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]
timeKey = utils.to_start_date(bar.datetime)
if self.mDateOccurCountMap.get(timeKey) is None:
self.mDateOccurCountMap[timeKey] = 0
if self.indicator.count >= 30:
aroon_down, aroon_up = self.indicator.aroon(n=14, array=False)
from earnmi.chart.KPattern import KPattern
if aroon_up < aroon_down or aroon_up < 50:
return None
kPatternValue = KPattern.encode2KAgo1(self.indicator)
if not kPatternValue is None:
self.mDateOccurCountMap[timeKey] += 1
_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 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 collect(self, bar: BarData) -> TraceData:
self.indicator.update_bar(bar)
kPatternValue = KPattern.encode2KAgo1(self.indicator)
if not kPatternValue is None and self.indicator.count > 20:
self.collect_k_count += 1
traceData = Skip1_Predict2_TraceData(kPatternValue, bar)
traceData.code = bar.symbol
return traceData
return None
def checkIfTrace(self, newBar: BarData) -> TraceIn3DayItem:
self.indicator.update_bar(newBar)
kEncodeValue = KPattern.encode2KAgo1(self.indicator)
if kEncodeValue is None:
return None
if self.collectKPatternOnly and self.kPattersMap.get(
kEncodeValue) is None:
return None
return TraceIn3DayItem(kEncodeValue, newBar)
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 collectKPattherAndShowChart():
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)
bars = []
limitSize = 0
chart = Chart()
for code in lists:
# for code in lists:
barList = sw.getSW2Daily(code, start, end)
indicator = Indicator(40)
preBar = None
yestodayIsMatch = False
for i in range(0, len(barList)):
bar = barList[i]
indicator.update_bar(bar)
patternValue = KPattern.encode1KAgo1(indicator)
todayIsMatch = 9 == patternValue
if todayIsMatch:
if indicator.count > 20:
chart.show(
indicator.makeBars(),
savefig=f"imgs/collectKPattherAndShowChart_{limitSize}"
)
limitSize += 1
if (limitSize > 50):
break
pass
if yestodayIsMatch:
pass
preBar = bar
yestodayIsMatch = todayIsMatch
if (limitSize > 50):
break
pass
def findKPatternThatIn3Day(first_day_pct: float = 3, targe_pct=3):
sw = SWImpl()
lists = sw.getSW2List()
start = datetime(2014, 5, 1)
end = datetime(2020, 8, 17)
dataSet = {}
total_count = 0
for code in lists:
# for code in lists:
barList = sw.getSW2Daily(code, start, end)
indicator = Indicator(40)
traceItems: ['TraceIn3DayItem'] = []
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():
###归纳到统计里面
dataItem: CountItem = dataSet.get(traceItem.kPattern)
if dataItem is None:
dataItem = CountItem()
dataSet[traceItem.kPattern] = dataItem
pct = traceItem.current_sell_pct
total_count += 1
dataItem.count_total += 1
dataItem.pct_total += pct
if traceItem.isSuccess():
dataItem.count_earn += 1
dataItem.pct_earn += pct
pass
for traceItem in toDeleteList:
traceItems.remove(traceItem)
indicator.update_bar(bar)
kEncodeValue = KPattern.encode2KAgo1(indicator)
if kEncodeValue is None:
continue
traceItem = TraceIn3DayItem(kEncodeValue, bar)
traceItems.append(traceItem)
##打印当前形态
occur_count = 0
print(f"总共分析{total_count}个形态，识别出{len(dataSet)}个形态，有意义的形态有：")
max_succ_rate = 0
min_succ_rate = 100
ret_list = []
for key, dataItem in dataSet.items():
success_rate = 100 * dataItem.count_earn / dataItem.count_total
if dataItem.count_total < 300:
continue
if success_rate < 40:
continue
ret_list.append(key)
if dataItem.count_earn > 0:
