def __computeRangeFloatList(self,
pct_list: [],
encoder: FloatEncoder,
sort=True) -> Sequence['FloatRange']:
rangeCount = {}
totalCount = len(pct_list)
for i in range(0, encoder.mask()):
rangeCount[i] = 0
for pct in pct_list:
encode = encoder.encode(pct)
rangeCount[encode] += 1
rangeList = []
for encode, count in rangeCount.items():
probal = 0.0
if totalCount > 0:
probal = count / totalCount
floatRange = FloatRange(encode=encode, probal=probal)
rangeList.append(floatRange)
if sort:
return FloatRange.sort(rangeList)
return rangeList
class More_detail_KPattern_skip1_predit2(Find_KPattern_skip1_predit2):
def __init__(self, limit_close_pct=1, kPatters: [] = None):
Find_KPattern_skip1_predit2.__init__(self,
limit_close_pct=limit_close_pct)
self.kPatters = kPatters
self.pct_split = [-7, -5, -3, -1.0, 0, 1, 3, 5, 7]
self.pctEncoder = FloatEncoder(self.pct_split)
self.kPattersMap = {}
self.allTradyDayCount = 0
self.allTradeDay = 0
self.occurDayMap = {}
for value in kPatters:
self.kPattersMap[value] = True
pass
def onCreate(self):
super().onCreate()
self.occurDayMap.clear()
self.allTradyDayCount = 0
self.allTradeDay = 0
def onStart(self, code: str) -> bool:
self.allTradyDayCount = 0
return Find_KPattern_skip1_predit2.onStart(self, code)
def onEnd(self, code: str):
Find_KPattern_skip1_predit2.onEnd(self, code)
self.allTradeDay = max(self.allTradeDay, self.allTradyDayCount)
def collect(self, bar: BarData) -> TraceData:
traceData = Find_KPattern_skip1_predit2.collect(self, bar)
self.allTradyDayCount += 1
if not traceData is None:
kPatternValue = traceData.kPatternValue
if self.kPattersMap.get(kPatternValue) is None:
##过滤
return None
return traceData
def newCountData(self) -> CountData:
data = Find_KPattern_skip1_predit2.newCountData(self)
data.sell_disbute = np.zeros(self.pctEncoder.mask()) ##卖方力量分布情况
data.buy_disbute = np.zeros(self.pctEncoder.mask()) # 买方力量分布情况
return data
def doWantedTraceData(self, traceData: Skip1_Predict2_TraceData,
countData: CountData):
Find_KPattern_skip1_predit2.doWantedTraceData(self, traceData,
countData)
sell_pct = traceData.sell_pct
buy_pct = traceData.buy_pct
##统计买卖双方的分布情况
countData.buy_disbute[self.pctEncoder.encode(buy_pct)] += 1
countData.sell_disbute[self.pctEncoder.encode(sell_pct)] += 1
occurBar = traceData.occurBar
dayKey = occurBar.datetime.year * 13 * 35 + occurBar.datetime.month * 13 + occurBar.datetime.day
self.occurDayMap[dayKey] = True
pass
def onDestroy(self):
Find_KPattern_skip1_predit2.onDestroy(self)
if not self.print_on_destroy:
return
print(f"所有交易日中，有意义的k线形态出现占比：%.2f%%，allTradeDay = { self.allTradeDay}" %
(100 * len(self.occurDayMap) / self.allTradeDay))
for kValue, dataItem in self.dataSet.items():
total_count1 = 0
total_count2 = 0
for cnt in dataItem.sell_disbute:
total_count1 += cnt
for cnt in dataItem.buy_disbute:
total_count2 += cnt
assert total_count1 == total_count2
assert total_count1 > 0
print(f"k线形态值:%6d， " % (kValue))
print(f" 卖方价格分布：")
info = ""
for encode in range(0, len(dataItem.sell_disbute)):
occurtRate = 100 * dataItem.sell_disbute[encode] / total_count1
info += f"{self.pctEncoder.descriptEncdoe(encode)}：%.2f%%," % (
occurtRate)
print(f" {info}")
print(f" 买方价格分布：")
info = ""
for encode in range(0, len(dataItem.buy_disbute)):
occurtRate = 100 * dataItem.buy_disbute[encode] / total_count1
info += f"{self.pctEncoder.descriptEncdoe(encode)}：%.2f%%," % (
occurtRate)
print(f" {info}")
def printKPatterMoreDetail(kPatters=[
535, 359, 1239, 1415, 1072, 712, 1412, 1240, 1413, 888, 2823, 706, 1414,
1064
]):
from vnpy.trader.constant import Exchange
from vnpy.trader.constant import Interval
sw = SWImpl()
lists = sw.getSW2List()
start = datetime(2014, 5, 1)
end = datetime(2020, 8, 17)
pct_split = [-7, -5, -3, -1.5, -0.5, 0.5, 1.5, 3, 5, 7]
#pct_split = [-7, -5, -3, -1.0, 0, 1, 3, 5, 7]
pct_split = [2]
pctEncoder = FloatEncoder(pct_split)
kPattersMap = {}
for value in kPatters:
