from mt5cli import Dataset, update_history, update_history_with_config

# Reuse an already-connected pdmt5 client (does not open/close MT5)
client = Mt5DataClient(config=Mt5Config(login=12345))
client.initialize_and_login_mt5()
try:
    update_history(
        client=client,
        output="history.db",
        symbols=["EURUSD", "GBPUSD"],
        datasets={Dataset.rates, Dataset.history_deals},
        timeframes=["M1", "H1"],  # default: all fixed MT5 timeframes
        lookback_hours=24,
        create_rate_views=True,
        with_views=True,
        include_account_events=True,
    )
finally:
    client.shutdown()

# Standalone wrapper that opens and closes MT5 for you
update_history_with_config(
    output="history.db",
    symbols=["EURUSD"],
    config=Mt5Config(login=12345),
)

• collect-history: explicit date-range export into SQLite.
• update_history: incremental append based on existing SQLite MAX(time) per symbol (and timeframe for rates); account-level deals use a separate cursor when include_account_events=True.
• rates table: normalized storage with symbol and timeframe columns.
• Rate compatibility views: mt5cli manages all rate_* views. Naming is rate_<symbol>__<timeframe> when a symbol has one timeframe, otherwise rate_<symbol>__<granularity>_<timeframe> (for example rate_EURUSD__M1_1). Stale rate_* views are dropped and recreated when rates change for offline tools such as mteor optimize.
• Rate view resolution: use resolve_rate_view_name() / resolve_rate_view_names() to map symbols and granularities to existing SQLite compatibility views without creating databases. Both accept None (or a missing path) and return deterministic default names unless require_existing=True.
• Rate view loading: use load_rate_data() / load_rate_data_from_connection() to load a SQLite rate table or view into a DatetimeIndex DataFrame.
• Multi-series rate loading: use build_rate_targets() to build neutral RateTarget(symbol, timeframe) pairs, resolve_rate_tables() to map them to table/view names (pass require_existing=True for strict resolution), and load_rate_series_from_sqlite() to load them into a mapping keyed by (symbol, integer timeframe). The loader requires existing managed views unless explicit_tables is supplied, and rejects duplicate (symbol, timeframe) targets.
• Multi-account latest rates: use collect_latest_rates_for_accounts() with AccountSpec to read the latest bars for several account groups, merged into a (symbol, integer timeframe) mapping. For long-running pollers, collect_latest_rates_for_accounts_with_retries() adds bounded exponential backoff that retries only pdmt5.Mt5TradingError / pdmt5.Mt5RuntimeError and re-raises once retry_count is exhausted.
• Latest closed bars: use collect_latest_closed_rates_for_accounts() when downstream logic must exclude the still-forming current bar. It fetches count + 1 bars at start_pos=0, drops the last row with drop_forming_rate_bar(), and validates each series is non-empty. collect_latest_closed_rates_by_granularity() returns the same data keyed by (symbol, granularity_name) such as ("EURUSD", "M1").

from mt5cli import AccountSpec, collect_latest_closed_rates_by_granularity

rates = collect_latest_closed_rates_by_granularity(
    [AccountSpec(symbols=["EURUSD", "GBPUSD"], login=12345)],
    ["M1", "H1"],
    count=500,
    retry_count=3,
)
eurusd_m1 = rates["EURUSD", "M1"]  # closed bars only

• Credential resolution: use resolve_account_spec() / resolve_account_specs() to merge explicit override values over AccountSpec fields and expand ${ENV_VAR} placeholders (via substitute_env_placeholders()), raising ValueError for missing variables. This keeps secrets out of plan/config files without coupling to any strategy code.
• Throttled history updates: use ThrottledHistoryUpdater to wrap update_history() with a minimum interval_seconds between successful runs (monotonic clock). Call should_update() / update(client, symbols) from an application loop; errors propagate by default, or pass suppress_errors=True to swallow recoverable Mt5*Error/sqlite3.Error and let the caller decide logging.
• Granularity-keyed rate loading: load_rate_series_by_granularity() builds targets with build_rate_targets(), loads them with load_rate_series_from_sqlite(), and returns a mapping keyed by (symbol | None, granularity_name) such as ("EURUSD", "M1") to reduce downstream boilerplate.
• MT5 session helper: use the mt5_session() context manager to attach to (or, when Mt5Config.path is set, launch) an MT5 terminal, log in, and yield a connected Mt5CliClient that shuts down on exit.
• SQLite export helpers: use export_dataframe_to_sqlite() for append mode, optional index export, and post-write deduplication by key columns.
• Recent ticks and margins: recent_ticks() and minimum_margins() SDK helpers (and matching CLI commands) cover common downstream read-only queries.

Requirements

• Python 3.11+
• Windows OS (MetaTrader 5 requirement)
• MetaTrader 5 platform installed

Development

git clone https://github.com/dceoy/mt5cli.git
cd mt5cli
uv sync

License

MIT