Question

我在csv文件中有一些历史交易日期，格式为：unixtime，price，volume我想分析这些数据。

我设法用Python做了，但是速度很慢（运行算法进行30天的数据测试需要2天左右）。

我正在尝试用c / c ++甚至Java或Scala来做，但我的主要问题是我无法重新采样数据。我需要将这些数据重新采样为以下格式：日期时间，开放，高，低，关闭，音量间隔15分钟，但我在c / c ++中找不到任何方法

在Python中，这可以实现我想要的（它使用pandas Dataframe）：

def resample_data(raw_data, time_frame):
    # resamples the ticker data in ohlc
    resampledData = raw_data.copy()
    ohlc_dict = {
        'open':'first',
        'high':'max',
        'low':'min',
        'close':'last',
        'price':'first'
        }

    resampledData = resampledData.resample(time_frame, how={'price':ohlc_dict, 'amount':'sum'})
    resampledData.amount = resampledData['amount']['sum'].fillna(0.0)
    resampledData['price']['close'] = resampledData['price']['close'].fillna(method='pad')
    resampledData = resampledData.apply(lambda x: x.fillna(resampledData['price']['close']))

    return resampledData

在c / c ++ / Java / scala中执行此操作的任何想法（或库）？

Answer 1

您可以使用标准Scala库快速举例说明。此代码可以在Scala REPL中运行：

// not importing external libraries like Joda time and its Scala wrappers
import java.util.Date
import scala.annotation.tailrec

case class Sample(value: Double, timeMillis: Long)
case class SampleAggregate(startTimeMillis: Long, endTimeMillis: Long,
  min: Sample, max: Sample)

val currentMillis = System.currentTimeMillis
val inSec15min = 15 * 60
val inMillis15min = inSec15min * 1000
// sample each second:
val data = (1 to inSec15min * 100).map { i =>
  Sample(i, currentMillis + i*1000) }.toList

@tailrec
def aggregate(xs: List[Sample], intervalDurationMillis: Long,
  accu: List[SampleAggregate]): List[SampleAggregate] =
  xs match {
    case h :: t =>
      val start = h.timeMillis
      val (slice, rest) = xs.span(_.timeMillis < (start + intervalDurationMillis))
      val end = slice.last.timeMillis
      val aggr = SampleAggregate(start, end, slice.minBy(_.value),
        slice.maxBy(_.value))
      aggregate(rest, intervalDurationMillis, aggr :: accu)
    case Nil =>
      accu.reverse
  }

val result = aggregate(data, inMillis15min, Nil)

虚假数据：

data.take(10).foreach(println)
Sample(1.0,1388809630677)
Sample(2.0,1388809631677)
Sample(3.0,1388809632677)
Sample(4.0,1388809633677)
Sample(5.0,1388809634677)
Sample(6.0,1388809635677)
Sample(7.0,1388809636677)
Sample(8.0,1388809637677)
Sample(9.0,1388809638677)
Sample(10.0,1388809639677)

结果：

result.foreach(println)
SampleAggregate(1388809630677,1388810529677,Sample(1.0,1388809630677),Sample(900.0,1388810529677))
SampleAggregate(1388810530677,1388811429677,Sample(901.0,1388810530677),Sample(1800.0,1388811429677))
SampleAggregate(1388811430677,1388812329677,Sample(1801.0,1388811430677),Sample(2700.0,1388812329677))
SampleAggregate(1388812330677,1388813229677,Sample(2701.0,1388812330677),Sample(3600.0,1388813229677))
SampleAggregate(1388813230677,1388814129677,Sample(3601.0,1388813230677),Sample(4500.0,1388814129677))
SampleAggregate(1388814130677,1388815029677,Sample(4501.0,1388814130677),Sample(5400.0,1388815029677))
SampleAggregate(1388815030677,1388815929677,Sample(5401.0,1388815030677),Sample(6300.0,1388815929677))

我们可以将一个函数传递给span，它将定义间隔（小时或天）。当从文件中读取时，也可以将其转换为Stream。

Answer 2

尝试查看Saddle进行数据操作。我自己刚刚发现了这个，所以我不确定它的全部功能，但它的灵感来自熊猫。

时间序列重新抽样历史贸易数据

2 个答案: