WO2022247312A1 - 一种通过计算机实现的交易价格参考指标计算方法 - Google Patents

一种通过计算机实现的交易价格参考指标计算方法 Download PDF

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WO2022247312A1
WO2022247312A1 PCT/CN2022/071971 CN2022071971W WO2022247312A1 WO 2022247312 A1 WO2022247312 A1 WO 2022247312A1 CN 2022071971 W CN2022071971 W CN 2022071971W WO 2022247312 A1 WO2022247312 A1 WO 2022247312A1
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price
time
trading
market
volume
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陈新燊
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陈新燊
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0206—Price or cost determination based on market factors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/06—Asset management; Financial planning or analysis

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  • the invention relates to a calculation method of a transaction price reference index implemented by a computer.
  • the method calculates and generates a transaction reference price index of a financial market transaction product by superimposing discrete quantitative elements of time and quantity distribution on a conventional price-time.
  • This traditional transaction price pricing logic has the following two problems:
  • the price is easy to be manipulated. Because the average closing price of a period is used as the transaction price, and the closing price is artificially pulled up or down at the moment before the end of the market to deliberately control the transaction price.
  • the real market price should be the transaction price with the largest trading volume or the longest trading time, not the closing price.
  • the primary purpose of the present invention is to provide a computer-implemented calculation method for transaction price reference indicators, which generates financial market transaction products by superimposing discrete quantitative elements of time and quantity distributions on conventional price-time
  • the transaction reference price index can accurately reflect the real-time market transaction situation, avoid the phenomenon of price manipulation, and realize accurate statistics and analysis of financial prices.
  • Another object of the present invention is to provide a computer-implemented method of calculating a transaction price reference indicator by superimposing conventional price-time on discrete quantified elements of intra-market activity related to time/volume distributions at different prices Come up and expand it.
  • a calculation method of a transaction price reference index implemented by a computer includes the following steps:
  • BTU is the basic time unit
  • the condensation point is the price point with the largest trading volume. Therefore, this is the price level where the market spends the most time or trades the most volume, known as the point of condensation.
  • each method produces a set of cohesion points, which may differ from each other.
  • the user will decide whether to calculate the displayed condensation points according to the time method or according to the quantity method. It should be noted that under normal circumstances, the cohesion point calculated by the time method should be close to the cohesion point calculated by the quantity method. This is because, the longer the market spends at a certain price, naturally, the more volume it trades there.
  • the present invention uses the frequency distribution to calculate the effective range mean shift method.
  • Each trading interval represents a unit of frequency (time or number of trades) at a certain price. Therefore, a frequency distribution chart can be viewed as a collection of transactions, each with its own price.
  • the invention then calculates the mean and standard deviation of the population of prices in the trading range. Since the effective range of the trading range accounts for 68% of trading activity, it can be considered a fair equilibrium value for the market, since this price range is the price range in which participants agree to trade within the entire community.
  • step 101 the time and price are first used to establish a distribution table, and a bar graph is established based on the distribution table; then, a frequency distribution graph is constructed by using the trading volume method based on the bar graph.
  • the preferred time frame is daily, and the price increment unit is 0.5; first, the trading volume of each discrete price throughout the day is drawn into a frequency distribution table, where the trading volume data comes from the specific trading volume , and represented by the number of stocks; then the Y-axis represents the discrete price level, the X-axis represents the trading volume of each price on the Y-axis, and draws a frequency distribution graph.
  • the transaction volume refers to the stock transaction volume or USD transaction volume.
  • transaction time which can be time units (if the time method is used) or quantity units (if the transaction quantity is used).
  • Bar is used to represent the graphical entity of a given time interval on any price-time chart, whether it is a bar or a Japanese candlestick.
  • the present invention superimposes the discrete quantitative elements of time and quantity distribution on the regular price-time to calculate and generate the transaction reference price index of financial market transaction products, so as to accurately reflect the real-time market transaction situation, avoid the phenomenon of price manipulation, and realize financial control. Accurate statistics and analysis of prices.
  • Fig. 1 is the time-price distribution table realized by the present invention.
  • Figure 2 is a time-price bar graph implemented by the present invention.
