The digital asset market attracts global attention with its high volatility. In this environment, how to steadily capture returns has become the goal pursued by countless participants. Quantitative trading, with its dependence on data and algorithm-driven characteristics, is becoming a powerful tool to deal with market challenges. Especially in 2025, this time node full of infinite possibilities is combined with the powerful programming language Python to build an automated "brick-moving" strategy, that is, to use the tiny price spreads between different trading platforms for arbitrage, which is considered a potential way to achieve efficient and stable profits.

In theory, capturing a daily return of 5% through millisecond trading speed and precise risk control is not an out of reach, although this requires top technology, deep market understanding and continuous optimization and iteration. This article will dive into the core elements of how to build such automation strategies using Python.

2025 Global Quantitative Trading Main Platform Official Website Address

• Binance Binance: ()
• Ouyi OKX: ()

The core concept of automatic brick-moving strategy

1. The core lies in discovering and utilizing instantaneous price differences between different digital asset trading platforms.
2. The strategy relies on the algorithm's high-speed execution ability to complete the buying and selling operation before the price difference disappears.
3. Successful automated brick-moving requires extremely low transaction latency and high reliability system architecture .
4. This is not simply buying low and selling high, but an accurate grasp of the market microstructure and liquidity .

Advantages of Python in building quantitative strategies

1. The Python ecosystem provides a rich quantitative trading library , such as Pandas, NumPy for data processing, CCXT and other APIs for connecting various trading platforms.
2. Its syntax is concise and easy to read , with high development efficiency , and can quickly convert transaction ideas into executable code.
3. Strong community support means it is easier to find solutions and resources when encountering problems.
4. Python's flexibility makes it ideal for iterating quickly and testing different policy logics.

Key Steps to Building an Efficient Automatic Policy

1. First, we need to stably obtain real-time market data (such as price and depth) of multiple trading platforms .
2. Design core arbitrage logic to accurately calculate whether the price difference between different platforms covers transaction costs and is profitable.
3. Develop an automated transaction execution module to quickly place orders, cancel orders and manage orders through API calls.
4. Carry out sufficient historical data backtesting and real-disk simulation tests , and continuously optimize strategy parameters.
5. Establish a complete risk control mechanism , including fund management, single transaction limits, abnormal situation handling, etc.
6. Deploy to low-latency servers and implement real-time monitoring and early warning systems .

Possibility and challenges of achieving 5% per day

1. Earning 5% per day is a challenging goal , and it means that strategies must continue to capture a large number of small arbitrage opportunities.
2. Market competition is becoming increasingly fierce , and efficient arbitrage opportunities are often fleeting .
3. Transaction fees, slippages and sudden market events may significantly erode strategic returns and even lead to losses.
4. Strong technical support (low-latency infrastructure, stable API connections) and continuous maintenance optimization are required.
5. Achieving and maintaining such a high daily chemical yield requires extremely high efficiency and robustness of the strategy and always respond to market changes .

The above is the detailed content of 2025 quantitative trading skills: Python's automatic brick-moving strategy, making a daily profit of 5% as stable as a dog!. For more information, please follow other related articles on the PHP Chinese website!