earn_pct = dataItem.pct_earn / dataItem.count_earn
else:
earn_pct = 0
avg_pct = dataItem.pct_total / dataItem.count_total
occur_count += dataItem.count_total
occur_rate = 100 * dataItem.count_total / total_count
max_succ_rate = max(success_rate, max_succ_rate)
min_succ_rate = min(success_rate, min_succ_rate)
print(
f"{key}： total={dataItem.count_total},suc=%.2f%%,occur_rate=%.2f%%,earn_pct:%.2f%%,avg_pct:%.2f%%)"
% (success_rate, occur_rate, earn_pct, avg_pct))
total_occur_rate = 100 * occur_count / total_count
print(f"总共：occur_rate=%.2f%%, min_succ_rate=%.2f%%, max_succ_rate=%.2f%%" %
(total_occur_rate, min_succ_rate, max_succ_rate))
print(f"{ret_list}")
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
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
def compute_SW_KPattern_data():
from earnmi.data.SWImpl import SWImpl
from earnmi.chart.Chart import Chart, IndicatorItem, Signal
sw = SWImpl()
lists = sw.getSW2List()
start = datetime(2014, 5, 1)
end = datetime(2020, 8, 17)
dataSet = {}
total_count = 0
for code in lists:
#for code in lists:
barList = sw.getSW2Daily(code, start, end)
indicator = Indicator()
preBar = None
for bar in barList:
##先识别形态
rets = KPattern.matchIndicator(indicator)
size = len(rets)
if size > 0 and not preBar is None:
"""有形态识别出来
"""
for item in rets:
name = item.name
value = item.value
total_count += 1
dataItem = None
if dataSet.__contains__(name):
dataItem = dataSet[name]
else:
dataItem = CountItem()
dataItem.values = [] ##形态被识别的值。
dataItem.pcts = [] ##识别之后第二天的盈利情况
dataSet[name] = dataItem
##第二天的收益
short_pct = ((bar.high_price + bar.close_price) / 2 -
preBar.close_price) / preBar.close_price
#long_pct = ((bar.high_price + bar.close_price) / 2 - preBar.close_price) / preBar.close_price
###pct = (bar.close_price - preBar.close_price) / preBar.close_price
##收录当前形态
dataItem.count_total += 1
dataItem.pct_total += short_pct
if short_pct > 0.000001:
dataItem.count_earn += 1
dataItem.pct_earn += short_pct
dataItem.values.append(value)
dataItem.pcts.append(short_pct)
pass
indicator.update_bar(bar)
preBar = bar
##打印当前形态
print(f"总共分析{total_count}个形态，识别出{len(dataSet)}个形态，有意义的形态有：")
for key, dataItem in dataSet.items():
if dataItem.count_total < 1000:
continue
success_rate = 100 * dataItem.count_earn / dataItem.count_total
if abs(int(success_rate - 50)) < 5:
continue
values = np.array(dataItem.values)
pcts = np.array(dataItem.pcts) * 100
count = len(values)
long_values = []
short_value = []
long_pcts = []
long_ok_cnt = 0
short_pcts = []
short_ok_cnt = 0
for i in range(0, count):
v = values[i]
if v > 0:
long_values.append(v)
long_pcts.append(pcts[i])
if pcts[i] >= 0.000001:
long_ok_cnt = long_ok_cnt + 1
else:
short_value.append(v)
short_pcts.append(pcts[i])
if pcts[i] <= -10.000001:
short_ok_cnt = short_ok_cnt + 1
long_values = np.array(long_values)
short_value = np.array(short_value)
long_pcts = np.array(long_pcts)
short_pcts = np.array(short_pcts)
long_pct = 0
long_std = math.nan
long_success = math.nan
short_pct = 0
short_std = math.nan
short_success = math.nan
if len(long_values) > 0:
long_pct = long_pcts.mean()
long_std = long_pcts.std()
long_success = long_ok_cnt / len(long_values)
if len(short_value) > 0:
short_pct = short_pcts.mean()
short_std = short_pcts.std()
short_success = short_ok_cnt / len(short_value)
print(
