💖💖作者：计算机编程小咖 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
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客户股票交易教学系统介绍
《客户股票交易教学系统的设计与实现》是一套专门为股票交易教学而开发的综合性管理平台，采用先进的B/S架构设计，支持Java+SpringBoot和Python+Django两种技术实现方案，前端采用Vue+ElementUI框架构建现代化用户界面，后端通过MySQL数据库确保数据的稳定存储和高效查询。该系统围绕股票交易教学的核心需求，构建了完整的教学管理生态，包含学员管理、讲师管理等人员管理模块，股票信息管理、股票分类管理、课程信息管理等教学资源管理功能，持仓股票管理、卖出股票管理等模拟交易操作模块，以及风险评估管理、投资建议管理、评估试题管理等专业教学辅助功能。系统还集成了股票论坛和论坛分类管理，为师生提供互动交流平台，通过弹窗提醒管理、举报记录管理确保平台运行秩序，在线评估管理和评估测试管理模块则实现了教学效果的量化考核。整个系统功能模块设计合理，技术架构稳定可靠，既满足了股票交易教学的专业性要求，又保证了系统的易用性和可维护性，为计算机专业学生提供了一个技术含量高、实用价值强的优质毕业设计项目选择。
客户股票交易教学系统演示视频
客户股票交易教学系统演示图片
客户股票交易教学系统代码展示
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.functions;
import org.springframework.stereotype.Service;
@Service
public class StockTradingService {
private SparkSession spark = SparkSession.builder().appName("StockTradingSystem").master("local[*]").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate();
@Autowired
private StockMapper stockMapper;
@Autowired
private HoldingMapper holdingMapper;
@Autowired
private RiskAssessmentMapper riskAssessmentMapper;
public ResultVO<Object> processStockDataAnalysis(Long studentId) {
try {
List<Stock> stockList = stockMapper.selectAllStocks();
Dataset<Row> stockDataset = spark.createDataFrame(stockList, Stock.class);
Dataset<Row> filteredData = stockDataset.filter(stockDataset.col("status").equalTo(1)).filter(stockDataset.col("price").gt(0));
Dataset<Row> priceAnalysis = filteredData.groupBy("category_id").agg(functions.avg("price").alias("avg_price"), functions.max("price").alias("max_price"), functions.min("price").alias("min_price"), functions.count("id").alias("stock_count"));
List<Row> analysisResult = priceAnalysis.collectAsList();
Map<String, Object> resultMap = new HashMap<>();
for (Row row : analysisResult) {
Long categoryId = row.getAs("category_id");
Double avgPrice = row.getAs("avg_price");
Double maxPrice = row.getAs("max_price");
Double minPrice = row.getAs("min_price");
Long stockCount = row.getAs("stock_count");
Map<String, Object> categoryAnalysis = new HashMap<>();
categoryAnalysis.put("averagePrice", avgPrice);
categoryAnalysis.put("maxPrice", maxPrice);
categoryAnalysis.put("minPrice", minPrice);
categoryAnalysis.put("stockCount", stockCount);
categoryAnalysis.put("priceVolatility", (maxPrice - minPrice) / avgPrice);
resultMap.put("category_" + categoryId, categoryAnalysis);
}
Dataset<Row> studentHoldings = filteredData.filter(filteredData.col("holder_id").equalTo(studentId));
if (studentHoldings.count() > 0) {
Row holdingStats = studentHoldings.agg(functions.sum("quantity").alias("total_quantity"), functions.sum(functions.col("price").multiply(functions.col("quantity"))).alias("total_value")).first();
resultMap.put("studentHoldingValue", holdingStats.getAs("total_value"));
resultMap.put("studentHoldingQuantity", holdingStats.getAs("total_quantity"));
}
return ResultVO.ok(resultMap);
} catch (Exception e) {
return ResultVO.fail("股票数据分析处理失败: " + e.getMessage());
}
}
public ResultVO<Object> executeHoldingOperation(HoldingStock holdingStock) {
try {
Stock stock = stockMapper.selectById(holdingStock.getStockId());
if (stock == null || stock.getStatus() != 1) {
return ResultVO.fail("股票信息不存在或已停用");
}
if (holdingStock.getQuantity() <= 0) {
return ResultVO.fail("持仓数量必须大于0");
}
BigDecimal totalCost = stock.getPrice().multiply(new BigDecimal(holdingStock.getQuantity()));
if (totalCost.compareTo(new BigDecimal("1000000")) > 0) {
return ResultVO.fail("单次持仓金额不能超过100万元");
}
HoldingStock existingHolding = holdingMapper.selectByStudentAndStock(holdingStock.getStudentId(), holdingStock.getStockId());
