market book get HOW

market_book_get — How it works

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📌 Overview

This example shows how to use the low-level asynchronous method market_book_get() to get the current Depth of Market (DOM) snapshot and perform custom liquidity calculations by BUY and SELL sides.

In this scenario, DOM is used as a source of instantaneous market state, while all analytics and interpretation are performed on the client side.

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Method Signature

async def market_book_get(
    symbol: str,
    deadline: Optional[datetime] = None,
    cancellation_event: Optional[asyncio.Event] = None,
):

Key points:

• the method is asynchronous and called with await
• returns the current DOM snapshot, not a stream of updates
• does not aggregate or analyze data
• assumes that DOM subscription is already open

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🧩 Code Example — Calculating Total Liquidity

import asyncio
from MetaRpcMT5 import MT5Account

async def calculate_liquidity():
    account = MT5Account(
        user=12345,
        password="password",
        grpc_server="mt5.mrpc.pro:443"
    )

    await account.connect_by_server_name(
        server_name="YourBroker-Demo",
        base_chart_symbol="EURUSD"
    )

    try:
        symbol = "EURUSD"

        # 1. Open DOM
        await account.market_book_add(symbol)

        # 2. Get current DOM snapshot
        dom_data = await account.market_book_get(symbol)

        # 3. Calculate total volume by side
        sell_volume = sum(
            e.volume_real for e in dom_data.mql_book_infos if e.type in [0, 2]
        )
        buy_volume = sum(
            e.volume_real for e in dom_data.mql_book_infos if e.type in [1, 3]
        )
        total_volume = sell_volume + buy_volume

        print(f"[LIQUIDITY] {symbol}")
        print(f"  Total DOM levels: {len(dom_data.mql_book_infos)}")
        print(f"  BUY side volume: {buy_volume:.2f} lots")
        print(f"  SELL side volume: {sell_volume:.2f} lots")
        print(f"  Total volume: {total_volume:.2f} lots")

        if total_volume > 0:
            buy_percent = (buy_volume / total_volume) * 100
            sell_percent = (sell_volume / total_volume) * 100

            print(f"\n  BUY: {buy_percent:.1f}% | SELL: {sell_percent:.1f}%")

            if buy_percent > 60:
                print("  [SIGNAL] Strong BUY side liquidity")
            elif sell_percent > 60:
                print("  [SIGNAL] Strong SELL side liquidity")
            else:
                print("  [SIGNAL] Balanced liquidity")

    finally:
        await account.channel.close()

asyncio.run(calculate_liquidity())

This example demonstrates a complete user-level liquidity calculation based on DOM data.

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🟢 Detailed Explanation

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1️⃣ Opening DOM

await account.market_book_add(symbol)

At this step:

• the terminal starts maintaining the order book for the symbol
• without this call, market_book_get may return empty data

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2️⃣ Getting DOM Snapshot

dom_data = await account.market_book_get(symbol)

The method returns:

• a list of order levels mql_book_infos
• each element represents one order book level

This is an instantaneous snapshot, not a stream.

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3️⃣ Separating Orders by Sides

e.type in [0, 2]  # SELL
e.type in [1, 3]  # BUY

Order type determines the market side:

• SELL — liquidity offers
• BUY — liquidity demand

Type interpretation is performed by the user.

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4️⃣ Volume Aggregation

sell_volume = sum(...)
buy_volume = sum(...)

At this step:

• volumes are summed by sides
• the API does not perform aggregation

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5️⃣ User-Level Analytics

if buy_percent > 60:
    ...
elif sell_percent > 60:
    ...

Threshold values:

• are chosen by the user
• do not have fixed values in the API

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The Role of Low-Level Method

Clear boundary of responsibility:

market_book_get():

• returns the current order book state
• does not store history
• does not calculate liquidity
• does not generate signals

User code:

• aggregates volumes
• calculates percentages
• interprets the balance of supply and demand

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Summary

This example illustrates the DOM analysis pattern:

get order book snapshot → aggregate levels → evaluate liquidity → make conclusions

The market_book_get() method provides raw DOM data, while all analytics and signals are fully formed on the user side.