kPattersMap[value] = True
class InnerData(object):
kValue: int ##
sell_disbute = np.zeros(pctEncoder.mask()) ##卖方力量分布情况
buy_disbute = np.zeros(pctEncoder.mask()) #买方力量分布情况
pass
dataSet = {}
occurDayMap = {}
allTrayDay = 1
for code in lists:
# for code in lists:
barList = sw.getSW2Daily(code, start, end)
indicator = Indicator(40)
traceItems: ['TraceIn3DayItem'] = []
allTrayDay = max(allTrayDay, len(barList))
for bar in barList:
###跟踪数据
toDeleteList = []
for traceItem in traceItems:
traceItem.onTraceBar(bar)
if traceItem.isFinished():
toDeleteList.append(traceItem)
if traceItem.isWanted():
occurBar = traceItem.firstBar
dayKey = occurBar.datetime.year * 13 * 35 + occurBar.datetime.month * 13 + occurBar.datetime.day
occurDayMap[dayKey] = True
###归纳到统计里面
innerData: InnerData = dataSet.get(traceItem.kPattern)
if innerData is None:
innerData = InnerData()
innerData.kValue = traceItem.kPattern
dataSet[traceItem.kPattern] = innerData
sell_pct = traceItem.current_sell_pct
buy_pct = traceItem.current_buy_pct
innerData.buy_disbute[pctEncoder.encode(buy_pct)] += 1
innerData.sell_disbute[pctEncoder.encode(
sell_pct)] += 1
pass
for traceItem in toDeleteList:
traceItems.remove(traceItem)
indicator.update_bar(bar)
kEncodeValue = KPattern.encode2KAgo1(indicator)
if kEncodeValue is None or kPattersMap.get(kEncodeValue) is None:
continue
traceItem = TraceIn3DayItem(kEncodeValue, bar)
traceItems.append(traceItem)
print(f"所有交易日中，有意义的k线形态出现占比：%.2f%%" %
(100 * len(occurDayMap) / allTrayDay))
for kValue, dataItem in dataSet.items():
total_count1 = 0
total_count2 = 0
for cnt in dataItem.sell_disbute:
total_count1 += cnt
for cnt in dataItem.buy_disbute:
total_count2 += cnt
assert total_count1 == total_count2
assert total_count1 > 0
print(f"\n\nk:%6d, " % (kValue))
print(f" 卖方价格分布：")
for encode in range(0, len(dataItem.sell_disbute)):
occurtRate = 100 * dataItem.sell_disbute[encode] / total_count1
print(f" {pctEncoder.descriptEncdoe(encode)}：%.2f%%" %
(occurtRate))
print(f" 买方价格分布：")
for encode in range(0, len(dataItem.buy_disbute)):
occurtRate = 100 * dataItem.buy_disbute[encode] / total_count1
print(f" {pctEncoder.descriptEncdoe(encode)}：%.2f%%" %
(occurtRate))
pass
class EngineModel2KAlgo2(CoreEngineModel):
def __init__(self):
self.lasted3Bar = np.array([None, None, None])
self.lasted3BarKdj = np.array([None, None, None])
self.kdjEncoder = FloatEncoder([15, 30, 45, 60, 75, 90])
self.sw = SWImpl()
def onCollectStart(self, code: str) -> bool:
from earnmi.chart.Indicator import Indicator
self.indicator = Indicator(40)
self.code = code
return True
def onCollectTrace(self, bar: BarData) -> CollectData:
self.indicator.update_bar(bar)
self.lasted3Bar[:-1] = self.lasted3Bar[1:]
self.lasted3BarKdj[:-1] = self.lasted3BarKdj[1:]
k, d, j = self.indicator.kdj(fast_period=9, slow_period=3)
self.lasted3Bar[-1] = bar
self.lasted3BarKdj[-1] = [k, d, j]
if self.indicator.count >= 20:
from earnmi.chart.KPattern import KPattern
kPatternValue = KPattern.encode2KAgo1(self.indicator)
if not kPatternValue is None:
_kdj_mask = self.kdjEncoder.mask()
kPatternValue = kPatternValue * _kdj_mask * _kdj_mask + self.kdjEncoder.encode(
k) * _kdj_mask + self.kdjEncoder.encode(d)
dimen = Dimension(type=TYPE_2KAGO1, value=kPatternValue)
collectData = CollectData(dimen=dimen)
collectData.occurBars.append(self.lasted3Bar[-2])
collectData.occurBars.append(self.lasted3Bar[-1])
collectData.occurKdj.append(self.lasted3BarKdj[-2])
collectData.occurKdj.append(self.lasted3BarKdj[-1])
return collectData
return None
def onCollect(self, data: CollectData, newBar: BarData) -> bool:
if len(data.occurBars) < 3:
data.occurBars.append(self.lasted3Bar[-1])
data.occurKdj.append(self.lasted3BarKdj[-1])
else:
data.predictBars.append(newBar)
size = len(data.predictBars)
return size >= 2
@abstractmethod
def getYLabelPrice(self, cData: CollectData) -> [float, float, float]:
