Sigma Quantiphi builds algorithmic trading solutions for the cryptocurrency markets — both for proprietary use and third-party clients.
In crypto trading, real-time data is more than a luxury; it’s a necessity. Whether you’re developing an execution bot or refining a quant strategy, the ability to monitor liquidation flows live can reveal market stress, price momentum, and opportunities for well-timed entries or exits.
Liquidations aren’t just a side effect of leverage; they’re often the very force that drives major moves. In this tutorial, you’ll learn how to stream live liquidation events from Binance using just 23 lines of Python. With CCXT and Crypto-Pandas, you’ll get structured, pandas-ready data for integration into your models, dashboards, or trading systems.
A liquidation happens when a leveraged position becomes unsustainable. The exchange steps in and force-closes the position to prevent further loss:
Either way, it creates forced market activity:
These events often trigger chain reactions and cascading liquidations, which can amplify price direction. Traders who can see this in real time can:
To stream this data in real-time, we’re using:
A premium WebSocket-based extension of the CCXT library, designed for live exchange data across major crypto platforms. It supports streaming depth, trades, and liquidations, which are essential for execution-level strategies
A thin Python wrapper that simplifies CCXT’s data into clean pandas DataFrames, ready for immediate analysis. It handles formatting and exchange quirks so you don’t have to.
Together, they give you a streamlined way to watch crypto liquidations unfold live, all from the command line.
We’ll walk you through a script that:
This script will form the foundation for building:
import asyncio
import ccxt.pro
from crypto_pandas.ccxt.async_ccxt_pandas_exchange import AsyncCCXTPandasExchange
You’re importing:
This creates a Binance WebSocket connection and wraps it for pandas-style output.
async def main():
try:
while True:
df = await ex.watchLiquidationsForSymbols()
print(df)
This continuously watches for liquidation events and prints the latest DataFrame every time a new one arrives.
watchLiquidationsForSymbols() automatically subscribes to all supported symbols and delivers updates in real time.
except KeyboardInterrupt:
await ex.close()
print(“Stopped by user.”)
except Exception as e:
await ex.close()
print(f”❌ Error: {e}”)
This ensures that if the script is interrupted or encounters an error, the connection is closed cleanly.
if __name__ == “__main__”:
asyncio.run(main())
This launches the async loop and starts streaming liquidation data as soon as the script runs.
An increase in long liquidations during a sell-off? It’s likely a sign of panic, not just a trend. Short liquidations during a breakout? You might be witnessing a short squeeze.
Track liquidation frequency per symbol over a rolling window and use it as a sentiment signal. For instance:
If you’re running automated strategies, you can use live liquidation data to:
This kind of data isn’t just useful for discretionary traders. Quant desks can:
The market doesn’t always move because of buying and selling from intent; it moves because of liquidity imbalances and forced actions. Liquidations are among the clearest signals of those breakdowns. They don’t just reflect what’s happening; they cause what happens next. With this simple Python setup, you gain access to a high-value, real-time stream of market stress. You’re now positioned to:
You’re not just collecting data anymore, you’re watching the pressure build.
Build smarter. Monitor deeper. Execute with confidence.
Sigma Quantiphi builds algorithmic trading solutions for the cryptocurrency markets — both for proprietary use and third-party clients.
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