f"{key}： count={count},suc_reate=%.2f%%,long(size:{len(long_values)},suc=%.2f%%,pcts:%.2f%%,std=%.2f),short(size:{len(short_value)},suc=%.2f%%,pcts:%.2f%%,std=%.2f)"
% (success_rate, long_success * 100, long_pct, long_std,
short_success * 100, short_pct, short_std))
print("-----------具体情况-----------")
outputKeys = ["CDLADVANCEBLOCK"]
for key in outputKeys:
dataItem = dataSet[key]
values = np.array(dataItem.values)
pcts = np.array(dataItem.pcts) * 100
count = len(dataItem.values)
print(
f"{key}： count={count},values:{values.mean()},pcts:%.2f%%,pcts_std=%.2f"
% (pcts.mean(), pcts.std()))
itemSize = 10
size = int(count / itemSize)
if count % itemSize > 0:
size = size + 1
for i in range(0, size):
lineStr = ""
start = itemSize * i
end = min(start + itemSize, count)
for j in range(start, end):
lineStr = lineStr + (f" %4d->%.2f%%," % (values[j], pcts[j]))
print(lineStr)
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
def compute_SW_KEncode_data():
from earnmi.data.SWImpl import SWImpl
sw = SWImpl()
lists = sw.getSW2List()
start = datetime(2014, 5, 1)
end = datetime(2020, 8, 17)
dataSet = {}
total_count = 0
occurKPattenDayMap = {}
kBarListTotalDay = 0
for code in lists:
#for code in lists:
barList = sw.getSW2Daily(code, start, end)
indicator = Indicator(40)
preBar = None
kBarListTotalDay = len(barList)
for bar in barList:
##先识别形态
kEncodeValue = KPattern.encode1KAgo1(indicator)
if kEncodeValue is None:
indicator.update_bar(bar)
preBar = bar
continue
total_count += 1
dataItem: CountItem = None
if dataSet.__contains__(kEncodeValue):
dataItem = dataSet[kEncodeValue]
else:
dataItem = CountItem()
dataSet[kEncodeValue] = dataItem
##第二天的收益
pct = ((bar.high_price + bar.close_price) / 2 -
preBar.close_price) / preBar.close_price
##收录当前形态
#dataItem.values.append(value)
dataItem.count_total += 1
dataItem.pct_total += pct
if pct > 0.000001:
dataItem.count_earn += 1
dataItem.pct_earn += pct
indicator.update_bar(bar)
preBar = bar
occurDayKey = preBar.datetime.year * 13 * 35 + preBar.datetime.month * 35 + preBar.datetime.day
occurKPattenDayMap[occurDayKey] = True
##打印当前形态
occur_count = 0
print(f"总共分析{total_count}个形态，识别出{len(dataSet)}个形态，有意义的形态有：")
max_succ_rate = 0
min_succ_rate = 100
ret_list = []
for key, dataItem in dataSet.items():
success_rate = 100 * dataItem.count_earn / dataItem.count_total
if dataItem.count_total < 500:
continue
if abs(int(success_rate - 50)) < 10:
continue
ret_list.append(key)
earn_pct = 100 * dataItem.pct_earn / dataItem.count_earn
if success_rate < 50:
earn_pct = 100 * (dataItem.pct_total - dataItem.pct_earn) / (
dataItem.count_total - dataItem.count_earn)
avg_pct = 100 * dataItem.pct_total / dataItem.count_total
occur_count += dataItem.count_total
occur_rate = 100 * dataItem.count_total / total_count
max_succ_rate = max(success_rate, max_succ_rate)
min_succ_rate = min(success_rate, min_succ_rate)
print(
f"{key}： total={dataItem.count_total},suc=%.2f%%,occur_rate=%.2f%%,earn_pct:%.2f%%,avg_pct:%.2f%%)"
% (success_rate, occur_rate, earn_pct, avg_pct))
total_occur_rate = 100 * occur_count / total_count
total_occur_in_day_rate = 100 * len(
occurKPattenDayMap) / kBarListTotalDay ##在所有交易日中，k线形态日出占比：
print(f"总共：occur_rate=%.2f%%, min_succ_rate=%.2f%%, max_succ_rate=%.2f%%"
f"\n所有交易日中，k线形态日出占比：%.2f%%" %
(total_occur_rate, min_succ_rate, max_succ_rate,
total_occur_in_day_rate))
print(f"{ret_list}")