  • Fig. 3 is a price-volume frequency table realized by the present invention.
  • Fig. 4 is a price-volume frequency distribution diagram realized by the present invention.
  • Fig. 5 is an effective range calculation table realized by the present invention.
  • the average closing price is widely used by traders and analysts as a means of calculating transaction prices in financial and commodity transactions. It is widely used in the market to use the closing average price as the trading price in a given time interval.
  • Figure 2 also shows that the price distribution obtained in the figure approximates the normal distribution in usual cases.
  • Each discrete price level on the Y-axis has a certain number of BTUs associated with it, which is a measure of the amount of time the market trades throughout the day at that price level.
  • Figures 3 and 4 illustrate exemplary embodiments for constructing frequency distribution graphs by the volume method.
  • the preferred time frame is daily and the price increment unit is 0.5.
  • the volume for each discrete price throughout the day is shown in the attached table of Figure 3.
  • Volume data is derived from volume and is expressed in number of shares. In other embodiments, if the security is a commodity or futures contract, the volume data may be expressed in dollar amounts of shares traded or number of contracts exchanged.
  • Figure 4 shows the resulting frequency distribution plot.
  • the Y-axis plots discrete price levels, and the X-axis plots volume at each price on the Y-axis. Figure 4 assumes that each "X" represents 1000 shares.
  • price 124 has 1000 volumes, so in the distribution chart in Figure 4, an "X" is marked on the price of 124.
  • price 123 has a volume of 2000, so in the distribution, two "X"s are marked at the price of 123.
  • Other entries in the table are drawn in the same manner on the distribution plot. In short, a repeated discussion of drawing the remaining entries is omitted.
  • the charting program uses the time or volume method to export the relevant distribution chart.
  • the charts derived from these two methods are highly correlated. This is because, all things being equal, the longer the market spends trading in price, the more volume will naturally be traded. However, this may not be the case for illiquid securities like small-cap stocks. Inactive stocks sometimes sit idle at the same price for most of the day with little or no trading volume. If this is the case, the time method will give wrong results.
  • the time method is preferable because real-time volumes for actively traded securities may not be precise. Users must decide which method to use for different securities.
  • Condensation points may sometimes exist with multiple price levels and maximum number of BTUs. If this is the case, the charting program defaults to displaying the one closest to the midpoint of the preferred bar. It is called the central condensation point. Alternatively, the graphing program can also be configured to display to the user all condensation points on a single bar.
  • each method produces a different set of condensation points than the other method.
  • the user will decide whether the displayed condensation points are calculated according to the time method or the volume method. It should be noted that, under normal circumstances, the modal point calculated by the time method should be close to the condensation point calculated by the volume method. This is because the more time the market spends at a certain price, naturally, the more volume is traded there.
  • each BTU represents a unit of frequency (either time or volume) at a particular price.
  • a frequency distribution map can be viewed as a collection of a population of BTUs, each with its own price.
  • the invention then calculates the mean and standard deviation of the population of BTU prices.
  • the valid range is then defined as the value of "mean ⁇ (standard deviation) (constant)", where the constant is predefined with a default value of 1.
  • the active range represents the price range on the bar chart that includes approximately 68% (standard deviation) of all trading activity, either by time or by volume.
  • the system reads the value of the constant from the parameter file Figure 1. In Fig.
  • the effective range is equal to ⁇ , which is equal to (121.79, 118.21). Since the active range accounts for 68% of trading activity, it can be considered the fair equilibrium value of the market, as it is the price range over which the total participants agree to trade across the trading range.
  • the present invention superimposes the discrete quantitative elements of time and quantity distribution on the regular price-time to calculate and generate the trading reference price index of financial market trading products, so as to accurately reflect the real-time market trading situation, avoid the phenomenon of price manipulation, and realize Accurate statistics and analysis of financial prices.
  • the present invention quantifies and superimposes the information in the market on the chart, and traders no longer need to observe and remember them by themselves, but can immediately retrieve them from the chart. Furthermore, it helps to analyze their time-series behavior and their relationship with common OHLC (opening price, highest price, lowest price and closing price), and then new trading insights can be formed more easily, providing people with accurate and reliable data.
  • OHLC open price, highest price, lowest price and closing price

Abstract

本发明是一种通过计算机实现的交易价格参考指标计算方法，该方法通过将时间和数量分布的离散量化元素叠加到常规价格？时间来计算产生金融市场交易产品的交易参考价格指标，以准确反应实时的市场交易情况，避免价格被操纵的现象，实现对金融价格的准确统计和分析。

