给出由Spark Structured Streaming编写的实现代码

好的，以下是使用Spark Structured Streaming实现你所述分析流程的代码示例:

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *

# 创建SparkSession
spark = SparkSession.builder.appName("dj30_analysis").getOrCreate()

# 设定流的输入数据源，这里使用Kafka作为数据源
df = spark.readStream.format("kafka") \
    .option("kafka.bootstrap.servers", "localhost:9092") \
    .option("subscribe", "dj30_data") \
    .load()

# 解析数据
schema = StructType() \
    .add("symbol", StringType()) \
    .add("date", StringType()) \
    .add("price", DoubleType())

df_parsed = df.selectExpr("CAST(value AS STRING)") \
    .select(from_json("value", schema).alias("data")) \
    .select("data.*")

# 计算移动平均线
dj30sum = df_parsed.groupBy("symbol", window("date", "10 days")) \
    .agg(sum("price").alias("dj30sum"))

dj30ct = df_parsed.groupBy("symbol", window("date", "10 days")) \
    .agg(count("price").alias("dj30ct"))

dj30avg = dj30sum.join(dj30ct, ["symbol", "window"]) \
    .withColumn("dj30avg", col("dj30sum") / col("dj30ct"))

# 比较移动平均线，产生买卖信号
dj30avg_10 = dj30avg.filter(col("window.end") == "10 days") \
    .withColumnRenamed("dj30avg", "dj30avg_10")

dj30avg_40 = dj30avg.filter(col("window.end") == "40 days") \
    .withColumnRenamed("dj30avg", "dj30avg_40")

dj30_signal = dj30avg_10.join(dj30avg_40, "symbol") \
    .filter(col("dj30avg_10") > col("dj30avg_40")) \
    .withColumn("signal", lit("buy")) \
    .select("symbol", "window.end", "signal")

dj30_signal = dj30_signal.union(dj30avg_10.join(dj30avg_40, "symbol") \
    .filter(col("dj30avg_10") < col("dj30avg_40")) \
    .withColumn("signal", lit("sell")) \
    .select("symbol", "window.end", "signal"))

# 将结果写入目标存储
query = dj30_signal.writeStream \
    .format("console") \
    .outputMode("append") \
    .start()

query.awaitTermination()

这段代码中，我们首先读取了Kafka作为数据源的流，并解析了数据，然后使用窗口函数和聚合函数计算了移动平均线。接下来，我们比较了短期移动平均线和长期移动平均线，产生了买卖信号。最后，我们将结果写入到控制台中。你可以根据自己的需求修改输出方式，比如将结果写入到Kafka或HDFS中。