public static List <ITimeLineData> ConvertTimeLineDataList(IKLineData data, float lastEndPrice, ITradingDayReader openDateReader)
{
List <SplitterResult> splitResult = DaySplitter.Split(data, openDateReader);
List <ITimeLineData> realdataList = new List <ITimeLineData>(splitResult.Count);
for (int i = 0; i < splitResult.Count; i++)
{
SplitterResult split = splitResult[i];
int date = split.Date;
int todayStartIndex = split.Index;
int todayEndIndex;
if (i == splitResult.Count - 1)
{
todayEndIndex = data.Length - 1;
}
else
{
todayEndIndex = splitResult[i + 1].Index;
}
int len = todayEndIndex - todayStartIndex + 1;
TimeLineData r = new TimeLineData(lastEndPrice, len);
r.Code = data.Code;
Convert2RealData(data, todayStartIndex, todayEndIndex, r);
realdataList.Add(r);
}
return(realdataList);
}
public static void FindBestRaterWeights()
{
using (StreamWriter w = new StreamWriter("rater-weights.csv", true))
{
w.WriteLine("down,up,view,dl,rmse,solved,total,precision,rank");
}
var once = new User(0, "");
var options = new List <dynamic>();
for (double up = 0.0; up < 5.0; up += 1)
{
for (double down = -5.0; down <= 0.0; down += 1)
{
for (double dl = 0.0; dl < 5.0; dl += 1)
{
for (double view = 0.0; view < 5.0; view += 1)
{
options.Add(new { up, down, dl, view });
}
}
}
}
var dbp = new UserBehaviorDatabaseParser();
var db = dbp.LoadUserBehaviorDatabase("UserBehaviour.txt");
var sp = new DaySplitter(db, 3);
var cp = new CorrelationUserComparer();
Parallel.ForEach(options, set =>
{
try
{
var rate = new LinearRater(set.down, set.up, set.view, set.dl);
var mfr = new MatrixFactorizationRecommender(20, rate);
//var mfr = new UserCollaborativeFilterRecommender(cp, rate, set.features);
mfr.Train(sp.TrainingDB);
var score = mfr.Score(sp.TestingDB, rate);
var results = mfr.Test(sp.TestingDB, 100);
lock (once)
{
using (StreamWriter w = new StreamWriter("rater-weights.csv", true))
{
w.WriteLine(set.down + "," + set.up + "," + set.view + "," + set.dl + "," + score.RootMeanSquareDifference + "," + results.UsersSolved + "," + results.TotalUsers + "," + results.AveragePrecision + "," + results.AverageRecall);
}
}
}
catch (Exception ex)
{
File.WriteAllText("errors.txt", ex.ToString());
}
});
}
public static void TestAllRecommenders()
{
UserBehaviorDatabaseParser dbp = new UserBehaviorDatabaseParser();
UserBehaviorDatabase db = dbp.LoadUserBehaviorDatabase("UserBehaviour.txt");
//var ubt = new UserBehaviorTransformer(db);
//var uart = ubt.GetUserArticleRatingsTable();
//uart.SaveSparcityVisual("sparcity.bmp");
//uart.SaveUserRatingDistribution("distrib.csv");
//uart.SaveArticleRatingDistribution("distriba.csv");
var rate = new LinearRater();
var sp = new DaySplitter(db, 5);
var uc = new CorrelationUserComparer();
//var rr = new RandomRecommender();
//rr.Train(sp.TrainingDB);
//ScoreResults scores5 = rr.Score(sp.TestingDB, rate);
//TestResults results5 = rr.Test(sp.TestingDB, 30);
var ubc = new UserCollaborativeFilterRecommender(uc, rate, 30);
var mfr = new MatrixFactorizationRecommender(30, rate);
var icf = new ItemCollaborativeFilterRecommender(uc, rate, 30);
var hbr = new HybridRecommender(ubc, mfr, icf);
hbr.Train(sp.TrainingDB);
ScoreResults scores1 = hbr.Score(sp.TestingDB, rate);
TestResults results1 = hbr.Test(sp.TestingDB, 30);
ubc = new UserCollaborativeFilterRecommender(uc, rate, 30);
mfr = new MatrixFactorizationRecommender(30, rate);
icf = new ItemCollaborativeFilterRecommender(uc, rate, 30);
ubc.Train(sp.TrainingDB);
ScoreResults scores2 = ubc.Score(sp.TestingDB, rate);
TestResults results2 = ubc.Test(sp.TestingDB, 30);
mfr.Train(sp.TrainingDB);
ScoreResults scores3 = mfr.Score(sp.TestingDB, rate);
TestResults results3 = mfr.Test(sp.TestingDB, 30);
icf.Train(sp.TrainingDB);
ScoreResults scores4 = icf.Score(sp.TestingDB, rate);
TestResults results4 = icf.Test(sp.TestingDB, 30);
using (StreamWriter w = new StreamWriter("results.csv"))
{
w.WriteLine("model,rmse,users,user-solved,precision,recall");
w.WriteLine("UCF," + scores2.RootMeanSquareDifference + "," + results2.TotalUsers + "," + results2.UsersSolved + "," + results2.AveragePrecision + "," + results2.AverageRecall);
w.WriteLine("SVD," + scores3.RootMeanSquareDifference + "," + results3.TotalUsers + "," + results3.UsersSolved + "," + results3.AveragePrecision + "," + results3.AverageRecall);
w.WriteLine("ICF," + scores4.RootMeanSquareDifference + "," + results4.TotalUsers + "," + results4.UsersSolved + "," + results4.AveragePrecision + "," + results4.AverageRecall);
w.WriteLine("HR," + scores1.RootMeanSquareDifference + "," + results1.TotalUsers + "," + results1.UsersSolved + "," + results1.AveragePrecision + "," + results1.AverageRecall);