Description

一种通过计算机实现的交易价格参考指标计算方法 技术领域
本发明涉及一种计算机实现的交易价格参考指标计算方法，该方法通过将时间和数量分布的离散量化元素叠加到常规价格-时间来计算产生金融市场交易产品的交易参考价格指标。
背景技术
对于每种金融产品(如外汇、股市、债券、资产交易、收购合并)和商品(如农产品、石油)，在市场开盘和收盘之间以及在高点和低点之间，都会发生许多其他活动和现象，这些活动和现象对于监视整体市场状况很有用。例如，市场活跃的区域，交易量最大的价格以及价格达到某个高价或低价附近时市场的表现。众所周知，这些市场内信息被广泛使用，尽管交易者和制定交易策略的分析师从常规数据和图表中看不到这些市场内信息，但它们扔被广泛使用，导致目前市场习惯是采用过去一个周期内市场收盘的价格的平均价作为市场的交易价格进行各种商业交易。
这种传统的交易价格定价逻辑存在以下两个问题：
1、价格容易被操控。因为是采用一个周期的收市价格的平均作为交易价格，而收市价格在收市结束前一刻是存在被人为拉高或者拉低做成刻意控制交易价格的目的。
2、不能反映市场交易的真实情况。市场真实价格应该是，交易量最大或者交易时间最长的交易价格，而不是收市价格。
发明内容
为解决上述问题，本发明的首要目的在于提供一种通过计算机实现的交易价格参考指标计算方法，该方法通过将时间和数量分布的离散量化元素叠加到常规价格-时间来计算产生金融市场交易产品的交易参考价格指标，以准确反应实时的市场交易情况，避免价格被操纵的现象，实现对金融价格的准确统计和分析。
本发明的另一目的在于提供一种通过计算机实现的交易价格参考指标计算方法，该方法是通过将常规价格-时间叠加在与不同价格上的时间/数量分布有关的市场内活动的离散量化元素上来对其进行扩展。
为实现上述目的，本发明的技术方案如下。
一种通过计算机实现的交易价格参考指标计算方法，该方法包括如下步骤：
101、以价格增量为基础构建频率分布图；其中，Y轴代表离散价格水平，X轴代表Y轴上每个价格的交易量；
102、从频率分布图中选取凝聚点，具有最大数量BTU的点，为凝聚点；
其中，BTU是基本时间单位，凝聚点是交易量最多的价格点。因此，这是市场花费最多时间或最多交易量的价格水平，称为凝聚点。
由于有时间和数量两种方法，因此每种方法都会产生一组凝聚点，而这些凝聚点可能彼此不同。用户将决定是根据时间方法还是根据数量方法计算显示的凝聚点。应当注意，在正常情况下，时间方法计算的凝聚点应近似于数量方法计算的凝聚点。这是因为，市场花在某个价格上的时间越长，自然地，它在那里的交易量就越大。
103、根据凝聚点计算活动范围的平均偏差，计算有效范围，以有效范围作为市场的公平均衡价值。找到包含连续的交易活动的相应的连续价格范围，称为有效范围，来确定公平的均衡价值。
本发明使用频率分布来计算有效范围的均值偏移方法。每个交易区间代表某个价格的频率单位(时间或交易数量)。因此，频率分布图可以视为一组交易的集合，每个交易具有各自的价格。然后，本发明计算交易区间价格总体的平均和标准偏差。由于交易区间的有效范围占交易活动的68％，因此可以将其视为市场的公平均衡值，因为该价格范围是参与者在整个小区间内同意交易的价格范围。
其中，步骤101中，先采用时间和价格建立分布表，依据分布表，建立条形图；再依据条形图，通过交易量方法构建频率分布图。
进一步，建频率分布图时，首选时间框架为每日，价格增量单位为0.5；先将全天每个离散价格的成交量绘制成频率分布表，其中，成交量数据来自于具体的成交量，并以股票数量表示；然后以Y轴代表离散价格水平，X轴代表Y轴上每个价格的交易量，绘制频率分布图。
所述交易量，是指股票交易额或美元交易额，当外汇等无法获取交易量的，以交易时间来衡量，可以是时间单位(如果使用时间方法)或数量单位(如果使用交易数量)。
“条形图”用于表示任何价格-时间图表上给定时间间隔的图形实体，无论它是条形图还是日式蜡烛图。
所述步骤103中，将有效范围定义为“平均值±(标准偏差)(常数)”的值，其中常数以默认值1预定义；依据公式有效范围＝μ±δ来计算有效范围，其中，μ是价格 的平均值，计算公式是其中n代表频率数量总和，f(x)＝价格(P)*频率(F)；δ是标准偏差，将有效范围视为市场的公平均衡价值。
本发明的有益效果在于：
本发明通过将时间和数量分布的离散量化元素叠加到常规价格-时间来计算产生金融市场交易产品的交易参考价格指标，以准确反应实时的市场交易情况，避免价格被操纵的现象，实现对金融价格的准确统计和分析。
在本发明之前，想要跟踪数量和时间分布信息的交易者必须手动进行。此外，它们没有一致的量化标准，仅依赖于粗略估计。根据本发明，通过将市场内信息量化并叠加在图表上，交易者不再需要自己观察和记住它们，而是可以从图表中立即检索它们。此外，有助于分析它们的时间序列行为以及它们与普通OHLC(开盘价，最高价，最低价和收盘价)的关系。然后可以更容易地形成新的交易见解，为人们提供准确、可靠的数据。
附图说明
图1是本发明所实现的时间-价格分布表。
图2是本发明所实现的时间-价格的条形图。
图3是本发明所实现的价格-成交量频率表。
图4是本发明所实现的价格-成交量频率分布图。
图5是本发明所实现的有效范围计算表。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
申请人研究发现，在金融和商品市场，收市价格平均价被交易者和分析师广泛使用，作为金融和商品交易时交易价格的计价手段。这种在给定的时间间隔内以收盘平均价作为交易价格被市场广泛使用。
