2026年大数据毕设新宠：基于Hadoop的茅台股票分析系统

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计算机毕设-小月哥 · 2025-08-26 13:35:35 发布

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基于大数据的贵州茅台股票数据分析系统-功能介绍

基于大数据的贵州茅台股票分析系统是一个集成了Hadoop分布式存储、Spark大数据处理技术的综合性股票数据分析平台。该系统采用Python作为主要开发语言，后端使用Django框架构建RESTful API接口，前端基于Vue+ElementUI+Echarts技术栈实现数据可视化展示。系统核心功能涵盖基础价格趋势分析、交易量与流动性分析、波动性与风险分析以及技术指标有效性分析四大模块，能够对茅台股票的日均价格走势、价格区间分布、涨跌幅特征、成交量变化、价量关系、波动率聚类、移动平均线信号、MACD买卖点等多维度数据进行深度挖掘分析。通过Spark SQL进行大规模数据查询优化，结合Pandas、NumPy进行统计计算，系统能够高效处理海量历史交易数据，为用户提供准确的技术分析结果和直观的图表展示，帮助投资者更好地理解茅台股票的市场表现规律和投资价值。

基于大数据的贵州茅台股票数据分析系统-选题背景意义

选题背景
随着金融市场数据规模的爆炸式增长，传统的股票分析方法已经难以满足海量数据处理和复杂分析的需求。贵州茅台作为A股市场的标志性蓝筹股，其股价表现不仅反映了公司自身的经营状况，更是整个消费板块乃至A股市场情绪的重要风向标。近年来茅台股价波动加剧，投资者对于深度数据分析的需求日益迫切，而传统Excel表格分析方式处理能力有限，无法应对大规模历史数据的统计分析需求。大数据技术的成熟为股票数据分析带来了新的解决思路，Hadoop生态系统能够高效存储和处理PB级别的金融数据，Spark内存计算框架显著提升了数据分析的速度和效率。在这样的技术背景下，构建一个基于大数据技术的茅台股票分析系统具有重要的现实意义和技术价值。

选题意义
本系统的构建具有多重实际意义和学术价值。从技术层面来看，该系统将大数据处理技术与金融数据分析相结合，为计算机专业学生提供了一个完整的大数据项目实践机会，有助于加深对Hadoop、Spark等核心技术的理解和应用能力。从应用角度来说，系统通过对茅台股票的多维度数据分析，能够为个人投资者和研究机构提供较为客观的技术分析参考，虽然不能直接指导投资决策，但可以作为投资分析的辅助工具。从教学角度考虑，该系统整合了数据存储、数据处理、数据分析、数据可视化等完整的数据科学流程，为相关专业的教学实践提供了一个相对完整的案例。从行业发展来看，这类系统的开发和应用推动了金融科技领域的技术创新，为传统金融分析方法的数字化转型提供了一定的参考价值，虽然作为毕业设计项目规模有限，但体现了大数据技术在金融领域应用的发展趋势。

基于大数据的贵州茅台股票数据分析系统-技术选型

大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
开发语言：Python+Java（两个版本都支持）
后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持）
前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery
详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy
数据库：MySQL

基于大数据的贵州茅台股票数据分析系统-视频展示

2026年大数据毕设新宠：基于Hadoop的茅台股票分析系统

基于大数据的贵州茅台股票数据分析系统-图片展示

在这里插入图片描述

基于大数据的贵州茅台股票数据分析系统-代码展示

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, stddev, corr, lag, when, abs as spark_abs
from pyspark.sql.window import Window
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views import View
import json

spark = SparkSession.builder.appName("MaotaiStockAnalysis").master("local[*]").getOrCreate()

