What is quantitative trading?

What is quantitative trading? Quantitative Trading

Quantitative trading, also known as Quantitative Trading in English, or Quant for short, is a trading method often used by financial investment institutions.

It aims to analyze a large amount of data in a short time, and then conduct automatic trading through pre-written trading models to improve trading efficiency and avoid the impact of human emotions on trading. With the popularization of computer languages, quantitative trading has gradually been applied among ordinary investors.

70% to 80% of transactions on Wall Street are completed through quantitative trading. Currently, some brokers, such as Interactive Brokers IBRK and Futu moomoo, provide APIs for conducting their own quantitative trading strategies.

Before making a choice, ordinary investors will analyze some data, including the target company's operating conditions, financial conditions, the performance of the company's stock price in the past period, etc.

After analyzing a bunch of data, they will choose based on their own subjective judgment, or follow the guidance of some masters, or follow the mainstream trend of the market.

If an investor is going to invest in multiple stocks, he will also need to collect information about the industry in which the company is located, etc., which increases the amount of analysis required. Professional investors spend most of their time collecting information, sorting it out, and then making judgments.

For financial institutions, the amount of information to be processed is thousands of times that of ordinary investors, and the judgments to be made require eliminating a large amount of interference to find the most accurate timing.

Regardless of ordinary investors or professional institutions, the investment process can generally be divided into two steps: The first step is to collect and organize the given information and find out the rules; The second step is to formulate a strategy based on the data and judge the stock trend; The first step is "annoying".

The amount of information is too large and too complicated. You need to collect a large amount of data on listed companies, including company fundamental data, stock transaction data, etc.; The second part is "difficulty". After obtaining the information, personal judgment of buying and selling will be influenced by personal emotions.

Many investors may have regretted: they were too impulsive at that time, just calm down. With the improvement of information technology and the development of computer computing power, the content of the first step is constantly simplified.

You can easily obtain various information data tables and trend charts, and the time range of data acquisition can also reach a large span. As for the second part, because it is impossible to carry out targeted data analysis, the investment operations given through computer data analysis are also in the reference stage.

In modern times, with the continuous development and simplification of computer languages, more and more people have mastered computer languages and relied on their own mathematical abilities to build calculation models that suit them.

After backtesting to verify the applicability of the model, target object data analysis and transaction point decisions can be carried out in a very short time. In this process, there is only computer analysis and no interference from human emotions.

This trading method has been verified over time and proved to be effective, so it is used in many large financial institutions.

Financial institutions can complete a large number of transactions in an instant, which is now called quantitative trading: Write targeted trading models through computer languages, such as C/C++, MATLAB, R or Python, etc., and use digital analysis and mathematical operations to complete large-scale information collection, data analysis, purely rational strategic decision-making and transaction execution.

Those currently engaged in performing quantitative trading are called quantitative traders (Quants), and their main jobs are: Strategy identification: Write your own trading model, or find existing strategies, and combine your own advantages to determine the use of strategies and trading frequency.

Strategy backtesting: Apply the target strategy to historical data, and use model operations to verify whether the calculation results of the model are consistent with the historical results. If they are consistent, you can continue to use them and make detailed adjustments. If they are not consistent, abandon the model.

Execution system: Apply successfully verified trading models to actual transactions to complete automatic quantitative transactions and minimize transaction costs. Risk management: Track quantitative transactions based on trading models, discover possible risks in the transaction process, and conduct risk management in a timely manner.

Founder of Quantitative Trading

Jim Simons was the first great investor to combine computer mathematical models with financial investments. The Renaissance Technologies hedge fund company he founded is currently the most successful hedge fund company focusing on quantitative trading. Jim Simons entered the financial field with a background as a mathematician and code breaker.

He advocated excluding various human emotional factors from financial investment decisions, using mature computer languages to judge market conditions and obtained astonishing returns from it. He believes that computers have their own opinions.

After establishing a mature and successful trading model, he will blindly follow the results of the computer and refuse any artificial emotions to interfere with the computer's calculation and judgment results.

Since he founded his Renaissance Technologies fund company based on computer quantitative trading in 1982, in just over 30 years, Jim Simons has accumulated a wealth of US$23 billion for himself with his quantitative trading system.

Advantages and Disadvantages of Quantitative Trading Quantitative trading is used by most financial institutions and is now gradually used by many individual investors.

Like many other trading methods, it has its own advantages and disadvantages: Advantages It can quickly collect and analyze large amounts of data, greatly reducing the workload of target selection. When the transaction operation trigger point is set, the transaction can be carried out automatically, reducing the workload of daily investment.

Use computer mathematical models to rationally analyze market conditions, judge operation methods, and effectively avoid the interference of human emotions. Disadvantages A single quantitative trading model cannot always be effective in a dynamic market, and parameters need to be adjusted regularly to adapt to changes in the market environment.

