The real use of battle royale in the dual currency system has not yet happened. Conclusion In August 2023, the MakerDAO ecological lending protocol Spark gave an annualized return of $DAI8%. Then Sun Chi entered in batches, investing a total of 230,000 $stETH, accounting for more than 15% of Spark's deposits, forcing MakerDAO to make an emergency proposal to lower the interest rate to 5%. MakerDAO's original intention was to "subsidize" the usage rate of $DAI, almost becoming Justin Sun's Solo Yield. July 2025, Ethe

What can truly make money stably is countercyclical traders with anti-human characteristics. 1. They identify whales in the market FOMO by fighting emotional kidnapping, and capture wrongly killed assets when panic sell-offs; 2. Establish mechanized trading discipline and strictly implement stop-profit and stop-loss rules to fight greed and fear; 3. Use cognitive arbitrage thinking to discover institutional trends and trend opportunities in advance through on-chain data and code updates and other underlying information, and ultimately solidify emotional isolation, data decision-making and countercyclical operations into trading instincts, thereby continuing to make profits in the encrypted market with amplified human nature.

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

pandas.melt() is used to convert wide format data into long format. The answer is to define new column names by specifying id_vars retain the identification column, value_vars select the column to be melted, var_name and value_name, 1.id_vars='Name' means that the Name column remains unchanged, 2.value_vars=['Math','English','Science'] specifies the column to be melted, 3.var_name='Subject' sets the new column name of the original column name, 4.value_name='Score' sets the new column name of the original value, and finally generates three columns including Name, Subject and Score.

First, define a ContactForm form containing name, mailbox and message fields; 2. In the view, the form submission is processed by judging the POST request, and after verification is passed, cleaned_data is obtained and the response is returned, otherwise the empty form will be rendered; 3. In the template, use {{form.as_p}} to render the field and add {%csrf_token%} to prevent CSRF attacks; 4. Configure URL routing to point /contact/ to the contact_view view; use ModelForm to directly associate the model to achieve data storage. DjangoForms implements integrated processing of data verification, HTML rendering and error prompts, which is suitable for rapid development of safe form functions.

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

Biopython is an important Python library for processing biological data in bioinformatics, which provides rich functions to improve development efficiency. The installation method is simple, you can complete the installation using pipinstallbiopython. After importing the Bio module, you can quickly parse common sequence formats such as FASTA files. Seq objects support manipulation of DNA, RNA and protein sequences such as inversion complementarity and translation into protein sequences. Through Bio.Entrez, you can access the NCBI database and obtain GenBank data, but you need to set up your email address. In addition, Biopython supports pairwise sequence alignment and PDB file parsing, which is suitable for structural analysis tasks.