如果有股票的每日开盘价,收盘价,最高价,最低价,日期,用springboot算出MACD,返回每日的MACD数据带日期,并优化算法
时间: 2023-08-03 17:06:52 浏览: 67
MACD是一种趋势指标,用于分析股票价格的变化趋势。在Spring Boot中,可以通过编写自定义算法来计算每日的MACD数据,并返回带有日期的数据。
下面是一个简单的实现方法:
1. 定义一个实体类来存储每日的股票价格数据,包括开盘价、收盘价、最高价、最低价和日期等字段。
```java
public class StockData {
private Double openPrice;
private Double closePrice;
private Double highPrice;
private Double lowPrice;
private Date date;
// getter and setter methods
}
```
2. 编写一个算法来计算每日的MACD数据。MACD的计算需要用到指数移动平均线(EMA),可以通过递归的方式来计算。具体代码实现如下:
```java
public class MacdCalculator {
private static final int SHORT_PERIOD = 12;
private static final int LONG_PERIOD = 26;
private static final int SIGNAL_PERIOD = 9;
public List<MacdData> calculate(List<StockData> stockDataList) {
List<MacdData> macdDataList = new ArrayList<>();
double shortEma = 0;
double longEma = 0;
double signalEma = 0;
double prevSignalEma = 0;
for (int i = 0; i < stockDataList.size(); i++) {
StockData stockData = stockDataList.get(i);
// 计算短期EMA
if (i == 0) {
shortEma = stockData.getClosePrice();
} else {
shortEma = calculateEma(stockData.getClosePrice(), shortEma, SHORT_PERIOD);
}
// 计算长期EMA
if (i == 0) {
longEma = stockData.getClosePrice();
} else {
longEma = calculateEma(stockData.getClosePrice(), longEma, LONG_PERIOD);
}
// 计算DIF
double dif = shortEma - longEma;
// 计算信号线
if (i == 0) {
signalEma = dif;
} else {
signalEma = calculateEma(dif, prevSignalEma, SIGNAL_PERIOD);
}
prevSignalEma = signalEma;
// 计算MACD
double macd = dif - signalEma;
MacdData macdData = new MacdData();
macdData.setDate(stockData.getDate());
macdData.setDif(dif);
macdData.setSignal(signalEma);
macdData.setMacd(macd);
macdDataList.add(macdData);
}
return macdDataList;
}
private double calculateEma(double currentPrice, double prevEma, int period) {
double k = 2.0 / (period + 1);
return currentPrice * k + prevEma * (1 - k);
}
}
```
3. 在Spring Boot中,可以将算法封装成一个RESTful API服务,以便于其他系统调用。具体实现可以参考以下代码:
```java
@RestController
public class MacdController {
@Autowired
private MacdCalculator macdCalculator;
@PostMapping("/macd")
public List<MacdData> calculateMacd(@RequestBody List<StockData> stockDataList) {
return macdCalculator.calculate(stockDataList);
}
}
```
4. 为了优化算法的性能,可以考虑使用并发计算来加速MACD数据的计算。可以使用Java的并发框架来实现。具体实现可以参考以下代码:
```java
public class ParallelMacdCalculator {
private static final int SHORT_PERIOD = 12;
private static final int LONG_PERIOD = 26;
private static final int SIGNAL_PERIOD = 9;
public List<MacdData> calculate(List<StockData> stockDataList) {
List<MacdData> macdDataList = new ArrayList<>();
double shortEma = 0;
double longEma = 0;
double signalEma = 0;
double prevSignalEma = 0;
int numProcessors = Runtime.getRuntime().availableProcessors();
ExecutorService executorService = Executors.newFixedThreadPool(numProcessors);
List<Future<MacdData>> futures = new ArrayList<>();
for (int i = 0; i < stockDataList.size(); i++) {
StockData stockData = stockDataList.get(i);
// 计算短期EMA
if (i == 0) {
shortEma = stockData.getClosePrice();
} else {
shortEma = calculateEma(stockData.getClosePrice(), shortEma, SHORT_PERIOD);
}
// 计算长期EMA
if (i == 0) {
longEma = stockData.getClosePrice();
} else {
longEma = calculateEma(stockData.getClosePrice(), longEma, LONG_PERIOD);
}
// 计算DIF
double dif = shortEma - longEma;
// 计算信号线
if (i == 0) {
signalEma = dif;
} else {
signalEma = calculateEma(dif, prevSignalEma, SIGNAL_PERIOD);
}
prevSignalEma = signalEma;
// 计算MACD
double macd = dif - signalEma;
Future<MacdData> future = executorService.submit(() -> {
MacdData macdData = new MacdData();
macdData.setDate(stockData.getDate());
macdData.setDif(dif);
macdData.setSignal(signalEma);
macdData.setMacd(macd);
return macdData;
});
futures.add(future);
}
for (Future<MacdData> future : futures) {
try {
macdDataList.add(future.get());
} catch (InterruptedException | ExecutionException e) {
e.printStackTrace();
}
}
executorService.shutdown();
return macdDataList;
}
private double calculateEma(double currentPrice, double prevEma, int period) {
double k = 2.0 / (period + 1);
return currentPrice * k + prevEma * (1 - k);
}
}
```
这样,我们就可以使用Spring Boot来实现一个计算股票MACD的应用,并通过并发计算来提高算法的性能。
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