大目金枪鱼交易价格预测数据

一、适用范围与对象：
适用于大宗水产品现货交易市场，涵盖大目金枪鱼等高价值及交易活跃的水产品品类。其设计兼顾了内部交易数据与外部市场行情，具有高度的行业通用性；
核心服务对象为水产品贸易企业、加工企业、大型采购商及渔业合作社。这些主体需进行频繁的大宗采购或销售，对价格趋势有敏锐的洞察需求。
二、解决的问题：
本数据旨在解决水产品交易中因价格波动剧烈、市场信息不对称导致的采购与销售决策难题。它通过算法规则预测交易价格，有效降低了人为经验判断的偏差和滞后性，帮助企业应对价格不确定性风险。
三、核心价值点：
其核心价值在于构建了一个透明、客观、数据驱动的价格发现机制。数据融合了实时市场情绪（市场平均价）、当期交易共识（交易平均价）和真实交易比重（加权平均价），能生成比单一价格指标更科学、更可靠的预测值，助力企业优化库存管理、锁定采购成本、提升谈判竞争力。
四、外部复用价值：
本数据具备显著的外部复用潜力。对于行业协会或政府监管部门，可作为发布官方价格指数、监测市场行情的核心工具；对于金融机构，可为水产品存货融资、价格保险等金融产品提供公允的价格参考基准，赋能产业链金融发展。一、数据采集：
采集交易明细表中大目金枪鱼的交易数据，具体采集字段包括商品名称、交易时间、周、月、年、规格、单价（元/吨）、交易吨数（吨）、交易金额（元）、市场均价（元/吨）；
新增算法字段：当月交易平均价（元/吨）、当月交易总量（吨）、当月交易总金额（元）、当月交易加权平均价（元/吨）、预测下月交易价格（元/吨）。
二、数据处理：
异常值过滤：设定交易金额的合理上下限，超出设定此范围的数据视为异常记录，予以剔除或标记复核；
将单价（元/吨）、交易吨数（吨）、交易金额（元）、当月交易平均价（元/吨）、当月交易总量（吨）、当月交易总金额（元）、当月交易加权平均价（元/吨）、市场均价（元/吨）、预测下月交易价格（元/吨）这些字段的数据保留3位小数处理。
三、核心算法规则：
当月交易笔数（笔）：当月大目金枪鱼交易的笔数总和
当月交易平均价（元/吨）：当月每笔大目金枪鱼交易的单价总和/当月交易笔数
当月交易总量（吨）：当月每笔大目金枪鱼交易的吨数总和
当月交易总金额（元）：当月大目金枪鱼交易的总金额
当月交易加权平均价（元/吨）：当月交易总金额/当月交易总量
预测下月交易价格（元/吨）=市场均价×0.5+当月交易平均价×0.3+当月交易加权平均价×0.2

I. Scope and Target Applicants:
This dataset is applicable to bulk aquatic products spot trading markets, covering high-value and actively traded aquatic product categories such as Bigeye Tuna. Its design integrates both internal transaction data and external market trends, featuring high industry versatility. Its core target users include aquatic product trading enterprises, processing enterprises, large-scale purchasers and fishery cooperatives. These entities engage in frequent bulk purchasing or sales, and have a pressing need for keen insights into price trends.

II. Solved Problems:
This dataset aims to address the difficulties in procurement and sales decision-making caused by drastic price fluctuations and asymmetric market information in aquatic product trading. It predicts transaction prices via algorithmic rules, effectively reducing the biases and lags of manual experience-based judgments, and helping enterprises cope with the risks of price uncertainty.

III. Core Value Points:
Its core value lies in establishing a transparent, objective, data-driven price discovery mechanism. The dataset integrates real-time market sentiment (market average price), current transaction consensus (transaction average price) and real transaction weight (weighted average price), which can generate more scientific and reliable forecast values than a single price indicator, helping enterprises optimize inventory management, lock in procurement costs and enhance negotiation competitiveness.

IV. External Reusability Value:
This dataset has significant external reuse potential. For industry associations or government regulatory authorities, it can serve as a core tool for releasing official price indices and monitoring market trends. For financial institutions, it can provide a fair price reference benchmark for financial products such as aquatic product inventory financing and price insurance, empowering the development of industrial chain finance.

V. Data Collection:
Collect transaction data of Bigeye Tuna from transaction detail sheets, with specific collected fields including: commodity name, transaction time, week, month, year, specification, unit price (yuan/ton), transaction volume (ton), transaction amount (yuan), market average price (yuan/ton).
Newly added algorithmic fields include: monthly transaction average price (yuan/ton), monthly total transaction volume (ton), monthly total transaction amount (yuan), monthly transaction weighted average price (yuan/ton), and forecasted next-month transaction price (yuan/ton).

VI. Data Processing:
Outlier filtering: Set reasonable upper and lower limits for transaction amount; data exceeding this range are regarded as abnormal records and will be eliminated or marked for review.
Retain 3 decimal places for data in the following fields: unit price (yuan/ton), transaction volume (ton), transaction amount (yuan), monthly transaction average price (yuan/ton), monthly total transaction volume (ton), monthly total transaction amount (yuan), monthly transaction weighted average price (yuan/ton), market average price (yuan/ton), and forecasted next-month transaction price (yuan/ton).

VII. Core Algorithmic Rules:
1. Monthly transaction count (times): Total number of Bigeye Tuna transactions in the current month.
2. Monthly transaction average price (yuan/ton) = Sum of unit prices of each Bigeye Tuna transaction in the current month / Monthly transaction count.
3. Monthly total transaction volume (ton) = Sum of transaction volumes of each Bigeye Tuna transaction in the current month.
4. Monthly total transaction amount (yuan) = Total transaction amount of Bigeye Tuna in the current month.
5. Monthly transaction weighted average price (yuan/ton) = Monthly total transaction amount / Monthly total transaction volume.
6. Forecasted next-month transaction price (yuan/ton) = Market average price × 0.5 + Monthly transaction average price × 0.3 + Monthly transaction weighted average price × 0.2

搜集汇总

背景与挑战

背景概述

该数据集是大目金枪鱼交易价格预测数据，包含527条记录，每月更新，涵盖交易时间、规格、单价、交易量等16个字段。其核心特点是利用算法规则预测下月交易价格，结合市场均价、交易平均价和加权平均价，旨在解决水产品交易中的价格波动和信息不对称问题，适用于贸易企业、金融机构等优化决策和风险管理。