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Gate.io Cryptopedia: What is Quantitative Trading
25 May 12:38
The concept of quantitative trading
Quantitative Trading originated in the 1970s and has gradually developed into a mainstream trading method. Specifically, quantitative trading is an investment method that relies on computers and statistical methods rather than human judgment to make decisions. By using advanced models and analyzing massive amounts of data, quantitative trading can identify trading opportunities. At the same time, quantitative trading avoids the wrong judgment caused by irrational factors such as human emotions. At present, in the U.S. stock market and many other developed financial markets, about 70-80 percent of the overall trading volume is generated through quantitative trading. But in emerging economies like China and India, this proportion is about 40%.
There are many notable quants like Renaissance, founded in 1982, and 2 Sigma, which relies on big data and artificial intelligence to analyze public opinion.
In principle, both quantitative trading and traditional trading are based on the Market's inefficient-form Market Efficiency. In inefficient markets, asset prices can be overvalued or undervalued due to factors such as Information asymmetry, transaction costs, and market psychology. Accurately capturing this kind of pricing inefficiency is the key to excess returns.
In manual trading, investment managers often conduct fundamental analysis such as growth and management, valuing individual stocks, thereby discovering improperly priced assets and profiting from the transaction. In quantitative investment, abstract knowledge about finance and economics is embodied in programs and algorithms.
For example, when satellite images of all Walmarts show an increase in the number of parked cars, which implies an increase in shoppers, the price of Walmart should rise, especially during the next quarterly earnings announcement. So when the quantitative program sees a significant increase in the number of parked cars, it can make a profit by soon buying relevant assets before Walmart's share price rises.
In a complex market, a large number of factors affect the price of assets. Whether historical price fluctuations, supply chain changes, or even social media discussions, may help us estimate the rise and fall of market prices. Computers are good at the analysis of massive data, and systematically identifying trading opportunities. Especially for those repeated arbitrage opportunities, computers can identify and capture them.
Image source: unsplash.com
Based on the trading frequency, from low to high, quantitative trading can be divided into ultra-low-frequency quantitative trading (up to 1 day or even a week), medium-and low-frequency Trading (ranging from 1 second to 1 hour), and high-frequency Trading (HFT, less than 1 second or even milliseconds).
Elements of quantitative trading
For quantitative trading, the hardware environment, relevant data, financial theory, program development are four necessary parts. As for HFT, it is necessary to maximize speed and reduce delays, and the hardware, as well as software conditions, are especially important. For example, HFT teams usually use a special circuit (FPGA) to improve their speed. As a result, high-frequency trading has a very high technical and financial threshold.
The theory of quantitative trading is generally called a trading strategy. A complete trading strategy needs to include at least three parts: input, logical processing, and output. There are many kinds of trading strategies, including statistical arbitrage strategies, trend trading strategies, and so on. New strategies like machine learning and big data analysis are still developing.
Quantitative transactions and cryptocurrencies
Some characteristics of the cryptocurrency market make quantitative trading naturally preferred. First of all, cryptocurrency exchange often provide related API interfaces to facilitate users obtaining detailed historical price data. In addition, unlike the stock market, the cryptocurrency market conducts transactions 7x24 hours, making transaction data more consistent and research-friendly. And computers can meet all-weather trading challenges.
Can individuals conduct quantitative transactions?
Although it is difficult to reach a high-frequency level, personal hardware and procedures can also capture trading signals in a minute scale and can conduct low-frequency quantitative trading.
Currently, Python and R are the main platform languages for developing quantitative trading software. As for trading strategies, ordinary investors can develop their own algorithms to implement certain trading techniques, or they can write quantitative programs based on certain trading indicators such as candle charts and Bollinger bands. After software and hardware preparation, we must also use historical data to carry out regression tests and simulation trading. After testing, we can carry out firm trading.
In addition, investors can visit the Quantitative Center of Gate.io , realizing quantitative trading of digital assets with only one key-press.
Author: Gate.io Researcher: Edward.H
* This article represents only the views of the researcher and does not constitute any investment suggestions.
*Gate.io reserves all rights to this article. Reposting of the article will be permitted provided Gate.io is referenced. In all other cases, legal action will be taken due to copyright infringement.