}
}
public int GetOpenDate(double time)
{
int date = (int)time;
if (!DaySplitter.IsNight(time))
{
return(date);
}
return(openDateCache.GetNextOpenDate(date));
}
/// <summary>
/// 将k线转换成日线
/// </summary>
/// <param name="data"></param>
/// <param name="targetPeriod"></param>
/// <param name="timeSplit"></param>
/// <returns></returns>
public static IKLineData Transfer_Day(IKLineData data, KLinePeriod targetPeriod)
{
List<SplitterResult> results = DaySplitter.Split(data);
List<KLineBar> charts = new List<KLineBar>(results.Count);
for (int i = 0; i < results.Count; i++)
{
int startIndex = results[i].Index;
int endIndex = (i == results.Count - 1) ? data.Length - 1 : results[i + 1].Index - 1;
charts.Add(GetChart_Day(data, startIndex, endIndex));
}
KLineData klineData = GetKLineData(charts);
klineData.Period = targetPeriod;
return klineData;
}
public void ChangeTime(double time)
{
if (currentTime == time)
{
return;
}
//比如 20150531140500
//1分钟线可以正常显示
//日线需要将
int date = DaySplitter.GetTimeDate(time, dataCache_Code.GetOpenDateReader());
if (currentDate != date)
{
//TODO 将以前生成的klineChartBuilder_1Minute cache下来
this.currentDataCache_CodeDate = dataCache_Code.GetCache_CodeDate(date);
this.klineChartBuilder_1Minute = new RealTimeDataBuilder_DayData(currentDataCache_CodeDate, time);
this.currentDate = date;
}
int index = klineData.IndexOfTime(time);
double t = klineData.Arr_Time[index];
IKLineData todayMinuteKLineData = klineChartBuilder_1Minute.MinuteKlineData;
KLineBar currentMinuteChart = klineChartBuilder_1Minute.GetCurrentChart();
//TODO 这里最多只支持到日线，两日线或以上不支持
int startIndex;
if (t == (int)t)
{
startIndex = 0;
}
else
{
startIndex = todayMinuteKLineData.IndexOfTime(t);
}
int endIndex = todayMinuteKLineData.IndexOfTime(currentMinuteChart.Time);
IKLineBar currentChart = todayMinuteKLineData.GetAggrKLineBar(startIndex, endIndex - 1);
KLineBarMerge.MergeTo((KLineBar)currentChart, currentMinuteChart);
this.currentChart = currentChart;
this.currentTime = time;
}
public void DaySplitterTest(int days, int items, int shouldItemsPerDay)
{
//Arrange
var splitter = new DaySplitter();
var testCollection = TestEntityGenerator
.Generate(TimeRange.Create(DateTime.Now.Date.AddHours(12).AddDays(-days), DateTime.Now.Date.AddHours(12)), items);
//Act
var splitted = splitter.Split(testCollection).ToList();
//Assert
var timeRanges = splitted.Select(x => x.Key);
var itemsPerDay = splitted.First().Value.Count();
var daysAreEqual = splitted.All(x => x.Value.Count() == itemsPerDay);
Assert.That(timeRanges.Count(), Is.EqualTo(days));
Assert.That(itemsPerDay, Is.EqualTo(shouldItemsPerDay));
Assert.That(daysAreEqual, Is.True);
}
public void DoIndex()
{
if (!File.Exists(path))
{
return;
}
String indexPath = path + ".index";
KLineDataStore_File_Single store = new KLineDataStore_File_Single(path);
IKLineData klineData = store.LoadAll();
List <SplitterResult> results = DaySplitter.Split(klineData);
List <String> indeies = new List <string>(results.Count);
for (int i = 0; i < results.Count; i++)
{
indeies.Add(results[i].ToString());
}
File.WriteAllLines(indexPath, indeies.ToArray());
//FileStream file = new FileStream(path, FileMode.Open);
//try
//{
//indeies.Add(((int)time).ToString() + "," + currentIndex);
//File.WriteAllLines(indexPath, indeies.ToArray());
//double lastTime = GetTimeByIndex(file, 0);
//double time = GetTimeByIndex(file, 1);
////KLinePeriod period = KLineData.GetPeriod(lastTime, time);
////算法
//int len = GetLength(file);
//int currentIndex = 0;
//bool hasNight = false;
//for (int index = 1; index < len; index++)
//{
// time = GetTimeByIndex(file, index);
// int date = (int)time;
// int lastDate = (int)lastTime;
// //夜盘开始，则一定是新的一天开始
// if (IsNightStart(time, lastTime))
// {
// indeies.Add(((int)lastTime).ToString() + "," + currentIndex.ToString());
// currentIndex = index;
// hasNight = true;
// }
// else if (hasNight)
// {
// //对于夜盘来说，如果到了第二天，则说明夜盘结束了,此时不算新的一天开始
// if (date != lastDate)
// hasNight = false;
// }
// //只要过了夜都算第二天的
// else if (date != lastDate)
// {
// indeies.Add(((int)lastTime).ToString() + "," + currentIndex.ToString());
// currentIndex = index;
// }
// lastTime = time;
//}
//}
//finally
//{
// //file.Close();
//}
}
public KLineData_DaySplitter(IKLineData klineData, ITradingSessionReader_Instrument tradingSessionReader)
{
this.results = DaySplitter.Split(klineData, tradingSessionReader);
}
public KLineData_DaySplitter(IKLineData klineData, ITradingTimeReader_Code tradingSessionReader)
{
this.results = DaySplitter.Split(klineData, tradingSessionReader);
}
