def on_market_prepare_open(self, protfolio: Portfolio, today: datetime):
"""
市场准备开始（比如：竞价）.
"""
indicator = Indicator(40)
for code in self.codes:
bars = self.market.getHistory().getKbars(code, 100)
indicator.update_bar(bars)
count = 20
aroon_down, aroon_up = indicator.aroon(count)
need_hold = aroon_up > 50 and aroon_up > aroon_down
position = protfolio.getLongPosition(code)
if need_hold:
if position.pos_total < 1:
targetPrice = bars[-1].close_price * 1.05 # 上一个交易日的收盘价作为买如价
ok = protfolio.buyAtPercentage(code, targetPrice, 1)
print(f"buy: price = {targetPrice} , {ok}")
else:
if position.pos_total > 0:
targetPrice = bars[-1].close_price * 0.92 # 上一个交易日的收盘价作为买如价
ok = protfolio.sellAll(code, targetPrice)
print(f"sell: price = {targetPrice} , {ok}")
pass
def computeAll():
from earnmi.data.SWImpl import SWImpl
from earnmi.chart.Chart import Chart, IndicatorItem, Signal
sw = SWImpl()
lists = sw.getSW2List()
start = datetime(2018, 5, 1)
end = datetime(2020, 8, 17)
dataSet = {}
class DataItem(object):
pass
for code in lists:
#for code in lists:
barList = sw.getSW2Daily(code, start, end)
indicator = Indicator()
for bar in barList:
##先识别形态
rets = KPattern.matchIndicator(indicator)
size = len(rets)
if size > 0:
"""有形态识别出来
"""
for item in rets:
name = item.name
value = item.value
dataItem = None
if dataSet.__contains__(name):
dataItem = dataSet[name]
else:
dataItem = DataItem()
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
pct = (bar.close_price - bar.open_price) / bar.open_price
##收录当前形态
dataItem.values.append(value)
dataItem.pcts.append(pct)
pass
indicator.update_bar(bar)
##打印当前形态
print(f"总共识别出{len(dataSet)}个形态")
for key, dataItem in dataSet.items():
values = np.array(dataItem.values)
pcts = np.array(dataItem.pcts) * 100
print(
f"{key}： len={len(dataItem.values)},values:{values.mean()},pcts:%.2f%%,pcts_std=%.2f"
% (pcts.mean(), pcts.std()))
def on_bar_per_minute(self, time: datetime, protfolio: Portfolio):
"""
市场开市后的每分钟。
"""
#每天两点半的后尝试去做交易。
if time.hour == 14 and time.minute == 30:
self.__history_bar100 = self.market.getHistory().getKbars(
self.code, 100)
assert len(self.__history_bar100) == 100
bars = self.__history_bar100
todayBar = self.market.getRealTime().getKBar(self.code)
indicator = Indicator(40)
indicator.update_bar(bars)
dif, dea, macd_bar = indicator.macd(fast_period=12,
slow_period=26,
signal_period=9,
array=True)
if (not self.today_has_buy):
# 预测金叉
todayBar.close_price = todayBar.close_price * 1.01
indicator.update_bar(todayBar)
dif, dea, predict_macd_bar = indicator.macd(fast_period=12,
slow_period=26,
signal_period=9,
array=True)
print(
f"[{self.market.getToday()}]:bar={macd_bar[-1]},predic_bar={predict_macd_bar[-1]}"
)
if (predict_macd_bar[-1] > 0 and macd_bar[-1] <= 0):
targetPrice = todayBar.close_price # 上一个交易日的收盘价作为买如价
print(f" gold cross!!!")
if protfolio.buy(self.code, targetPrice, 100):
self.today_has_buy = True
elif (not self.today_has_sell):
todayBar.close_price = todayBar.close_price * 0.99
indicator.update_bar(todayBar)
dif, dea, predict_macd_bar = indicator.macd(fast_period=12,
slow_period=26,
signal_period=9,
array=True)
print(
f"[{self.market.getToday()}]:bar={macd_bar[-1]},predic_bar={predict_macd_bar[-1]}"
)
if (predict_macd_bar[-1] <= 0 and macd_bar[-1] > 0):
targetPrice = todayBar.close_price
print(f" dead cross!!!")
if protfolio.sell(self.code, targetPrice, 100):
self.today_has_sell = True
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 on_market_prepare_open(self, protfolio: Portfolio, today: datetime):
"""
市场准备开始（比如：竞价）.