What are the quantitative trading platforms? Quantitative trading platforms are currently mainly used by major financial institutions, hedge funds and other platforms that need to analyze large amounts of transaction data.

There are also more and more individuals who, after learning computer languages, write their own trading programs to conduct financial transactions that are more in line with their own investment characteristics.

Platforms that can write quantitative transactions are mainly programs written in several major computer languages, such as C/C++, MATLAB, or Python language writing platforms. Platforms for learning quantitative trading are currently very popular.

They usually provide a relatively complete course system to train high-end quantitative traders from aspects such as mathematics, logic, computer language and financial knowledge.

Futu’s Quantitative Trading API Introduction Futu not only provides low transaction fees and supports investment in the financial field in the United States, Hong Kong, China and other places, but also provides investors with API technology to facilitate investors to make more comprehensive investments.

Futu moomoo account opening Futu Quantitative API is called Futu OpenAPI, which is mainly composed of FutuOpenD and Futu API. Among them, FutuOpenD is the gateway program of Futu API.

It is mainly used to run the customer's local computer and cloud server, and is responsible for sending the transfer protocol request to the Futu backend, and then returning the processed data to the client.

Futu API is an API SDK encapsulated by Futu for several mainstream computer languages: Python, Java, C#, C++, JavaScript, etc., to facilitate users to call directly and simplify the difficulty of users developing investment programs by themselves.

Even if the language used by investors is not among the above, they can still connect to the naked protocol by themselves to complete the development of investment strategies. When users need to use Futu OpenAPI, they usually need to perform two steps: Install and start the gateway program FutuOpenD on the local or cloud client.

The program will expose the client interface in a customized TCP protocol for subsequent data transmission. This protocol interface has nothing to do with computer programming languages. Download Futu API and complete the running environment setup to facilitate subsequent quick calls to the pre-packaged corresponding API SDK.

After the software installation is completed, users need to open a Futu OpenAPI account. This account is designed with two types of accounts: platform account and transaction business account. The platform account is the account registered by the user on Futu. This account can be used on both the Futu Niu Niu and Futu moomoo platforms.

Trading business accounts need to be opened separately according to the different fields in which investors invest, such as: Hong Kong stock account is used for securities investment in the Hong Kong market U.S. stock accounts are used to make financial investments in the U.S.

market The A-share connect account is used to trade stock securities in the A-share connect market. Futures accounts are used to trade futures products in global markets, including futures markets in Hong Kong, the United States, Singapore and Japan. Next, investors can use Futu OpenAPI.

Currently, Futu quantitative trading API mainly provides two major functions: market analysis and transaction execution. The market analysis function supports users to collect and analyze market data for all or specific financial product categories in Hong Kong, the United States and A-share markets.

Data types include real-time quotations, real-time K-lines, real-time swings, historical K-lines, etc. . The transaction execution function supports users to conduct real transactions and simulated transactions involving stocks, futures, options, etc. in the world's five major markets: Hong Kong, the United States, A-shares, Singapore and Japan.

Futu OpenAPI is a relatively mature quantitative trading API on the market. It can provide users with: Full platform operation: FutuOpenD supports the installation and operation of Windows, MacOS, CentOS, Ubuntu and other systems.

Multi-language writing: Futu OpenAPI supports Python, Java, C#, C++, JavaScript and other mainstream languages Stable operating environment: Futu OpenAPI provides a stable technical framework, allowing users to smoothly write trading models that suit themselves, and conduct backtesting, applications, etc.

Extremely fast experience: Whether it is code running programs or real-time transactions, it can be completed extremely quickly if the network speed permits. The fastest order can be placed is 0.0014 seconds, which is very suitable for high-frequency traders.

Free trading: After users write their own trading model, they can trade using it without paying any additional fees. Multi-category investment: Futu OpenAPI supports real-time trading and simulated trading of multi-category products in Hong Kong, the United States, A-shares, Singapore and Japan. How to build a Python quantitative trading system?

Quantitative trading systems can be written in a variety of computer languages, and Python, as the most popular computer language at the moment, ranks first in usage among many languages. Python is a cross-platform compatible high-level programming language.

The open source environment has multiple proprietary professional library functions, such as: Scipy, numpy, pandas, matplotlib, quantopian, Zipline, TA-Lib, Pybacktest, etc. can quickly develop barrier-free quantitative trading strategies.

Tensorflow, seaborn, scikit learn, Keras, plotly, and stats can help transaction models perform more effective data mining and transaction execution. SpyderIDE optimizes data visualization in trading models, making financial analysis more intuitive and easier.

As an exclusive algorithmic trading library function for Python, PyAlgoTrade focuses on paper trading, backtesting, real-time trading and technical analysis, bringing more efficient quantitative trading.

