量化交易是一种市场策略，它依靠数学和统计模型来识别并执行机会。这些模型由定量分析驱动，这就是该策略的名称。它通常也被称为“定量交易”，有时也称为“定价”。
量化分析研究和测量将行为的复杂模式分解为数值。
What data can quantitative traders view?
The two most common data points that quantitative traders check are price and quantity. V+MrsFu123 system development, however, any parameters that can be refined into numerical values can be included in the strategy. For example, some traders may build tools to monitor investor sentiment on social media.
Quantitative traders can use many publicly available databases to inform and establish their statistical models. These alternative datasets are used to identify patterns outside of traditional financial sources, such as fundamentals.
Quantitative trading system
Quantitative traders develop systems to identify new opportunities and often implement them. Although each system is unique, they usually contain the same components:
strategy
Backtesting
implement
risk management
The following is a detailed description of each:
- Strategy
Before creating the system, Quants will study the policies they want it to follow. Usually, this takes the form of a hypothesis. For example, the above example uses the assumption that FTSE, for example, tends to perform certain operations at specific times of the day.
After adopting appropriate strategies, the next task is to convert them into mathematical models, and then improve them to increase returns and reduce risks.
This is also the key point that quantitative indicators will determine the frequency of system transactions. The high-frequency system opens and closes many positions every day, while the low-frequency system aims to find long-term opportunities.
- Backtesting
Backtesting involves applying strategies to historical data to understand their performance in the real-time market. Quants often uses this component to further optimize its system in an attempt to eliminate any problems.
Backtesting is an important part of any automated trading system, but successful operation cannot guarantee the profit when the model takes effect. The completely retested strategy will still fail for a variety of reasons: including incorrect historical data or unpredictable market trends.
A common problem with backtesting is to determine how much volatility the system will see when generating returns. If traders only view the annual return of the strategy, they cannot understand the complete situation.
- Execution
Each system will contain an execution component, ranging from fully automatic to fully manual. Automated policies often use APIs to quickly open and close positions without manual input. A manual may require traders to call brokers to trade.
The HFT system is completely automated in nature – human traders cannot open and close positions quickly enough to succeed.
The key part of execution is to minimize transaction costs, which may include commissions, taxes, delays and spreads. Complex algorithms can be used to reduce the cost of each transaction – after all, if the opening and closing costs of each position are too high, even a successful plan may fail.
- Risk Management
Any form of transaction requires risk management, and the number is the same. Risk refers to any factor that may interfere with the success of the strategy.
Capital allocation is an important area of risk management, covering the size of each transaction – if multiple systems are used for quantification tools, how much capital will be invested in each model. This is a complex area, especially when dealing with leverage strategies.
A fully automated strategy should not be affected by human bias, but only if its creators ignore it. For retail traders, keeping the system running without too much patching may be a major part of managing risk.
Quantitative trading strategy
Quantitative traders can use a variety of strategies, from simple to incredibly complex. Here are six common examples you might encounter:
Mean reversion
Trend Tracking
Statistics tao. Li
Mean reversion
Many quantitative strategies belong to the general scope of mean reversion. Mean reversion is a financial theory that assumes that prices and returns have long-term trends. Any deviation should eventually revert to this trend
量化交易如何运作？
量化交易通过使用基于数据的模型来确定特定结果发生的可能性。与其他形式的交易不同，它完全依靠统计方法和编程来完成此操作。
Quantitative trading refers to the establishment of mathematical models based on certain data and historical statistics,and the formulation of trading strategies in combination with mathematical analysis and computer technology.
Quantitative trading can be understood as programmed trading.It is systematic and disciplined,easy to use,and can help users by analyzing data.In this way,users'emotional fluctuations can be reduced.
The advantages of quantitative robots can enable users to reduce emotional fluctuations,monitor market fluctuations in real time,and avoid users making irrational decisions.In addition,if the trading volume of some currencies is small,and the trading volume is low when encountering a bear market,the quantitative robot can make the market more conventional and avoid unreasonable price fluctuations
量化交易与算法交易
算法交易者使用自动系统来分析图表模式，然后代表他们开立和关闭头寸。量化交易者使用统计方法来识别但不一定执行机会。尽管它们彼此重叠，但是这是两种不应该混淆的独立技术。
两者之间有一些重要区别：
算法系统将始终代表您执行。一些量化交易者使用模型来识别机会，然后手动打开头寸
量化交易使用高级数学方法。算法倾向于依赖更传统的技术分析
算法交易仅使用图表分析和来自交易所的数据来寻找新头寸。量化交易者使用许多不同的数据集
了解有关算法交易的更多信息，或创建一个帐户以立即开始使用。
The automatic quantification robot can run on the server for 24 hours.After initialization,the robot will trade according to the set strategy.Trade when the conditions are met,and do not need to mark the order for a long time.The robot has a variety of built-in transaction strategies to meet different types.After the strategy is set,the robot can only allocate the conditions of each transaction,strictly implement the transaction strategy,and adjust in real time according to the market and big data,so as to view the transaction conditions in real time and ensure the timeliness of transaction execution.