"""
indicator = Indicator(40)
for code in self.codes:
bars = self.market.getHistory().getKbars(code, 100)
indicator.update_bar(bars)
dif, dea, macd_bar = indicator.kdj()
##金叉出现
if (macd_bar[-1] >= 0 and macd_bar[-2] <= 0):
tradePrice = bars[-1].close_price * 1.01 # 上一个交易日的收盘价作为买如价
protfolio.buy(code, tradePrice, 1)
protfolio.cover(code, tradePrice, 1) ##平仓做空
##死叉出现
if (macd_bar[-1] <= 0 and macd_bar[-2] >= 0):
targetPrice = bars[-1].close_price * 0.99 # 上一个交易日的收盘价作为买如价
protfolio.sell(code, targetPrice, 1)
protfolio.short(code, targetPrice, 1) ##开仓做空
pass
def computeAndPrint(bars: []) -> AnalysisData:
data = AnalysisData()
total_count = len(bars)
previous_macd = -1
previouc_kdj = -1
indicator = Indicator(50)
for i in range(0, total_count):
bar: BarData = bars[i]
indicator.update_bar(bar)
k_large_than_d = False
if indicator.count >= 13:
k, d, j = indicator.kdj(fast_period=9, slow_period=3, array=True)
k_large_than_d = k[-1] >= d[-1]
##金叉出现
if (k[-1] >= d[-1] and k[-2] <= d[-2]):
previouc_kdj = i
if indicator.count >= 30:
dif, dea, macd_bar = indicator.macd(fast_period=12,
slow_period=26,
signal_period=9,
array=True)
##金叉出现
if (macd_bar[-1] >= 0 and macd_bar[-2] <= 0):
previous_macd = i
if previouc_kdj > 0:
data.count = data.count + 1
if k_large_than_d:
data.k_large_than_d_count = data.k_large_than_d_count + 1
dis = previous_macd - previouc_kdj
if (dis >= 0 and dis < KDJ_DIS_SIZE):
data.kdj_dis[dis] = data.kdj_dis[dis] + 1
return data
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}")
"""
不同的股票价格的macd的数值不同，macd是跟价格相关的。这样不同股票的价格的macd数值就不好做个比较。
所以要标准化，不同价格的macd的值要标准进行可比较。
标准化的步骤就是： macd值处于最后一天的收盘价。
这里统计下中证500的macd标准化之后的数据统计情况
"""
if __name__ == "__main__":
start = datetime(2015, 10, 1)
end = datetime(2020, 9, 30)
souces = ZZ500DataSource(start, end)
bars,code = souces.nextBars()
dif_list = []
dea_list = []
while not bars is None:
indicator = Indicator(40)
for bar in bars:
indicator.update_bar(bar)
if indicator.count > 33:
dif, dea, macd_bar = indicator.macd(fast_period=12, slow_period=26, signal_period=9, array=False)
dif_value = dif/bar.close_price * 100
dea_value = dea / bar.close_price * 100
dif_list.append(dif_value)
dea_list.append(dea_value)
if abs(dif_value) > 20:
print(f"dif:{dif_value},code={code},bar = {bar}")
if abs(dea_value) > 20:
print(f"dea:{dea_value},code={code},bar = {bar}")
bars, code = souces.nextBars()
dif_list = np.array(dif_list)
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
exchange=Exchange.SSE,
datetime=None,
gateway_name="unkonw",
open_price=883,
high_price=0,
low_price=0)
bar4 = BarData(symbol="test",
exchange=Exchange.SSE,
datetime=None,
gateway_name="unkonw",
open_price=884,
high_price=0,
low_price=0)
indexes = Indicator(100)
assert indexes.inited == False
bars55 = buidildBars(55)
indexes.update_bar(bars55)
assert indexes.count == 55
assert indexes.inited == False
assert indexes.open_array[-1] == 54
assert indexes.open_array[-55] == 0
bars42 = buidildBars(42)
indexes.update_bar(bars42)
assert indexes.count == 97
assert indexes.inited == False
assert indexes.open_array[-1] == 41
assert indexes.open_array[-42] == 0
def compute_SW_KEncode_parseAlgro1_split(
pct_split=[-7, -5, -3, -1.5, -0.5, 0.5, 1.5, 3, 5, 7],
extra_split=[1, 2, 3]):
from earnmi.data.SWImpl import SWImpl
sw = SWImpl()
lists = sw.getSW2List()
start = datetime(2014, 5, 1)
end = datetime(2020, 8, 17)
dataSet = {}
pct_split = [-7, -5, -3, -1.5, -0.5, 0.5, 1.5, 3, 5, 7]
extra_split = [0.5, 1.0, 1.5, 2.0, 2.5, 2.0]
total_count = 0
pct_code_count = np.zeros(len(pct_split) + 1)
high_extra_pct_code_count = np.zeros(len(extra_split) + 1)
low_extra_pct_code_count = np.zeros(len(extra_split) + 1)
for code in lists:
#for code in lists:
barList = sw.getSW2Daily(code, start, end)
indicator = Indicator(40)
preBar = None
for i in range(0, len(barList)):
bar = barList[i]
indicator.update_bar(bar)
if (i > 10):
k_code, pct_code, high_extra_pct_code, low_extra_pct_code = KEncode.parseAlgro1(
indicator.close[-2], indicator.open[-1],
indicator.high[-1], indicator.low[-1], indicator.close[-1],
pct_split, extra_split)
high_extra_pct_code_count[
