Machine learning is to trading what fire was to the cavemen. That’s how one industry player described the impact of a disruptive technology on a staid industry. AI trading companies use various tools in the AI wheelhouse — machine learning and algorithmic predictions, for example — allowing brokers to customize exchanges and secure stocks. One benefit of AI stock trading is that it can be executed on ordinary networks and PCs.
When Wall Street statisticians realized they could apply machine learning to many aspects of finance, including investment trading applications, Anthony Antenucci, vice president of global business development at Intelenet Global Services, had insight to share. “They could effectively crunch millions upon millions of data points in real time and capture information that current statistical models couldn’t,” he told ITPro Today. “Machine learning is evolving at an even quicker pace and financial institutions are one of the first adaptors.”
Exemples:
Through its 2017 acquisition of 'Neurensic', 'Trading Technologies' has an AI platform that identifies complex trading patterns on a massive scale across multiple markets in real time. Combining machine learning technology with high-speed, big data processing power, the company provides clients with the ability to build their own algorithm trading platforms. This allows users to automate the entry and exit of positions and reduce the market impact of large orders as well the risk of manual errors.
'Numerai' uses machine learning to predict stock market trends and manage a new kind of hedge fund. The firm is a unique player in the market, as it uses encrypted data sets to crowdsource stock market models predicted by AI. The models are sourced from anonymous data scientists who are awarded Numerai’s cryptocurrency, NMR, for providing better models.
'IntoTheBlock' uses AI and deep learning to power its price predictions for a variety of crypto markets. IntoTheBlock’s models are trained on spot, blockchain and derivatives datasets and allow users to access historical data to better inform their trade decisions.
Overnight, 'Trade Ideas' AI-powered self-learning, robo-trading platform “Holly” subjects dozens of investment algorithms to more than a million different trading scenarios to increase the alpha probability in future sessions. Each night the AI assistant platform will select the strategies with the highest statistical chance to deliver profitable trades for the upcoming trading day. On average, Holly enters between 5 and 25 trades per day based on various strategies.
'Sentieo' provides a host of financial solutions with the help of AI. The company’s AI-powered financial search engine collects internal and external content into a single shared workspace. Analysts can use its natural language processing to identify the latest news on key financial searches, while individual investors can use its platform to research companies and markets.
In conclusion, machine learning can help us improve our trading game, but from the studies researched, AI, let alone still can’t do better than a human.
https://builtin.com/artificial-intelligence/ai-trading-stock-market-tech
Niciun comentariu:
Trimiteți un comentariu