class PriceTrendAnalysisView(View):
    def post(self, request):
        data = json.loads(request.body)
        start_date = data.get('start_date')
        end_date = data.get('end_date')
        stock_df = spark.read.option("header", "true").csv("hdfs://localhost:9000/maotai_stock_data.csv")
        stock_df = stock_df.filter((col("trade_date") >= start_date) & (col("trade_date") <= end_date))
        stock_df = stock_df.withColumn("daily_avg_price", (col("open_price").cast("double") + col("close_price").cast("double")) / 2)
        daily_trend = stock_df.select("trade_date", "daily_avg_price", "close_price").orderBy("trade_date")
        window_spec = Window.orderBy("trade_date").rowsBetween(-4, 0)
        stock_df = stock_df.withColumn("ma5", avg("close_price").over(window_spec))
        window_spec_20 = Window.orderBy("trade_date").rowsBetween(-19, 0)
        stock_df = stock_df.withColumn("ma20", avg("close_price").over(window_spec_20))
        price_volatility = stock_df.withColumn("daily_return", (col("close_price") - lag("close_price", 1).over(Window.orderBy("trade_date"))) / lag("close_price", 1).over(Window.orderBy("trade_date")))
        volatility_stats = price_volatility.select(avg("daily_return").alias("avg_return"), stddev("daily_return").alias("volatility")).collect()[0]
        price_ranges = stock_df.withColumn("price_range", 
            when(col("close_price") < 200, "低价区间")
            .when((col("close_price") >= 200) & (col("close_price") < 400), "中价区间")
            .when((col("close_price") >= 400) & (col("close_price") < 600), "高价区间")
            .otherwise("超高价区间"))
        range_distribution = price_ranges.groupBy("price_range").count().collect()
        breakthrough_days = stock_df.withColumn("is_breakthrough",
            when((col("close_price") > lag("high_price", 1).over(Window.orderBy("trade_date"))) |
                 (col("close_price") < lag("low_price", 1).over(Window.orderBy("trade_date"))), 1)
            .otherwise(0))
        breakthrough_count = breakthrough_days.filter(col("is_breakthrough") == 1).count()
        result_data = {
            "trend_data": [{"date": row["trade_date"], "price": float(row["daily_avg_price"]), "ma5": float(row["ma5"]) if row["ma5"] else None, "ma20": float(row["ma20"]) if row["ma20"] else None} for row in daily_trend.collect()],
            "volatility": {"avg_return": float(volatility_stats["avg_return"]) if volatility_stats["avg_return"] else 0, "volatility": float(volatility_stats["volatility"]) if volatility_stats["volatility"] else 0},
            "range_distribution": [{"range": row["price_range"], "count": row["count"]} for row in range_distribution],
            "breakthrough_days": breakthrough_count
        }
        return JsonResponse(result_data, safe=False)

class VolumeAnalysisView(View):
    def post(self, request):
        data = json.loads(request.body)
        start_date = data.get('start_date')
        end_date = data.get('end_date')
        stock_df = spark.read.option("header", "true").csv("hdfs://localhost:9000/maotai_stock_data.csv")
        stock_df = stock_df.filter((col("trade_date") >= start_date) & (col("trade_date") <= end_date))
        stock_df = stock_df.withColumn("volume", col("volume").cast("long"))
        stock_df = stock_df.withColumn("close_price", col("close_price").cast("double"))
        volume_trend = stock_df.select("trade_date", "volume").orderBy("trade_date")
        window_spec = Window.orderBy("trade_date").rowsBetween(-9, 0)
        stock_df = stock_df.withColumn("volume_ma10", avg("volume").over(window_spec))
        price_volume_corr = stock_df.select(corr("close_price", "volume").alias("correlation")).collect()[0]["correlation"]
        volume_stats = stock_df.select(avg("volume").alias("avg_volume"), stddev("volume").alias("volume_std")).collect()[0]
        volume_threshold = float(volume_stats["avg_volume"]) + 2 * float(volume_stats["volume_std"])
        abnormal_volume_days = stock_df.filter(col("volume") > volume_threshold)
        abnormal_data = abnormal_volume_days.select("trade_date", "volume", "close_price", "change_percent").collect()
        stock_df = stock_df.withColumn("volume_category",
            when(col("volume") < float(volume_stats["avg_volume"]) * 0.5, "低量")
            .when((col("volume") >= float(volume_stats["avg_volume"]) * 0.5) & (col("volume") < float(volume_stats["avg_volume"]) * 1.5), "正常")
            .when((col("volume") >= float(volume_stats["avg_volume"]) * 1.5) & (col("volume") < volume_threshold), "放量")
            .otherwise("巨量"))
        volume_distribution = stock_df.groupBy("volume_category").count().collect()
        liquidity_analysis = stock_df.withColumn("turnover_rate", col("volume") / 1000000000 * 100)
        avg_turnover = liquidity_analysis.select(avg("turnover_rate").alias("avg_turnover")).collect()[0]["avg_turnover"]
        result_data = {
            "volume_trend": [{"date": row["trade_date"], "volume": int(row["volume"]), "ma10": int(row["volume_ma10"]) if row["volume_ma10"] else None} for row in volume_trend.collect()],
            "price_volume_correlation": float(price_volume_corr) if price_volume_corr else 0,
            "abnormal_days": [{"date": row["trade_date"], "volume": int(row["volume"]), "price": float(row["close_price"]), "change": float(row["change_percent"]) if row["change_percent"] else 0} for row in abnormal_data],
            "volume_distribution": [{"category": row["volume_category"], "count": row["count"]} for row in volume_distribution],
            "avg_turnover_rate": float(avg_turnover) if avg_turnover else 0
        }
        return JsonResponse(result_data, safe=False)

