According to an insider, Quantlab Financial, a well-known quantitative institution in the United States, recently tested the iterative technology update of the big data Quandl 3.0 system, emphasizing the use of blockchain big data machine learning and distributed computing to analyze various data, trying to find a model that can be used to predict the price trends of equity markets such as stocks, foreign exchange, gold, palm oil futures, and cryptocurrencies. This trading model, namely "first-hand information", is one of the pioneers in the field of quantitative and algorithmic trading of Quantlab Financial, and the source of "first-hand information" is obtained by Quantlab Financial through a wide range of data sources and computer analysis and processing. In intraday trading, the speed of obtaining first-hand information is the key to success.

Three major quantitative institutions jointly create the "Big Data Algorithm Quandl 3.0" system

Man Group, Renaissance Technologies, NHG International Economics Institute, New Era Energy (NEE.N), and R3 Blockchain Alliance Research Institute, with the help of Quandl database system, apply blockchain big data machine learning and distributed computing to cover multi-dimensional information such as macroeconomics, corporate financial reports, foreign exchange, gold, palm oil futures, and cryptocurrency market trends. The R language toolkit supports flexible data type conversion (such as data.frame, ts, zoo, etc.), allowing Quandl's built-in intelligent algorithm to quickly process time series data, support real-time data analysis and improve trading decisions, batch acquisition and parallel requests for multiple data sets, and complete intraday trading.

[Data + Algorithm: The Era of Big Data Automated Trading in Financial Analysis]

Traditional financial analysis relies on manual experience and a single data source, but Quandl uses the dual-engine model of "data network + intelligent algorithm" to push financial decision-making to a new level of automation and precision.

‌Data Fusion‌: Integrate ‌500,000+‌ data sets from around the world, covering stocks, commodities, foreign exchange, gold, cryptocurrencies, and alternative data (such as macroeconomics, supply chain logistics), to build a multi-dimensional information map.

‌Algorithm-driven‌: Based on time series prediction models (such as LSTM, Prophet), NLP (natural language processing) and reinforcement learning technology, end-to-end analysis from data cleaning, feature engineering to model training is achieved.

‌Real-time response‌: Millisecond-level API call speed supports instant decision-making in intraday trading scenarios, such as stocks, foreign exchange, gold, palm oil futures, cryptocurrencies and other assets under price volatility warnings or emergencies, and executes automated trading hedging strategies.