Pythoncanbeoptimizedformemory-boundoperationsbyreducingoverheadthroughgenerators,efficientdatastructures,andmanagingobjectlifetimes.First,usegeneratorsinsteadofliststoprocesslargedatasetsoneitematatime,avoidingloadingeverythingintomemory.Second,choos

Install pyodbc: Use the pipinstallpyodbc command to install the library; 2. Connect SQLServer: Use the connection string containing DRIVER, SERVER, DATABASE, UID/PWD or Trusted_Connection through the pyodbc.connect() method, and support SQL authentication or Windows authentication respectively; 3. Check the installed driver: Run pyodbc.drivers() and filter the driver name containing 'SQLServer' to ensure that the correct driver name is used such as 'ODBCDriver17 for SQLServer'; 4. Key parameters of the connection string

Introduction to Statistical Arbitrage Statistical Arbitrage is a trading method that captures price mismatch in the financial market based on mathematical models. Its core philosophy stems from mean regression, that is, asset prices may deviate from long-term trends in the short term, but will eventually return to their historical average. Traders use statistical methods to analyze the correlation between assets and look for portfolios that usually change synchronously. When the price relationship of these assets is abnormally deviated, arbitrage opportunities arise. In the cryptocurrency market, statistical arbitrage is particularly prevalent, mainly due to the inefficiency and drastic fluctuations of the market itself. Unlike traditional financial markets, cryptocurrencies operate around the clock and their prices are highly susceptible to breaking news, social media sentiment and technology upgrades. This constant price fluctuation frequently creates pricing bias and provides arbitrageurs with

iter() is used to obtain the iterator object, and next() is used to obtain the next element; 1. Use iterator() to convert iterable objects such as lists into iterators; 2. Call next() to obtain elements one by one, and trigger StopIteration exception when the elements are exhausted; 3. Use next(iterator, default) to avoid exceptions; 4. Custom iterators need to implement the __iter__() and __next__() methods to control iteration logic; using default values is a common way to safe traversal, and the entire mechanism is concise and practical.

Use psycopg2.pool.SimpleConnectionPool to effectively manage database connections and avoid the performance overhead caused by frequent connection creation and destruction. 1. When creating a connection pool, specify the minimum and maximum number of connections and database connection parameters to ensure that the connection pool is initialized successfully; 2. Get the connection through getconn(), and use putconn() to return the connection to the pool after executing the database operation. Constantly call conn.close() is prohibited; 3. SimpleConnectionPool is thread-safe and is suitable for multi-threaded environments; 4. It is recommended to implement a context manager in combination with context manager to ensure that the connection can be returned correctly when exceptions are noted;

TosecureMySQLeffectively,useobject-levelprivilegestolimituseraccessbasedontheirspecificneeds.Beginbyunderstandingthatobject-levelprivilegesapplytodatabases,tables,orcolumns,offeringfinercontrolthanglobalprivileges.Next,applytheprincipleofleastprivile

MySQL replication filtering can be configured in the main library or slave library. The main library controls binlog generation through binlog-do-db or binlog-ignore-db, which is suitable for reducing log volume; the data application is controlled by replicate-do-db, replicate-ignore-db, replicate-do-table, replicate-ignore-table and wildcard rules replicate-wild-do-table and replicate-wild-ignore-table. It is more flexible and conducive to data recovery. When configuring, you need to pay attention to the order of rules, cross-store statement behavior,

collections.Counter is used to count element frequency, 1. It can count list elements such as Counter(['apple','banana','apple']) and output Counter({'apple':3,'banana':2,'orange':1}); 2. It can count string characters such as Counter("helloworld") and output Counter({'l':3,'o':2,'h':1,'e':1,'w':1,'r':1,'d':1}); 3. Use most_common(n) to obtain the first n most common elements