但是，对于每种金融商品，在市场开市和收市之间以及在高点和低点之间，都会发生许多其他活动和现象，这些活动和现象对于监视总体市场状况很有用。例如，市场活跃的区域，交易量最大的价格以及价格达到某个高价或低价附近时市场的行为。众所周知，尽管制定交易策略的交易者和分析师无法从常规图表中看到这些市场内信息，但它们被广泛使用。
仅以收盘平均价格的常规计价方法显然无法提供基本市场情况的完整信息。价格从开盘价到收盘价的中间路径已被忽略。传统上，想要跟踪此类市场内信息的交易者必须依靠繁琐的手动过程，例如从报价屏幕观察价格波动并将信息记录到日志中，通过分析条形图中价格上花费的时间和交易量的分布来推导出该信息。例如，通过建立一个频率分布图来记录条形图上每个价格交易的时间/数量单位的数量，可以轻松地辨别诸如哪个价格范围包含高活跃度，低活跃度和大多数活跃度的信息。此外，可以基于该分布来计算各种统计参数。因此，申请人通过使用系统记录交易过程数据并实时运算形成了一种新型的、客观反映市场真实价格的交易价格参考指标“凝聚指标”。
频率分布图的建立。
如图1表格所示，先将时间与价格建立条形表，表中所示，第一行时间对应于9:30-10:00，其最高价为121，最低价为120。在图2中120、120.5和121在对应坐标都标有“X”。接下来，对应于10:00-10:30的第二行，第二个小节的最高价是122，最低价是120.5。因此，在120.5、121、121.5和122中的每一个对应坐标都会标记一个“X”。相同的过程重复图2中的其余数据，并且为简洁起见，这里不重复描述。
图2还表明，图中得到的价格分布近似于通常情况下的正态分布。Y轴上的每个离散价格水平都有一定数量的BTU与之关联，这是对市场在相应价格水平上全天交易的时间量的度量。
图3和图4示出了用于通过交易量方法构建频率分布图的示例性实施例。首选时间框架为每日，价格增量单位为0.5。全天每个离散价格的成交量如图3的附表所示。成交量数据来自于成交量，并以股票数量表示。在其他实施例中，如果证券是商品或期货合约，则交易量数据可以用交易的股票的美元金额或交换的合约数量来表示。图4显示了得到的频率分布图。Y轴绘制离散价格水平，X轴绘制Y轴上每个价格的交易量。图4假设每个“X”代表1000股。根据图3的表格，价格124有1000成交量，因此在图4的分布图中，一个“X”标记在124的价格上。同样，价格123有2000成交量，因此在分布图中，两个“X”标记在123的价格上。表中的其他条目在分布图上以相同的方式绘制。简而言之，省略了绘制其余条目的重复讨论。
图4中的分布图被故意构造成与图2中的完全相同，以便于随后的讨论。
用户可以自行选择图表程序是否使用时间或交易量方法导出相关的分布图。对于货币和指数期货等完全流动的证券，从这两种方法导出的图表高度相关。这是因为在所有条件都相同的情况下，市场花在价格交易上的时间越长，交易量自然就越大。然而，对 于像小盘股这样的非流动性证券来说，情况可能并非如此。不活跃的股票有时会在一天的大部分时间里以相同的价格闲置，交易量很少或没有交易量。如果是这样的话，时间方法会给出错误的结果。另一方面，对于流动性证券，时间方法更可取，因为活跃交易证券的实时交易量可能不精确。用户必须决定对不同的证券使用哪种方法。
凝聚指标的确定。
考虑如图2所示的频率分布图，如图2所示，价格120.5具有最大数量的BTU，其可以是时间单位(如果使用时间方法)或交易量单位(如果使用交易量方法)。因此，它是市场花费最多时间或交易量最多的价格水平。120.5称为凝聚点。
凝聚点有时可能存在多个价格水平和最多数量的BTU。如果是这种情况，则图表程序默认地显示最接近优选条的中点的一个。它被称为中心凝聚点。或者，图表程序也可以被配置成向用户显示单个条上的所有凝聚点。
由于获得频率分布图有时间和交易量两种方法，每种方法产生一组不同于另一种方法的凝聚点。用户将决定显示的凝聚点是根据时间法还是交易量方法计算。应注意的是，在正常情况下，时间法计算的模态点应近似于交易量法计算的凝聚点。这是因为市场在某个价格上花费的时间越多，自然，在那里的交易量就越大。
图2的频率分布图作为示例来计算活动范围的平均偏差方法。图中，每个BTU代表一个特定价格的频率单位(无论是时间还是交易量)。因此，频率分布图可以被视为BTU总体的集合，每个BTU具有各自的价格。然后，本发明计算BTU价格总体的平均值和标准差。然后将有效范围定义为“平均值±(标准偏差)(常数)”的值，其中常数以默认值1预定义。因此，在默认情况下，活跃区间表示条形图上的价格区间，包括所有交易活动的大约68％(标准差)，无论是按时间还是按交易量。系统从参数文件图1读取常数的值。在图5中，假设常数为1。因此，有效范围等于μ±δ，等于(121.79，118.21)。由于活跃区间占交易活动的68％，因此可以将其视为市场的公平均衡价值，因为它是总参与者同意在整个交易区间进行交易的价格区间。
总之，本发明通过将时间和数量分布的离散量化元素叠加到常规价格-时间来计算产生金融市场交易产品的交易参考价格指标，以准确反应实时的市场交易情况，避免价格被操纵的现象，实现对金融价格的准确统计和分析。
本发明通过将市场内信息量化并叠加在图表上，交易者不再需要自己观察和记住它们，而是可以从图表中立即检索它们。此外，有助于分析它们的时间序列行为以及它们 与普通OHLC(开盘价，最高价，最低价和收盘价)的关系，然后可以更容易地形成新的交易见解，为人们提供准确、可靠的数据。
以上仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。

