class QuantStrategy(CoreEngineStrategy):
def __init__(self):
self.sw = SWImpl()
def generatePredictOrder(self, engine: CoreEngine, predict: PredictData,debugPrams:{}=None) -> PredictOrder:
if debugPrams is None:
debugPrams = {}
quantData = engine.queryQuantData(predict.dimen)
code = predict.collectData.occurBars[-1].symbol
name = self.sw.getSw2Name(code)
order = PredictOrder(dimen=predict.dimen, code=code, name=name)
start_price = engine.getEngineModel().getYBasePrice(predict.collectData)
_min, _max = quantData.getSellFloatEncoder().parseEncode(quantData.sellRange[0].encode)
order.suggestSellPrice = start_price * (1 + (_min + _max) / 2 / 100)
_min, _max = quantData.getBuyFloatEncoder().parseEncode(quantData.buyRange[0].encode)
order.suggestBuyPrice = start_price * (1 + (_min + _max) / 2 / 100)
order.power_rate = quantData.getPowerRate();
##for backTest
self.checkIfBuyPrice(order,predict.collectData.occurBars[-1].close_price,debugPrams)
return order
def checkIfBuyPrice(self,order: PredictOrder,targetPrice:float,debugPrams:{}=None):
if order.status != PredictOrderStatus.TRACE:
return
quantData = engine.queryQuantData(order.dimen)
if quantData.getPowerRate() > 0.9 and order.suggestBuyPrice >= targetPrice:
order.status = PredictOrderStatus.HOLD
order.buyPrice = targetPrice
def updatePredictOrder(self, order: PredictOrder, bar: BarData, isTodayLastBar: bool,debugPrams:{}):
if (order.status == PredictOrderStatus.HOLD):
if bar.high_price >= order.suggestSellPrice:
order.sellPrice = order.suggestSellPrice
order.status = PredictOrderStatus.CROSS
return
if order.holdDay >= 1:
order.sellPrice = bar.close_price
order.status = PredictOrderStatus.CROSS
return
order.holdDay += 1
self.checkIfBuyPrice(order,bar.low_price,debugPrams)
class Generate_Feature_KPattern_skip1_predit2(
More_detail_KPattern_skip1_predit2):
def __init__(self, limit_close_pct=1, kPatters: [] = None):
super().__init__(limit_close_pct, kPatters)
self.traceDatas = []
self.sw = SWImpl()
self.predictTraceDatas: ['Skip1_Predict2_TraceData'] = []
#pct_split = [-7, -5, -3, -1.5, -0.5, 0.5, 1.5, 3, 5, 7]
#self.pctEncoder = FloatEncoder(pct_split)
def onCreate(self):
super().onCreate()
self.traceDatas.clear()
def doWantedTraceData(self, traceData: Skip1_Predict2_TraceData,
countData: CountData):
super().doWantedTraceData(traceData, countData)
self.traceDatas.append(traceData)
pass
def onTraceStop(self, traceData: Skip1_Predict2_TraceData):
super().onTraceStop(traceData)
if (not traceData.skipBar is None):
self.predictTraceDatas.append(traceData)
def generateData(
self,
traceDatas: Sequence["Skip1_Predict2_TraceData"]) -> pd.DataFrame:
trainDataSet = []
for traceData in traceDatas:
occurBar = traceData.occurBar
skipBar = traceData.skipBar
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
data = []
data.append(self.code)
data.append(self.sw.getSw2Name(self.code))
data.append(traceData.kPatternValue)
data.append(buy_pct)
data.append(sell_pct)
data.append(traceData.indicator_k)
data.append(traceData.indicator_j)
data.append(traceData.sell_pct)
data.append(traceData.buy_pct)
trainDataSet.append(data)
cloumns = [
"code", "name", "kPattern", "buy_price", "sell_price", "k", "j",
"label_sell_price", "label_buy_price"
]
return pd.DataFrame(trainDataSet, columns=cloumns)
def getPandasData(self) -> pd.DataFrame:
return self.generateData(self.traceDatas)
def writeToXml(self) -> bool:
size = len(self.traceDatas)
if size < 1:
print("no need to write")
return False
wxl = self.getPandasData()
writer = pd.ExcelWriter('files/sw_train_data_sample.xlsx')
wxl.to_excel(writer, sheet_name="sample", index=False)
writer.save()
writer.close()
print(f"write dataSize = {size}")
return True
def onDestroy(self):
super().onDestroy()
class MyStrategy(CoreEngineStrategy):
def __init__(self):
self.sw = SWImpl()
self.mDateHoldCountMap = {} ##统计产生收集个数的次数
def generatePredictOrder(self,
engine: CoreEngine,
predict: PredictData,
debugPrams: {} = None) -> PredictOrder:
if debugPrams is None:
