sw = SWImpl()
lists = sw.getSW2List()
chart = Chart()
start = datetime(2014, 5, 1)
end = datetime(2020, 8, 17)
dict = {}
dict[0] = []
dict[1] = []
dict[2] = []
dict[3] = []
dict[4] = []
indictor = macd()
for code in lists:
if len(sw.getSW2Stocks(code)) < 10:
continue
bars = sw.getSW2Daily(code, start, end)
# print(f"bar.size = {bars.__len__()}")
chart.run(bars, indictor)
holdbars = indictor.getHoldBars()
# maker = HoldBarMaker()
# for holdbar in holdbars:
# if(HoldBarUtils.isEarnBarList(holdbar.bars)):
# barList = holdbar.bars
# maker.onHoldStart(barList[0])
# for i in range(1,len(barList)):
# maker.onHoldUpdate(barList[i])
# maker.onHoldEnd()
from datetime import datetime
from earnmi.data.MarketImpl import MarketImpl
from earnmi.data.SWImpl import SWImpl
import time
sw = SWImpl()
swCode = sw.getSW2List()[0]
stockslist = sw.getSW2Stocks(swCode)
print(f"stockslist:{len(stockslist)}")
startDate = datetime(2015, 4, 1)
endDate = datetime(2020, 5, 1)
market = MarketImpl()
market.setToday(endDate)
market.addNotice('002852')
bars = market.getHistory().getKbars('002852', 300 * 3)
for code in stockslist:
market.addNotice(code)
print(f"getBars:{code}")
bars = market.getHistory().getKbars(code, 300 * 3)
#time.sleep(2)
print(f"getBars size:{len(bars)}")
def computeHoldBarIndictor(indictor:IndicatorItem)->HoldBarData:
sw = SWImpl()
lists = sw.getSW2List()
chart = Chart()
total_cost_pcts = []#收益
avg_eran_cost_pcts = []#每个盈利holdbard的平均盈利
total_days = []
total_eran_days = []
total_holdbars = []
total_holdbars_earn = []
max_cost_pcts =[] #最大收益
min_cost_pcts = []
for code in lists:
if len(sw.getSW2Stocks(code)) < 10:
continue
bars = sw.getSW2Daily(code, start, end)
#print(f"bar.size = {bars.__len__()}")
chart.run(bars, indictor)
holdbarList = indictor.getHoldBars()
#holdbarList = HoldBarUtils.filterHoldBar(holdbarList)
data = HoldBarUtils.computeHoldBarIndictor(holdbarList);
if data is None:
continue
total_cost_pcts.append(data.total_cost_pct)
max_cost_pcts.append(data.max_cost_pct)
min_cost_pcts.append(data.min_cost_pct)
total_days.append(data.total_day)
total_holdbars.append(data.total_holdbar)
total_holdbars_earn.append(data.total_holdbar_earn)
avg_eran_cost_pcts.append(data.avg_eran_cost_pct)
total_eran_days.append(data.total_earn_day)
ret = HoldBarData()
total_cost_pcts = np.array(total_cost_pcts)
total_days = np.array(total_days)
max_cost_pcts = np.array(max_cost_pcts)
min_cost_pcts = np.array(min_cost_pcts)
total_holdbars = np.array(total_holdbars)
total_holdbars_earn = np.array(total_holdbars_earn)
avg_eran_cost_pcts = np.array(avg_eran_cost_pcts)
total_eran_days = np.array(total_eran_days)
ret.total_min_cost_pct = total_cost_pcts.min()
ret.total_max_cost_pct = total_cost_pcts.max()
ret.total_cost_pct = total_cost_pcts.mean()
ret.total_cost_pct_std = np.std(total_cost_pcts)
ret.total_day = total_days.mean()
ret.max_cost_pct = max_cost_pcts.mean()
ret.min_cost_pct = min_cost_pcts.mean()
ret.total_holdbar = total_holdbars.mean()
ret.total_holdbar_earn = total_holdbars_earn.mean()
ret.avg_eran_cost_pct = avg_eran_cost_pcts.mean()
ret.total_earn_day = total_eran_days.mean()
return ret