bars: ['BarData'] = cData.predictBars
if len(bars) > 0:
sell_price = -9999999999
buy_price = -sell_price
for bar in bars:
sell_price = max((bar.high_price + bar.close_price) / 2,
sell_price)
buy_price = min((bar.low_price + bar.close_price) / 2,
buy_price)
return sell_price, buy_price
return None, None
def getYBasePrice(self, cData: CollectData) -> float:
return cData.occurBars[-2].close_price
def generateXFeature(self, cData: CollectData) -> []:
#保证len小于三，要不然就不能作为生成特征值。
if (len(cData.occurBars) < 3):
return None
occurBar = cData.occurBars[-2]
skipBar = cData.occurBars[-1]
kdj = cData.occurKdj[-1]
sell_pct = 100 * ((skipBar.high_price + skipBar.close_price) / 2 -
occurBar.close_price) / occurBar.close_price
buy_pct = 100 * ((skipBar.low_price + skipBar.close_price) / 2 -
occurBar.close_price) / occurBar.close_price
def set_0_between_100(x):
if x > 100:
return 100
if x < 0:
return 0
return x
def percent_to_one(x):
return int(x * 100) / 1000.0
data = []
data.append(percent_to_one(buy_pct))
data.append(percent_to_one(sell_pct))
data.append(set_0_between_100(kdj[0]) / 100)
data.append(set_0_between_100(kdj[2]) / 100)
return data
def printKPatterMoreDetail(kPatters=[
6, 3, 17, 81, 7, 5, 4, 82, 159, 16, 28, 83, 15, 84, 18, 27, 93, 104, 158,
92, 160, 236, 157, 94, 85, 80, 14, 8, 161, 9, 29, 170, 26, 19, 38, 2, 79
]):
from earnmi.data.SWImpl import SWImpl
from vnpy.trader.constant import Exchange
from vnpy.trader.constant import Interval
sw = SWImpl()
lists = sw.getSW2List()
start = datetime(2014, 5, 1)
end = datetime(2020, 8, 17)
pct_split = [-7, -5, -3, -1.5, -0.5, 0.5, 1.5, 3, 5, 7]
pct_split = [-7, -5, -3, -1.0, 0, 1, 3, 5, 7]
pct_split = [-0.5, 0.5]
pctEncoder = FloatEncoder(pct_split)
kPattersMap = {}
for value in kPatters:
kPattersMap[value] = True
class InnerData(object):
kValue: int ##
sell_disbute = np.zeros(pctEncoder.mask()) ##卖方力量分布情况
buy_disbute = np.zeros(pctEncoder.mask()) #买方力量分布情况
pass
dataSet = {}
occurDayMap = {}
allTrayDay = 1
for code in lists:
# for code in lists:
barList = sw.getSW2Daily(code, start, end)
indicator = Indicator(40)
preBar = None
previousIsMatch = False
previousPatternVaule = None
allTrayDay = max(allTrayDay, len(barList))
for i in range(0, len(barList)):
bar = barList[i]
indicator.update_bar(bar)
patternValue = KPattern.encode1KAgo1(indicator)
todayIsMatch = False
if not patternValue is None:
todayIsMatch = kPattersMap.__contains__(patternValue)
if todayIsMatch:
dayKey = bar.datetime.year * 13 * 35 + bar.datetime.month * 13 + bar.datetime.day
occurDayMap[dayKey] = True
pass
if previousIsMatch:
innerData: InnerData = dataSet.get(previousIsMatch)
if innerData is None:
innerData = InnerData()
innerData.kValue = previousIsMatch
dataSet[previousPatternVaule] = innerData
sell_pct = 100 * ((bar.high_price + bar.close_price) / 2 -
preBar.close_price) / preBar.close_price
buy_pct = 100 * ((bar.low_price + bar.close_price) / 2 -
preBar.close_price) / preBar.close_price
innerData.buy_disbute[pctEncoder.encode(buy_pct)] += 1
innerData.sell_disbute[pctEncoder.encode(sell_pct)] += 1
pass
preBar = bar
previousIsMatch = todayIsMatch
previousPatternVaule = patternValue
print(f"所有交易日中，有意义的k线形态出现占比：%.2f%%" %
(100 * len(occurDayMap) / allTrayDay))
for kValue, dataItem in dataSet.items():
total_count1 = 0
total_count2 = 0
for cnt in dataItem.sell_disbute:
total_count1 += cnt
for cnt in dataItem.buy_disbute:
total_count2 += cnt
assert total_count1 == total_count2
assert total_count1 > 0
print(f"\n\nk:%6d, " % (kValue))
print(f" 卖方价格分布：")
for encode in range(0, len(dataItem.sell_disbute)):
occurtRate = 100 * dataItem.sell_disbute[encode] / total_count1
print(f" {pctEncoder.descriptEncdoe(encode)}：%.2f%%" %
(occurtRate))
print(f" 买方价格分布：")
for encode in range(0, len(dataItem.buy_disbute)):
occurtRate = 100 * dataItem.buy_disbute[encode] / total_count1
print(f" {pctEncoder.descriptEncdoe(encode)}：%.2f%%" %
(occurtRate))
pass