if (existingHolding != null) {
int newQuantity = existingHolding.getQuantity() + holdingStock.getQuantity();
BigDecimal newTotalValue = stock.getPrice().multiply(new BigDecimal(newQuantity));
existingHolding.setQuantity(newQuantity);
existingHolding.setHoldingValue(newTotalValue);
existingHolding.setUpdateTime(new Date());
holdingMapper.updateById(existingHolding);
} else {
holdingStock.setHoldingValue(totalCost);
holdingStock.setHoldingTime(new Date());
holdingStock.setStatus(1);
holdingMapper.insert(holdingStock);
}
List<HoldingStock> allHoldings = holdingMapper.selectByStudentId(holdingStock.getStudentId());
Dataset<Row> holdingsDataset = spark.createDataFrame(allHoldings, HoldingStock.class);
Dataset<Row> riskAnalysis = holdingsDataset.groupBy("student_id").agg(functions.sum("holding_value").alias("total_portfolio_value"), functions.count("stock_id").alias("diversification_count"), functions.stddev("holding_value").alias("value_volatility"));
Row riskResult = riskAnalysis.first();
Double portfolioValue = riskResult.getAs("total_portfolio_value");
Long diversificationCount = riskResult.getAs("diversification_count");
Double volatility = riskResult.getAs("value_volatility");
String riskLevel = calculateRiskLevel(portfolioValue, diversificationCount, volatility);
Map<String, Object> operationResult = new HashMap<>();
operationResult.put("holdingSuccess", true);
operationResult.put("portfolioValue", portfolioValue);
operationResult.put("riskLevel", riskLevel);
operationResult.put("diversificationScore", diversificationCount);
return ResultVO.ok(operationResult);
} catch (Exception e) {
return ResultVO.fail("持仓操作处理失败: " + e.getMessage());
}
}
public ResultVO<Object> generateRiskAssessment(Long studentId, Map<String, Object> assessmentData) {
try {
List<RiskAssessment> historicalAssessments = riskAssessmentMapper.selectByStudentId(studentId);
Dataset<Row> assessmentDataset = spark.createDataFrame(historicalAssessments, RiskAssessment.class);
Dataset<Row> riskTrends = assessmentDataset.orderBy(functions.col("assessment_date").desc()).limit(12);
List<Row> trendData = riskTrends.collectAsList();
double riskTolerance = calculateRiskTolerance(assessmentData);
double investmentExperience = calculateExperienceScore(assessmentData);
double financialCapacity = calculateFinancialScore(assessmentData);
double compositeScore = (riskTolerance * 0.4 + investmentExperience * 0.3 + financialCapacity * 0.3);
String riskProfile = determineRiskProfile(compositeScore);
List<HoldingStock> currentHoldings = holdingMapper.selectByStudentId(studentId);
Dataset<Row> holdingsAnalysis = spark.createDataFrame(currentHoldings, HoldingStock.class);
Dataset<Row> portfolioMetrics = holdingsAnalysis.agg(functions.sum("holding_value").alias("total_value"), functions.count("stock_id").alias("stock_count"), functions.avg("holding_value").alias("avg_holding"));
Row metricsResult = portfolioMetrics.first();
Double totalValue = metricsResult.getAs("total_value");
Long stockCount = metricsResult.getAs("stock_count");
Double avgHolding = metricsResult.getAs("avg_holding");
List<String> recommendations = generateInvestmentAdvice(riskProfile, totalValue, stockCount, compositeScore);
RiskAssessment newAssessment = new RiskAssessment();
newAssessment.setStudentId(studentId);
newAssessment.setRiskScore(compositeScore);
newAssessment.setRiskLevel(riskProfile);
newAssessment.setAssessmentDate(new Date());
newAssessment.setRecommendations(String.join(";", recommendations));
riskAssessmentMapper.insert(newAssessment);