Using Python as a computer language to write trading models is the same as the development process of all quantitative trading models, which consists of strategy identification, strategy backtesting, execution system and risk management.

But the advantage of Python is that in all processes, its computer language is easier to understand, its logical ordering is more organized, and it provides multiple exclusive library functions that can be called directly.

In the strategy identification stage, you can call multiple library functions according to the trading characteristics you need to write a trading strategy that is more suitable for you.

In the strategy backtesting phase, professional library functions can perform more comprehensive data backtesting to obtain more accurate backtesting results and ensure that the trading model written in the early stage is more effective.

In terms of execution system, because of the clarity of language logic, the probability of BUGs during model execution is greatly reduced, and no investment benefits are missed.

During the risk management process, because the language is clear, it is easy to find adjustment points and make fine adjustments to the data to control necessary risk management without affecting the complete operation of the entire trading model.

What are the quantitative trading strategies? Quantitative trading strategy means that quantitative traders write targeted trading models based on the characteristics of trading styles, collect and integrate the required information, and conduct data monitoring and decision-making execution based on different proposed trading judgment points.

At present, the more successful quantitative trading strategies that have been verified by the market include: Alpha Hedging Strategy Investors will face systemic risks - Beta/β risks and non-systematic risks - Alpha/α risks in market transactions.

By measuring and separating systemic risks, they can obtain excess absolute returns, that is, alpha return strategies, which are called alpha hedging strategies.

Reference: Alpha Hedging Strategy Source Code turtle trading strategy The turtle strategy is a trend-following quantitative trading strategy that sets parameters in entry conditions, position control, fund management, stop loss and take profit to conduct automated trading.

This strategy can be used as a basic template for the design of complex trading strategies. Reference: Turtle trading strategy source code Multi-factor stock picking strategy The multi-factor stock selection strategy is to find certain indicator parameters related to the rate of return and construct a stock portfolio based on this indicator.

If the stock portfolio outperforms the market index, continue to go long and short the futures index to earn alpha income. If it underperforms, go long the index futures and short the current stock portfolio to earn reverse alpha income. It is an important model in current quantitative stock selection.

Reference: Multi-factor stock selection strategy source code Double moving average strategy The basic idea of the dual moving average strategy is to establish an m-day moving average and an n-day moving average respectively. The two moving averages will definitely intersect.

If m>n, the n-day moving average "crosses" the m-day moving average point, which is the buying point, and vice versa, it is the selling point. This strategy carries out automated quantitative trading by seizing the strong and weak moments of stocks based on the intersection of moving averages of different days.

Reference: Double moving average strategy source code Cross-species arbitrage strategy The basic idea of this strategy is to trade the price difference between two different types of index futures products that are interrelated.

Interdependence means that they are mutually substitutable or affected by the same supply and demand factors, such as arbitrage between related commodities or arbitrage between raw materials and finished products. For the market, this strategy can help distorted market prices return to normal levels and increase market liquidity.

Reference: Cross-variety arbitrage strategy source code intertemporal arbitrage strategy Similar to the cross-quality arbitrage strategy, the intertemporal arbitrage strategy is also a quantitative trading strategy suitable for futures.

Intertemporal arbitrage is to gain arbitrage benefits by trading futures contracts of the same index and different delivery months on the same exchange. Reference: Intertemporal arbitrage strategy source code Exponential Enhancement Strategy This strategy is suitable for index investors.

Fund managers use this strategy to keep the characteristic parameters in their recommended investment portfolios higher than the return level of the underlying index to maintain good investment performance.

Reference: Exponential enhancement strategy source code Grid Trading Strategy This strategy is an active trading strategy that uses market fluctuations to make profits. Its basic idea is to use the price difference of the investment target to fluctuate repeatedly within the preset value grid range to repeatedly increase and decrease positions.

For example, increase positions when the price of the target object breaks through the grid, and reduce positions when it returns to the grid, so as to maximize investment returns.

Reference: Grid trading strategy source code Industry rotation strategy This strategy aims to automatically switch between different industries in order to maximize investment returns based on the strength of different brands in different industries.

Reference: Industry rotation strategy source code High Frequency Trading Strategies High-frequency trading strategies can help investors earn profits in extremely short market changes.

The computer can track market trends in real time according to the set program, automatically perform buying or selling operations within the set price difference, and earn large profits from price fluctuations through a large number of transactions. R-Breaker Strategy

R-Breaker is an intraday trading strategy.

Based on the closing price, highest price and lowest price data of the previous trading day, with the help of a specific mathematical model, six price levels are established, from high to low: breakthrough buying price, observation selling price, reversal selling price, reversal buying price, observation buying price and breakthrough selling price.

These six price levels are different operation trigger points for the current transaction. Investors can adjust the parameters in the model to adjust the gap between each price level to change the automatic operation trigger conditions. This strategy was rated one of the most profitable by Future Thruth magazine.

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