class TechnicalIndicatorView(View):
    def post(self, request):
        data = json.loads(request.body)
        start_date = data.get('start_date')
        end_date = data.get('end_date')
        stock_df = spark.read.option("header", "true").csv("hdfs://localhost:9000/maotai_stock_data.csv")
        stock_df = stock_df.filter((col("trade_date") >= start_date) & (col("trade_date") <= end_date))
        stock_df = stock_df.withColumn("close_price", col("close_price").cast("double"))
        stock_df = stock_df.withColumn("high_price", col("high_price").cast("double"))
        stock_df = stock_df.withColumn("low_price", col("low_price").cast("double"))
        window_spec_12 = Window.orderBy("trade_date").rowsBetween(-11, 0)
        window_spec_26 = Window.orderBy("trade_date").rowsBetween(-25, 0)
        stock_df = stock_df.withColumn("ema12", avg("close_price").over(window_spec_12))
        stock_df = stock_df.withColumn("ema26", avg("close_price").over(window_spec_26))
        stock_df = stock_df.withColumn("macd_dif", col("ema12") - col("ema26"))
        window_spec_9 = Window.orderBy("trade_date").rowsBetween(-8, 0)
        stock_df = stock_df.withColumn("macd_dea", avg("macd_dif").over(window_spec_9))
        stock_df = stock_df.withColumn("macd_histogram", (col("macd_dif") - col("macd_dea")) * 2)
        stock_df = stock_df.withColumn("price_change", col("close_price") - lag("close_price", 1).over(Window.orderBy("trade_date")))
        stock_df = stock_df.withColumn("gain", when(col("price_change") > 0, col("price_change")).otherwise(0))
        stock_df = stock_df.withColumn("loss", when(col("price_change") < 0, spark_abs(col("price_change"))).otherwise(0))
        window_spec_14 = Window.orderBy("trade_date").rowsBetween(-13, 0)
        stock_df = stock_df.withColumn("avg_gain", avg("gain").over(window_spec_14))
        stock_df = stock_df.withColumn("avg_loss", avg("loss").over(window_spec_14))
        stock_df = stock_df.withColumn("rs", col("avg_gain") / col("avg_loss"))
        stock_df = stock_df.withColumn("rsi", 100 - (100 / (1 + col("rs"))))
        window_spec_20 = Window.orderBy("trade_date").rowsBetween(-19, 0)
        stock_df = stock_df.withColumn("sma20", avg("close_price").over(window_spec_20))
        stock_df = stock_df.withColumn("std20", stddev("close_price").over(window_spec_20))
        stock_df = stock_df.withColumn("upper_band", col("sma20") + 2 * col("std20"))
        stock_df = stock_df.withColumn("lower_band", col("sma20") - 2 * col("std20"))
        macd_signals = stock_df.withColumn("macd_signal",
            when((col("macd_dif") > col("macd_dea")) & (lag("macd_dif", 1).over(Window.orderBy("trade_date")) <= lag("macd_dea", 1).over(Window.orderBy("trade_date"))), "金叉")
            .when((col("macd_dif") < col("macd_dea")) & (lag("macd_dif", 1).over(Window.orderBy("trade_date")) >= lag("macd_dea", 1).over(Window.orderBy("trade_date"))), "死叉")
            .otherwise("持有"))
        signal_performance = macd_signals.filter(col("macd_signal").isin(["金叉", "死叉"])).collect()
        bollinger_breakouts = stock_df.filter((col("close_price") > col("upper_band")) | (col("close_price") < col("lower_band"))).count()
        rsi_extremes = stock_df.filter((col("rsi") > 70) | (col("rsi") < 30)).count()
        technical_data = stock_df.select("trade_date", "close_price", "macd_dif", "macd_dea", "macd_histogram", "rsi", "upper_band", "lower_band", "sma20").collect()
        result_data = {
            "technical_indicators": [{"date": row["trade_date"], "price": float(row["close_price"]), "macd_dif": float(row["macd_dif"]) if row["macd_dif"] else None, "macd_dea": float(row["macd_dea"]) if row["macd_dea"] else None, "macd_hist": float(row["macd_histogram"]) if row["macd_histogram"] else None, "rsi": float(row["rsi"]) if row["rsi"] else None, "bb_upper": float(row["upper_band"]) if row["upper_band"] else None, "bb_lower": float(row["lower_band"]) if row["lower_band"] else None, "bb_mid": float(row["sma20"]) if row["sma20"] else None} for row in technical_data],
            "signal_count": len(signal_performance),
            "bollinger_breakouts": bollinger_breakouts,
            "rsi_extreme_count": rsi_extremes
        }
        return JsonResponse(result_data, safe=False)