high_extra_pct_code] = high_extra_pct_code_count[
high_extra_pct_code] + 1
low_extra_pct_code_count[
low_extra_pct_code] = low_extra_pct_code_count[
low_extra_pct_code] + 1
pct_code_count[pct_code] = pct_code_count[pct_code] + 1
total_count += 1
preBar = bar
##打印当前形态
print(f"pct_split: {pct_split}")
print(f"extra_split: {extra_split}")
count_list = pct_code_count
print(f"应该服从正太分布")
print(f"\n：pct_code_count分布,avg = %.4f%%" % (100 / len(count_list)))
for codeId in range(0, len(count_list)):
item_count = count_list[codeId]
item_occur_rate = 100 * item_count / total_count
print(f"\t{codeId}: %.4f %% count:%d" %
(item_occur_rate, item_count))
count_list = high_extra_pct_code_count
print(f"\n：high_extra_pct_code_count分布,avg = %.4f%%" %
(100 / len(count_list)))
for codeId in range(0, len(count_list)):
item_count = count_list[codeId]
item_occur_rate = 100 * item_count / total_count
print(f"\t{codeId}: %.4f %% count:%d" %
(item_occur_rate, item_count))
count_list = low_extra_pct_code_count
print(f"\n：low_extra_pct_code_count分布,avg = %.4f%%" %
(100 / len(count_list)))
for codeId in range(0, len(count_list)):
item_count = count_list[codeId]
item_occur_rate = 100 * item_count / total_count
print(f"\t{codeId}: %.4f %% count:%d" %
(item_occur_rate, item_count))
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}")
def run(self, bars: list, item: IndicatorItem = None):
if (bars[0].datetime > bars[-1].datetime):
bars = bars.__reversed__()
data = []
index = []
indicator = Indicator()
### 初始化columns
columns = ['Open', 'High', 'Low', 'Close', "Volume"]
holdbarMaker: HoldBarMaker = None
if not item is None:
holdbarMaker = item._holdbarMaker
if not holdbarMaker is None:
holdbarMaker.reset()
item_names = item.getNames()
item_size = len(item_names)
for i in range(item_size):
columns.append(item_names[i])
columns.append("signal_buy")
columns.append("signal_sell")
columns.append("signal_redTag")
item_signal_buy_open = False
item_signal_sell_open = False
item_signal_redTag_open = False
item_signal = Signal()
##current_hold_bar: HoldBar = None
has_buy = False
for bar in bars:
index.append(bar.datetime)
list = [
bar.open_price, bar.high_price, bar.low_price, bar.close_price,
bar.volume
]
indicator.update_bar(bar)
if not item is None:
item_names = item.getNames()
item_size = len(item_names)
item_signal.reset()
item_signal.hasBuy = has_buy
item_value = item.getValues(indicator, bar, item_signal)
for i in range(item_size):
list.append(item_value[item_names[i]])
if item_signal.buy:
list.append(bar.low_price * 0.99)
item_signal_buy_open = True
has_buy = True
##生成一个新的holdbar。
if not holdbarMaker is None:
holdbarMaker.onHoldStart(bar)
else:
##更新holdBar
if not holdbarMaker is None:
holdbarMaker.onHoldUpdate(bar)
list.append(np.nan)
if item_signal.sell:
list.append(bar.high_price * 1.01)
item_signal_sell_open = True
has_buy = False
##结束目前的HoldBar
if not holdbarMaker is None:
holdbarMaker.onHoldEnd()
else:
list.append(np.nan)
if item_signal.redTag:
item_signal_redTag_open = True
list.append(bar.low_price * 0.99)
else:
list.append(np.nan)
data.append(list)
trades = pd.DataFrame(data, index=index, columns=columns)
return trades, item_signal_buy_open, item_signal_sell_open, item_signal_redTag_open
def onStart(self, code: str) -> bool:
self.indicator = Indicator(40)
self.traceCode = code
self.traceName = self.sw.getSw2Name(code)
return True
def onStart(self, code: str) -> bool:
self.indicator = Indicator(40)
self.code = code
return True
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 onCollectStart(self, code: str) -> bool:
from earnmi.chart.Indicator import Indicator
self.indicator = Indicator(40)
self.code = code
return True
from earnmi.chart.Indicator import Indicator from earnmi.data.MarketImpl import MarketImpl from datetime import datetime, timedelta import talib code = "600155" #code = '000300' #801161.XSHG market = MarketImpl() market.addNotice(code) market.setToday(datetime.now()) bars = market.getHistory().getKbars(code, 80) indicator = Indicator() indicator.update_bar(bars) beta = talib.CORREL(indicator.high, indicator.low, 30) print(beta)