东南太平洋鱿鱼统货交易价格预测数据

一、适用范围与对象：
适用于大宗水产品现货交易市场，涵盖东南太平洋鱿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
数据采集是价格预测模型的基础，多元化和高质量的数据是预测准确性的关键保障。
数据来源：公司内部的浙农产业数据云系统中的交易明细表模块。
采集字段：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
数据处理旨在提升数据质量，为模型训练提供干净、一致的数据集。
（1）数据清洗与异常值处理：
规则过滤：设定字段的合理上下限，超出范围的数据标记为异常。
统计过滤：对于单价、交易吨数等连续数值字段，使用3σ法则识别并处理极端异常值。
（2）数据转换与标准化：
精度统一：将所有金额类、重量类字段统一保留3位小数。
三、核心算法规则
核心算法分为统计模块和预测模块。统计模块用于描述当前市场状态，预测模块用于展望未来价格。
统计模块（用于计算当月指标）：
输入：经过处理的所有当月东南太平洋鱿鱼规格为统货的交易记录。
输出：
当月交易笔数（笔）：当月总交易记录数。
当月交易总量（吨）：当月交易吨数之和。
当月交易总金额（元）：当月交易金额之和。
当月交易平均价（元/吨）：当月所有交易记录单价总和的算术平均值。公式：当月交易平均价=当月所有东南太平洋鱿鱼交易记录单价总和/当月交易笔数
当月交易加权平均价（元/吨）：以交易量为权重的平均价，更能反映市场真实成本。公式： 当月交易加权平均价=当月交易总金额/当月交易总量
预测模块（用于预测下月交易价格）：
公式：预测下月交易价格= 0.5×市场均价+0.3×当月交易平均价+0.2×当月交易加权平均价

1. Scope and Application Objects
This dataset is applicable to bulk aquatic products spot trading markets, covering high-value and actively-traded aquatic product categories such as southern Pacific squid. Its design integrates both internal transaction data and external market trends, featuring high industry universality.
The core target users include aquatic product trading enterprises, processing enterprises, large-scale purchasers and fishery cooperatives. These entities conduct frequent bulk procurement or sales activities, and have a strong demand for sharp insights into price trends.

2. Problems Solved
This dataset aims to address the challenges in procurement and sales decision-making caused by drastic price fluctuations and asymmetric market information in aquatic product trading. It predicts transaction prices through algorithmic rules, effectively reducing deviations and lags caused by human empirical judgment, and helping enterprises cope with price uncertainty risks.

3. 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. This helps enterprises optimize inventory management, lock in procurement costs and enhance negotiation competitiveness.

4. External Reusability Value
This dataset has significant external reusability 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.

I. Data Collection
Data collection is the foundation of price prediction models, and diversified and high-quality data are key guarantees for prediction accuracy.
Data Source: Transaction Detail Module in the company's internal Zheinnong Industrial Data Cloud System.
Collected Fields: Commodity name, transaction time, week, month, year, specification, unit price (yuan/ton), transaction tonnage (ton), transaction amount (yuan), market average price (yuan/ton);
New Algorithm-generated Fields: Monthly transaction average price (yuan/ton), monthly total transaction tonnage (ton), monthly total transaction amount (yuan), monthly transaction weighted average price (yuan/ton), predicted next-month transaction price (yuan/ton).

II. Data Processing
Data processing aims to improve data quality and provide a clean and consistent dataset for model training.
(1) Data Cleaning and Outlier Handling:
Rule Filtering: Set reasonable upper and lower limits for fields, and mark data beyond the range as abnormal.
Statistical Filtering: For continuous numerical fields such as unit price and transaction tonnage, use the 3σ rule to identify and handle extreme outliers.
(2) Data Conversion and Standardization:
Uniform Precision: Uniformly retain 3 decimal places for all amount and weight fields.

III. Core Algorithm Rules
The core algorithm is divided into a statistics module and a prediction module. The statistics module is used to describe the current market state, and the prediction module is used to forecast future prices.
Statistics Module (for calculating monthly indicators):
Input: Processed transaction records of southern Pacific squid (specification: ungraded cargo) in the current month.
Output:
- Number of monthly transactions (transactions): Total number of transaction records in the month.
- Total monthly transaction tonnage (ton): Sum of transaction tonnages in the month.
- Total monthly transaction amount (yuan): Sum of transaction amounts in the month.
- Monthly transaction average price (yuan/ton): Arithmetic mean of the unit prices of all transaction records in the month. Formula: Monthly transaction average price = Sum of unit prices of all southern Pacific squid transaction records in the month / Number of monthly transactions
- Monthly transaction weighted average price (yuan/ton): Average price weighted by transaction volume, which better reflects the real market cost. Formula: Monthly transaction weighted average price = Total monthly transaction amount / Total monthly transaction tonnage

Prediction Module (for forecasting next-month transaction price):
Formula: Predicted next-month transaction price = 0.5 × Market average price + 0.3 × Monthly transaction average price + 0.2 × Monthly transaction weighted average price