def onCollectTrace(self, bar: BarData) -> CollectData:
self.indicator.update_bar(bar)
self.lasted15Bar[:-1] = self.lasted15Bar[1:]
self.lasted3BarKdj[:-1] = self.lasted3BarKdj[1:]
self.lasted3BarMacd[:-1] = self.lasted3BarMacd[1:]
self.lasted3BarArron[:-1] = self.lasted3BarArron[1:]
k, d, j = self.indicator.kdj(fast_period=9, slow_period=3)
dif, dea, mBar = self.indicator.macd(fast_period=12,
slow_period=26,
signal_period=9)
aroon_down, aroon_up = self.indicator.aroon(n=14)
self.lasted15Bar[-1] = bar
self.lasted3BarKdj[-1] = [k, d, j]
self.lasted3BarMacd[-1] = [dif, dea, mBar]
self.lasted3BarArron[-1] = [aroon_down, aroon_up]
if self.indicator.count <= 15:
return None
#最近15天之内不含停牌数据
if not BarUtils.isAllOpen(self.lasted15Bar):
return None
#交易日天数间隔超过5天的数据
if BarUtils.getMaxIntervalDay(self.lasted15Bar) >= 5:
return None
timeKey = utils.to_start_date(bar.datetime)
if self.mDateOccurCountMap.get(timeKey) is None:
self.mDateOccurCountMap[timeKey] = 0
if self.indicator.count >= 30:
k0, d0, j0 = self.lasted3BarKdj[-2]
k1, d1, j1 = self.lasted3BarKdj[-1]
#金叉产生
goldCross = k0 < d0 and k1 >= d1
if not goldCross:
return None
kPatternValue = KPattern.encode3KAgo1(self.indicator)
if not kPatternValue is None:
self.mDateOccurCountMap[timeKey] += 1
dimen = Dimension(type=TYPE_2KAGO1, value=kPatternValue)
collectData = CollectData(dimen=dimen)
collectData.occurBars = list(self.lasted15Bar[-3:])
collectData.occurKdj = list(self.lasted3BarKdj)
collectData.occurExtra['lasted3BarMacd'] = self.lasted3BarMacd
collectData.occurExtra[
'lasted3BarArron'] = self.lasted3BarArron
return collectData
return None
def generateSWTrainData(kPatterns: [], start: datetime,
end: datetime) -> pd.DataFrame:
sw = SWImpl()
lists = sw.getSW2List()
cloumns = [
"code", "name", "kPattern", "k", "d", "dif", "dea", "macd", "open",
"short", "long"
]
datas = []
kPatternMap = {}
for kPatternValues in kPatterns:
kPatternMap[kPatternValues] = True
macd_list = []
for code in lists:
# for code in lists:
name = sw.getSw2Name(code)
barList = sw.getSW2Daily(code, start, end)
indicator = Indicator(34)
preBar = None
for bar in barList:
##先识别形态
kEncodeValue = None
if indicator.inited:
tmpKEncodeValue = KPattern.encode3KAgo1(indicator)
if kPatternMap.__contains__(tmpKEncodeValue):
kEncodeValue = tmpKEncodeValue
if kEncodeValue is None:
indicator.update_bar(bar)
preBar = bar
continue
##昨天的kdj
k, d, j = indicator.kdj(array=False)
dif, dea, macd = indicator.macd(fast_period=12,
slow_period=26,
signal_period=9,
array=False)
##第二天的收益
short_pct = 100 * ((bar.high_price + bar.close_price) / 2 -
preBar.close_price) / preBar.close_price
long_pct = 100 * ((bar.low_price + bar.close_price) / 2 -
preBar.close_price) / preBar.close_price
open_pct = 100 * (bar.open_price -
preBar.close_price) / preBar.close_price
item = []
item.append(code)
item.append(name)
item.append(kEncodeValue)
item.append(k)
item.append(d)
item.append(dif)
item.append(dea)
item.append(macd)
#下个k线数据
item.append(open_pct)
item.append(short_pct)
item.append(long_pct)
datas.append(item)
macd_list.append(macd)
indicator.update_bar(bar)
preBar = bar
macd_list = np.array(macd_list)
print(
f"total size : {len(datas)},mean ={macd_list.mean()},max={macd_list.max()},min={macd_list.min()}"
)
wxl = pd.DataFrame(datas, columns=cloumns)
return wxl