Reference:
https://therobusttrader.com/what-percentage-of-trading-is-algorithmic/
Quantitative Trading originated in the 1970s and has gradually developed into a mainstream trading method. Specifically, quantitative trading is an investment method that relies on computers and statistical methods rather than human judgment to make decisions. By using advanced models and analyzing massive amounts of data, quantitative trading can identify trading opportunities. At the same time, quantitative trading avoids the wrong judgment caused by irrational factors such as human emotions. At present, in the U.S. stock market and many other developed financial markets, about 70-80 percent of the overall trading volume is generated through quantitative trading. But in emerging economies like China and India, this proportion is about 40%.
There are many notable quants like Renaissance, founded in 1982, and 2 Sigma, which relies on big data and artificial intelligence to analyze public opinion.
In principle, both quantitative trading and traditional trading are based on the Market's inefficient-form Market Efficiency. In inefficient markets, asset prices can be overvalued or undervalued due to factors such as Information asymmetry, transaction costs, and market psychology. Accurately capturing this kind of pricing inefficiency is the key to excess returns.
In manual trading, investment managers often conduct fundamental analysis such as growth and management, valuing individual stocks, thereby discovering improperly priced assets and profiting from the transaction. In quantitative investment, abstract knowledge about finance and economics is embodied in programs and algorithms.
For example, when satellite images of all Walmarts show an increase in the number of parked cars, which implies an increase in shoppers, the price of Walmart should rise, especially during the next quarterly earnings announcement. So when the quantitative program sees a significant increase in the number of parked cars, it can make a profit by soon buying relevant assets before Walmart's share price rises.
In a complex market, a large number of factors affect the price of assets. Whether historical price fluctuations, supply chain changes, or even social media discussions, may help us estimate the rise and fall of market prices. Computers are good at the analysis of massive data, and systematically identifying trading opportunities. Especially for those repeated arbitrage opportunities, computers can identify and capture them.
Image source: unsplash.com
Classification of quantitative transactions
Based on the trading frequency, from low to high, quantitative trading can be divided into ultra-low-frequency quantitative trading (up to 1 day or even a week), medium-and low-frequency Trading (ranging from 1 second to 1 hour), and high-frequency Trading (HFT, less than 1 second or even milliseconds).
Elements of quantitative trading
For quantitative trading, the hardware environment, relevant data, financial theory, program development are four necessary parts. As for HFT, it is necessary to maximize speed and reduce delays, and the hardware, as well as software conditions, are especially important. For example, HFT teams usually use a special circuit (FPGA) to improve their speed. As a result, high-frequency trading has a very high technical and financial threshold.
The theory of quantitative trading is generally called a trading strategy. A complete trading strategy needs to include at least three parts: input, logical processing, and output. There are many kinds of trading strategies, including statistical arbitrage strategies, trend trading strategies, and so on. New strategies like machine learning and big data analysis are still developing.
Quantitative transactions and cryptocurrencies
Some characteristics of the cryptocurrency market make quantitative trading naturally preferred. First of all, cryptocurrency exchange often provide related API interfaces to facilitate users obtaining detailed historical price data. In addition, unlike the stock market, the cryptocurrency market conducts transactions 7x24 hours, making transaction data more consistent and research-friendly. And computers can meet all-weather trading challenges.
Can individuals conduct quantitative transactions?
Although it is difficult to reach a high-frequency level, personal hardware and procedures can also capture trading signals in a minute scale and can conduct low-frequency quantitative trading.
Currently, Python and R are the main platform languages for developing quantitative trading software. As for trading strategies, ordinary investors can develop their own algorithms to implement certain trading techniques, or they can write quantitative programs based on certain trading indicators such as candle charts and Bollinger bands. After software and hardware preparation, we must also use historical data to carry out regression tests and simulation trading. After testing, we can carry out firm trading.
In addition, investors can visit the Quantitative Center of Gate.io , realizing quantitative trading of digital assets with only one key-press.
Gate.io Quantitative Center
Author: Gate.io Researcher: Edward.H
* This article represents only the views of the researcher and does not constitute any investment suggestions.
*Gate.io reserves all rights to this article. Reposting of the article will be permitted provided Gate.io is referenced. In all other cases, legal action will be taken due to copyright infringement.
Reference:
https://therobusttrader.com/what-percentage-of-trading-is-algorithmic/
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