for (int price: prices)
时间: 2023-05-02 22:03:11 浏览: 207
Q: "for (int price: prices)" 是什么意思?
A: 这是一个 for 循环的语法结构,它的作用是将数组 prices 中的每个元素赋值给变量 price,然后执行循环体(即大括号 {} 中的语句)。在循环中,变量 price 的值会随着数组的遍历而改变。
相关问题
编写spark生成器生成字段title: String, author: String, price: Double, sales: Int, rating: Double, publishTime: String
这里是一个简单的 Spark 数据生成器,可以生成包含 `title`、`author`、`price`、`sales`、`rating` 和 `publishTime` 字段的数据。
```scala
import org.apache.spark.sql.{DataFrame, Row, SparkSession}
import java.sql.Timestamp
import scala.util.Random
object DataGenerator {
def generateData(spark: SparkSession, numRecords: Int): DataFrame = {
val titles = Seq("The Great Gatsby", "To Kill a Mockingbird", "1984", "Pride and Prejudice", "The Catcher in the Rye", "The Hobbit", "The Lord of the Rings", "Animal Farm", "The Diary of a Young Girl", "The Hitchhiker's Guide to the Galaxy")
val authors = Seq("F. Scott Fitzgerald", "Harper Lee", "George Orwell", "Jane Austen", "J.D. Salinger", "J.R.R. Tolkien", "George Orwell", "J.R.R. Tolkien", "Anne Frank", "Douglas Adams")
val prices = Seq(9.99, 12.99, 14.99, 19.99, 24.99, 29.99, 34.99, 39.99, 44.99, 49.99)
val sales = Seq(100, 200, 300, 400, 500, 600, 700, 800, 900, 1000)
val ratings = Seq(3.5, 4.0, 4.5, 5.0)
val dates = Seq("2021-01-01", "2021-02-01", "2021-03-01", "2021-04-01", "2021-05-01", "2021-06-01", "2021-07-01", "2021-08-01", "2021-09-01", "2021-10-01")
val random = new Random()
val data = (1 to numRecords).map { i =>
val title = titles(random.nextInt(titles.length))
val author = authors(random.nextInt(authors.length))
val price = prices(random.nextInt(prices.length))
val sale = sales(random.nextInt(sales.length))
val rating = ratings(random.nextInt(ratings.length))
val date = Timestamp.valueOf(dates(random.nextInt(dates.length)) + s" ${random.nextInt(24)}:${random.nextInt(60)}:${random.nextInt(60)}")
Row(title, author, price, sale, rating, date.toString)
}
val schema = spark.sqlContext.createDataFrame(Seq.empty[Row]).schema
.add("title", "string")
.add("author", "string")
.add("price", "double")
.add("sales", "int")
.add("rating", "double")
.add("publishTime", "string")
spark.createDataFrame(data, schema)
}
}
```
调用 `generateData` 函数即可生成指定数量的数据,例如:
```scala
val numRecords = 1000
val data = DataGenerator.generateData(spark, numRecords)
data.show()
```
import jqdata # 初始化聚宽账号密码 def initialize(context): # 设置回测日期区间 set_benchmark('000300.XSHG') set_option('use_real_price', True) # 设置买入的股票数量上限 g.max_stock_count = 5 def handle_data(context, data): # 获取当前日期 current_date = context.current_dt.date() # 获取股票池中的股票列表 stocks = get_index_stocks('000852.XSHG') # 按照股票池中的股票进行遍历 for stock in stocks: # 判断股票是否满足买入条件 if check_buy_condition(stock, current_date, context): buy_stock(stock, context) # 判断持有的股票是否满足卖出条件 if check_sell_condition(stock, current_date, context): sell_stock(stock, context) def check_buy_condition(stock, current_date, context): # 判断股票是否连续下跌三天 prices = attribute_history(stock,3, 'close', skip_paused=True) if len(prices) == 3 and prices['close'][-1] < prices['close'][-2] < prices['close'][-3]: return True else: return False def buy_stock(stock, context): # 判断当前持仓的股票数量是否已达上限 if len(context.portfolio.positions) >= g.max_stock_count: return # 买入股票 order_value(stock, context.portfolio.cash / g.max_stock_count) def check_sell_condition(stock, current_date, context): # 获取持有股票的买入日期 buy_date = context.portfolio.positions[stock].init_time.date() # 判断是否满足卖出条件 if current_date - buy_date >= 3: # 判断是否亏损超过5% if (context.portfolio.positions[stock].last_price - context.portfolio.positions[stock].avg_cost) / context.portfolio.positions[stock].avg_cost <= -0.05: return True return False def sell_stock(stock, context): # 卖出股票 order_target(stock, 0) 报错count = int(unit[:-1]) ValueError: invalid literal for int() with base 10: 'clos'
这个错误是因为在获取股票价格时,使用了错误的列名。在`attribute_history()`函数中,第三个参数应该是列名,而不是'close'。请将`attribute_history(stock,3, 'close', skip_paused=True)`修改为`attribute_history(stock, 3, 'close', skip_paused=True).close`。这样就可以正确获取股票的收盘价了。