debugPrams = {}
code = predict.collectData.occurBars[-1].symbol
name = self.sw.getSw2Name(code)
order = PredictOrder(dimen=predict.dimen, code=code, name=name)
predict_sell_pct = predict.getPredictSellPct(engine.getEngineModel())
predict_buy_pct = predict.getPredictBuyPct(engine.getEngineModel())
start_price = engine.getEngineModel().getYBasePrice(
predict.collectData)
order.suggestSellPrice = start_price * (1 + predict_sell_pct / 100)
order.suggestBuyPrice = start_price * (1 + predict_buy_pct / 100)
order.power_rate = engine.queryQuantData(predict.dimen).getPowerRate()
timeKey = utils.to_start_date(
predict.collectData.occurBars[-1].datetime)
if self.mDateHoldCountMap.get(timeKey) is None:
self.mDateHoldCountMap[timeKey] = 0
##for backTest
occurBar: BarData = predict.collectData.occurBars[-2]
skipBar: BarData = predict.collectData.occurBars[-1]
buy_price = skipBar.close_price
predict_sell_pct = 100 * (order.suggestSellPrice -
start_price) / start_price
predict_buy_pct = 100 * (order.suggestBuyPrice -
start_price) / start_price
buy_point_pct = 100 * (
buy_price - occurBar.close_price) / occurBar.close_price ##买入的价格
abilityData = engine.queryPredictAbilityData(predict.dimen)
quantData = engine.queryQuantData(predict.dimen)
delta = abs(quantData.sellCenterPct) - abs(quantData.buyCenterPct)
if abs(delta) < 0.05:
# 多空力量差不多
power = 0
if delta > 0:
# 适合做多
power = (quantData.sellCenterPct +
quantData.buyCenterPct) / quantData.sellCenterPct
else:
power = -(quantData.sellCenterPct +
quantData.buyCenterPct) / quantData.buyCenterPct
extraCondition = True
quant_power = debugPrams.get("quant_power")
if not quant_power is None:
extraCondition = extraCondition and predict.quantData.getPowerRate(
) >= quant_power
predict_buy_pct_param = debugPrams.get("predict_buy_pct")
if not predict_buy_pct_param is None:
extraCondition = extraCondition and predict_buy_pct >= predict_buy_pct_param
if extraCondition and predict_sell_pct - buy_point_pct > 1 \
and abilityData.trainData.biasSellLoss < 10:
order.status = PredictOrderStatus.HOLD
order.buyPrice = buy_price
timeKey = utils.to_start_date(
predict.collectData.occurBars[-1].datetime)
self.mDateHoldCountMap[timeKey] += 1
else:
order.status = PredictOrderStatus.STOP
return order
def updatePredictOrder(self,
order: PredictOrder,
bar: BarData,
isTodayLastBar: bool,
debugParams: {} = None):
if (order.status == PredictOrderStatus.HOLD):
if bar.high_price >= order.suggestSellPrice:
order.sellPrice = order.suggestSellPrice
order.status = PredictOrderStatus.CROSS
return
order.holdDay += 1
if order.holdDay >= 2:
order.sellPrice = bar.close_price
order.status = PredictOrderStatus.CROSS
return
class MyPattherCollector(KPatternCollector):
# 收集指定的k线
collectKPatternOnly = True
KPattern: [] = [712]
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)
def __init__(self):
self.kPattersMap = {}
self.sw = SWImpl()
self.dataSet = []
for value in self.KPattern:
self.kPattersMap[value] = True
def onStart(self, code: str) -> bool:
self.indicator = Indicator(40)
self.traceCode = code
self.traceName = self.sw.getSw2Name(code)
return True
"""
检查是否追踪某个k线形态，是的话，创建一个最终值对象。
"""
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 onTraceFinish(self, traceItem: TraceIn3DayItem):
if traceItem.isWanted():
##收集数据。
data = []
data.append(self.traceCode)
data.append(self.traceName)
data.append(traceItem.kPattern)
data.append(traceItem.first_buy_pct)
data.append(traceItem.fisrt_sell_pct)
data.append(traceItem.current_sell_pct)
data.append(traceItem.current_buy_pct)
self.dataSet.append(data)
def onDestroy(self):
import pandas as pd
cloumns = [
"code", "name", "kPattern", "buy_price", "sell_price",
"label_sell_price", "label_buy_price"
]
wxl = pd.DataFrame(self.dataSet, columns=cloumns)
writer = pd.ExcelWriter('files/sw_train_data_sample.xlsx')
wxl.to_excel(writer, sheet_name="sample", index=False)
writer.save()
writer.close()
print(f"dataSize = {len(self.dataSet)}")
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