Claims (4)

  1. 一种通过计算机实现的交易价格参考指标计算方法，该方法包括如下步骤：
    101、以价格增量为基础构建频率分布图；其中，Y轴代表离散价格水平，X轴代表Y轴上每个价格的交易量；
    102、从频率分布图中选取凝聚点，具有最大数量BTU的点，为凝聚点；
    其中，BTU是基本时间单位，凝聚点是交易量最多的价格点；
    103、根据凝聚点计算活动范围的平均偏差，计算有效范围，以有效范围作为市场的公平均衡价值；找到包含连续的交易活动的相应的连续价格范围，称为有效范围，来确定公平的均衡价值。
  2. 如权利要求1所述的通过计算机实现的交易价格参考指标计算方法，其特征在于步骤101中，先采用时间和价格建立分布表，依据分布表，建立条形图；再依据条形图，通过交易量方法构建频率分布图。
  3. 如权利要求2所述的通过计算机实现的交易价格参考指标计算方法，其特征在于，建频率分布图时，首选时间框架为每日，价格增量单位为0.5；先将全天每个离散价格的成交量绘制成频率分布表，其中，成交量数据来自于具体的成交量，并以股票数量表示；然后以Y轴代表离散价格水平，X轴代表Y轴上每个价格的交易量，绘制频率分布图。
  4. 如权利要求1所述的通过计算机实现的交易价格参考指标计算方法，其特征在于所述步骤103中，将有效范围定义为“平均值±(标准偏差)(常数)”的值，其中常数以默认值1预定义；依据公式有效范围＝μ±δ来计算有效范围，其中，μ是价格的平均值，计算公式是其中n代表频率数量总和，f(x)＝价格(P)*频率(F)；δ是标准偏差，将有效范围视为市场的公平均衡价值。
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