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NEW YORK, Dec 30 (Reuters) - Sam Bankman-Fried is expected on Tuesday to enter a plea of not guilty to criminal charges that he cheated investors and looted billions of dollars at his now-bankrupt FTX cryptocurrency exchange, according to a source familiar with the matter.
Bankman-Fried is accused of illegally using FTX customer deposits to support his Alameda Research hedge fund, buy real estate and make millions of dollars in political contributions.
He is scheduled to appear at 2 p.m. EST (1900 GMT) on Tuesday before U.S. District Judge Lewis Kaplan in Manhattan to enter a plea.
A lawyer for Bankman-Fried did not immediately reply to a request for comment.
It is not unusual for criminal defendants to initially plead not guilty. Defendants are free to change their plea at a later date.
Bankman-Fried has been free on $250 million bond following his extradition last month from the Bahamas, where he lived and where the exchange was based.
Since his release, Bankman-Fried has been subject to electronic monitoring and required to live with his parents, both professors at Stanford Law School in California.
The Massachusetts Institute of Technology graduate has been charged with two counts of wire fraud and six conspiracy counts, including to launder money and commit campaign finance violations. He could face up to 115 years in prison if convicted.
Bankman-Fried has admitted to making mistakes running FTX but said he did not believe he was criminally liable.
The 30-year-old crypto mogul rode a boom in the value of bitcoin and other digital assets to become a billionaire several times over and an influential political donor in the United States, until FTX collapsed in early November after a wave of withdrawals. The exchange declared bankruptcy on Nov. 11.
The prosecution case was strengthened by last month's guilty pleas of two of Bankman-Fried's closest associates.
Caroline Ellison, who was Alameda's chief executive, and Gary Wang, FTX's former chief technology officer, pleaded guilty to seven and four criminal charges, respectively, and agreed to cooperate with prosecutors.
Ellison told prosecutors she agreed with Bankman-Fried to hide from FTX's investors, lenders and customers that the hedge fund could borrow unlimited sums from the exchange, according a transcript of her Dec. 19 plea hearing.
Reporting by Jack Queen; Additional reporting by Chris Prentice; Editing by Noeleen Walder and Daniel Wallis
Our Standards: The Thomson Reuters Trust Principles.
AI robot trading is the use of artificial intelligence (AI) and machine learning algorithms to analyze financial markets and execute trades automatically. AI robots are able to analyze vast amounts of data and make decisions based on that analysis, allowing them to identify trading opportunities that might be missed by human traders. One key aspect of AI robot trading is machine learning, which is a type of AI that enables systems to learn and improve their performance over time. Machine learning algorithms are able to analyze data and use it to make predictions or decisions without being explicitly programmed to do so. This allows them to adapt and improve their performance as they are exposed to more data. There are several techniques that can be used in AI robot trading, including: Decision trees: These are algorithms that use a tree-like structure to make decisions based on multiple conditions. Neural networks: These are algorithms that are inspired by the structure and function of the human brain, and are capable of learning and adapting based on input data. Genetic algorithms: These are algorithms that use principles of genetics and natural selection to optimize and improve their performance over time. Reinforcement learning: This is a type of machine learning that involves an AI agent receiving rewards or punishments for certain actions, and using that feedback to learn and adapt its behavior. By using these and other techniques, AI robot traders are able to analyze financial data and make informed decisions about when to buy and sell assets. This can help them achieve better returns and outperform human traders in some cases.