Map<String, Object> assessmentResult = new HashMap<>();
assessmentResult.put("riskProfile", riskProfile);
assessmentResult.put("riskScore", compositeScore);
assessmentResult.put("portfolioValue", totalValue);
assessmentResult.put("diversificationLevel", stockCount);
assessmentResult.put("recommendations", recommendations);
assessmentResult.put("trendAnalysis", analyzeTrends(trendData));
return ResultVO.ok(assessmentResult);
} catch (Exception e) {
return ResultVO.fail("风险评估生成失败: " + e.getMessage());
}
}
private String calculateRiskLevel(Double portfolioValue, Long diversificationCount, Double volatility) {
if (volatility == null) volatility = 0.0;
if (portfolioValue < 10000 || diversificationCount < 3) return "高风险";
if (portfolioValue > 100000 && diversificationCount > 10 && volatility < 5000) return "低风险";
return "中风险";
}
private double calculateRiskTolerance(Map<String, Object> data) {
int age = (Integer) data.getOrDefault("age", 25);
String riskPreference = (String) data.getOrDefault("riskPreference", "moderate");
double baseScore = Math.max(0, Math.min(100, 100 - (age - 20) * 2));
switch (riskPreference.toLowerCase()) {
case "aggressive": return baseScore * 1.2;
case "conservative": return baseScore * 0.6;
default: return baseScore;
}
}
private double calculateExperienceScore(Map<String, Object> data) {
int yearsExperience = (Integer) data.getOrDefault("experience", 0);
String educationLevel = (String) data.getOrDefault("education", "undergraduate");
double baseScore = Math.min(100, yearsExperience * 15 + 20);
if ("graduate".equals(educationLevel)) baseScore += 15;
if ("doctorate".equals(educationLevel)) baseScore += 25;
return baseScore;
}
private double calculateFinancialScore(Map<String, Object> data) {
double annualIncome = ((Number) data.getOrDefault("income", 0)).doubleValue();
double monthlyExpense = ((Number) data.getOrDefault("expense", 0)).doubleValue();
double netWorth = ((Number) data.getOrDefault("netWorth", 0)).doubleValue();
double incomeScore = Math.min(40, annualIncome / 1000);
double expenseRatio = monthlyExpense * 12 / Math.max(annualIncome, 1);
double expenseScore = Math.max(0, 30 - expenseRatio * 30);
double wealthScore = Math.min(30, netWorth / 10000);
return incomeScore + expenseScore + wealthScore;
}
private String determineRiskProfile(double score) {
if (score >= 80) return "激进型";
if (score >= 60) return "积极型";
if (score >= 40) return "稳健型";
if (score >= 20) return "保守型";
return "非常保守型";
}
private List<String> generateInvestmentAdvice(String riskProfile, Double totalValue, Long stockCount, Double score) {
List<String> advice = new ArrayList<>();
if (stockCount < 5) advice.add("建议增加投资组合的多样化，持有至少5-8只不同类型的股票");
if (totalValue < 50000 && "激进型".equals(riskProfile)) advice.add("可以考虑增加高成长性股票的配置比例");
if (totalValue > 200000 && "保守型".equals(riskProfile)) advice.add("建议配置部分稳健型蓝筹股降低组合风险");
if (score < 30) advice.add("建议加强投资理论学习，提升风险识别能力");
advice.add("定期重新评估投资组合，根据市场变化调整策略");
return advice;
}
private Map<String, Object> analyzeTrends(List<Row> trendData) {
Map<String, Object> trends = new HashMap<>();
if (trendData.size() < 2) {
trends.put("trend", "数据不足");
return trends;
}
double latestScore = trendData.get(0).getAs("risk_score");
double previousScore = trendData.get(1).getAs("risk_score");
double scoreDiff = latestScore - previousScore;
trends.put("scoreChange", scoreDiff);
trends.put("trend", scoreDiff > 5 ? "风险偏好上升" : scoreDiff < -5 ? "风险偏好下降" : "风险偏好稳定");
trends.put("assessmentCount", trendData.size());
return trends;
}
}
客户股票交易教学系统文档展示
💖💖作者：计算机编程小咖 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
