symbol info tick HOW

symbol_info_tick — How it works¶

📌 Overview¶

This example shows how to use the low-level asynchronous method symbol_info_tick() to retrieve the current tick for a symbol and perform custom validation of market data before trading.

A tick is the latest available market information (Bid, Ask, time, etc.). By itself, it does not guarantee that the data is suitable for trading, so the example immediately demonstrates how this data is validated on the user side.

Method Signature¶

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

Key points:

The method is asynchronous and called with await
symbol — trading symbol name
The method returns an object with the last known tick
The method does not perform data correctness checks

🧩 Code Example — Tick validation before trading¶

async def validate_tick(account, symbol: str) -> bool:
    try:
        tick = await account.symbol_info_tick(symbol)

        if tick.bid <= 0 or tick.ask <= 0:
            print(f"Invalid prices: bid={tick.bid}, ask={tick.ask}")
            return False

        spread = tick.ask - tick.bid
        if spread <= 0:
            print(f"Invalid spread: {spread}")
            return False

        if tick.time > 0:
            current_time = datetime.now(timezone.utc).timestamp()
            age = current_time - tick.time

            if age > 10:
                print(f"Tick too old: {age} seconds")
                return False

        print("Tick validation passed")
        return True

    except Exception as e:
        print(f"Validation error: {e}")
        return False

This example demonstrates a typical safety check that is performed before sending trading operations.

🟢 Detailed Explanation¶

1️⃣ Retrieving Last Tick¶

tick = await account.symbol_info_tick(symbol)

At this step:

one asynchronous call is performed
the terminal returns the last known tick for the symbol
the tick object contains prices and time

The method returns data "as is", without interpretation.

2️⃣ Checking Bid and Ask Prices¶

if tick.bid <= 0 or tick.ask <= 0:

The presence of valid prices is checked:

values <= 0 indicate absence of valid quotes
such data cannot be used for trading

3️⃣ Checking Spread¶

spread = tick.ask - tick.bid
if spread <= 0:

Here, the logical correctness of prices is checked:

Ask must be greater than Bid
spread must be positive

4️⃣ Checking Tick Freshness¶

if tick.time > 0:
    current_time = datetime.now(timezone.utc).timestamp()
    age = current_time - tick.time

Tick time is compared with current time:

tick.time — Unix timestamp
tick age is calculated in seconds

if age > 10:

The example uses a custom rule:

a tick older than 10 seconds is considered unsuitable for trading

This is not an API rule, but a decision of the application code.

5️⃣ Successful Validation¶

print("Tick validation passed")
return True

If all checks pass, the tick is considered valid.

6️⃣ Error Handling¶

except Exception:
    return False

Any error when retrieving or checking the tick leads to trading prohibition.

The Role of Low-Level Method¶

Clear boundary of responsibility:

symbol_info_tick():

returns the last tick
does not check its correctness
does not assess data freshness

validate_tick():

checks prices
checks spread
checks tick age
makes the decision whether trading is allowed

Summary¶

This example illustrates the basic pattern of working with low-level API:

low-level data → custom validation → trading decision

The symbol_info_tick() method provides raw market data, while all logic for their interpretation and validation remains on the user side.