class BollBacktesting(BaseBackTesing):
def __init__(self):
super().__init__()
self.stock_code = None
self.offline_stock_action = OfflineStockAction()
self.indicator_strategy = IndicatorStrategy()
self.boll = None
self.stock = None
self.buy_date = None
def set_stock_code(self, stock_code):
self.stock_code = stock_code
def is_buy_position(self, history_prices, current_price):
all_prices = history_prices
avg_vol = all_prices.tail(5)['volume'].mean() # 5日均量
newest_price = all_prices.tail(1)
close_price = newest_price['close']
newest_vol = newest_price['volume']
newest_high = newest_price['high']
newest_low = newest_price['low']
newest_open = newest_price['open']
last_high_price = all_prices.tail(2).head(1)['high']
last_close_price = all_prices.tail(2).head(1)['close']
try:
if newest_high.item() > last_high_price.item() * 1.03 \
and (close_price.item() - newest_open.item()) > 0.8 * (newest_high.item() - newest_low.item()) \
and (newest_vol.item() > avg_vol.item()):
upperband = self.boll['upperband']
middleband = self.boll['middleband']
lowerband = self.boll['lowerband']
if close_price.item() > middleband[len(middleband) - 1] \
and last_close_price.item() < middleband[len(middleband) - 2]:
log.info("stock_info: %s(%s) close_price:%.2f, +%.2f%%",
self.stock['display_name'],
self.stock['stock_code'], close_price.item(),
(close_price.item() - last_close_price.item()) /
last_close_price.item() * 100)
log.info("upper :%.2f; middle: %.2f; lower: %.2f",
upperband[len(upperband) - 1],
middleband[len(middleband) - 1],
lowerband[len(lowerband) - 1])
log.info(
"======================================================"
)
self.buy_date = datetime.datetime.strptime(
str(current_price['trade_day']).split(" ")[0],
'%Y-%m-%d')
return True
except ValueError:
print(newest_high)
return False
return False
def buy_action(self, history_prices, current_price):
self.buy_position_percent_value(current_price, 1)
def is_sell_position(self, history_prices, current_price):
if self.buy_date is not None \
and self.position > 0:
cur_date = datetime.datetime.strptime(
str(current_price['trade_day']).split(" ")[0], '%Y-%m-%d')
print((cur_date - self.buy_date).days)
if (cur_date - self.buy_date).days > 3:
self.buy_date = None
log.info("持有超过2天，卖出")
return True
return False
def sell_action(self, history_prices, current_price):
self.sell_position_percent_value(current_price, 1)
def is_add_position(self, history_prices, current_price):
super().is_add_position(history_prices, current_price)
def add_position_action(self, history_prices, current_price):
super().add_position_action(history_prices, current_price)
def is_reduce_position(self, history_prices, current_price):
super().is_reduce_position(history_prices, current_price)
def reduce_position_action(self, history_prices, current_price):
super().reduce_position_action(history_prices, current_price)
def is_clear_position(self, history_prices, current_price):
if self.position > 0:
profit = self.position * current_price['open'] - self.INIT_BALANCE
print(profit)
if profit < 0 and (-profit) / self.INIT_BALANCE > 0.08:
log.info("触及止损，清仓")
return True
return False
def clear_position_action(self, history_prices, current_price):
super().sell_position_percent_value(current_price=current_price,
percent=1)
def buy_fee(self):
return super().buy_fee()
def sell_fee(self):
return super().sell_fee()
def end_backtesting(self, history_prices, current_price):
if self.position > 0:
self.sell_position_percent_value(current_price, 1)
def buy_position_percent_value(self, current_price, percent):
# 早盘就卖
current_price['close'] = current_price['open']
return super().buy_position_percent_value(current_price, percent)
def sell_position_percent_value(self, current_price, percent):
return super().sell_position_percent_value(current_price, percent)
def run(self):
if self.stock_code is None:
raise Exception("stock code can not be None")
self.stock = pd.DataFrame(
self.offline_stock_action.query_by_stock_code(self.stock_code))
total_history_prices = self.offline_stock_action.query_all_history_prices(
self.stock_code)
upperband, middleband, lowerband = self.indicator_strategy.calculate_boll(
total_history_prices['close'])
bollinger = {
'upperband': upperband,
'middleband': middleband,
'lowerband': lowerband,
'trade_day': total_history_prices['trade_day']
}
self.boll = pd.DataFrame(bollinger)
super().run_backtesting(total_history_prices=total_history_prices,
start_date=datetime.datetime(2018, 1, 1))
return float(
round((self.balance - self.INIT_BALANCE) / self.INIT_BALANCE * 100,
2))
class SimpleBackTesting(BaseBackTesing):
def __init__(self):
BaseBackTesing.__init__(self)
self.stock_code = None
self.offline_stock_action = OfflineStockAction()
self.bull_up_strategy = BullUpStrategy()
self.indicator_strategy = IndicatorStrategy()
self.total_dmi = None
self.stock = None
self.pressure_level = self.MAX_VALUE
self.stop_loss_level = 0
pass
def set_stock_code(self, stock_code):
self.stock_code = stock_code
def is_buy_position(self, history_prices, current_price):
history_prices = history_prices.append(current_price)
# 判断是否是反弹时间点
bull_up_list = self.bull_up_strategy\
.list_bull_up_stock(count=4, up_thread_hold=0.03,
stock_list=self.stock,
end_day=current_price['trade_day'],
prices=history_prices[history_prices['trade_day']
<= current_price['trade_day']],
safe_flag=True)
if bull_up_list[0].empty is False:
self.stock = bull_up_list[0]
self.pressure_level = self.stock['pressure_price']
self.stop_loss_level = self.stock['stop_loss_price']
current_dmi = self.total_dmi[self.total_dmi['trade_day'] == current_price['trade_day']]
if float(current_dmi['pdi'].tail(1)) > float(current_dmi['mdi'].tail(1)) \
and float(current_dmi['adx'].tail(1)) > 30:
return True
return False
def buy_action(self, history_prices, current_price):
self.buy_position_percent_value(current_price=current_price,
percent=0.8)
def is_sell_position(self, history_prices, current_price):
return super().is_sell_position(history_prices, current_price)
def sell_action(self, history_prices, current_price):
super().sell_action(history_prices, current_price)
def is_add_position(self, history_prices, current_price):
if float(current_price['close']) > float(self.pressure_level) \
and current_price['volume'] > history_prices.tail(5)['volume'].mean():
return True
return super().is_add_position(history_prices, current_price)
#放量突破压力位，加仓20%
def add_position_action(self, history_prices, current_price):
self.stop_loss_level = self.pressure_level
self.pressure_level = self.pressure_level * 1.05
self.buy_position_percent_value(current_price=current_price,
percent=0.6)
def is_reduce_position(self, history_prices, current_price):
if int(self.position) > 0 and float(current_price['close']) < float(self.pressure_level)\
< float(current_price['high']):
return True
return super().is_reduce_position(history_prices, current_price)
#遇到压力位如果当天最高价超过，但是收盘没有超过则减仓50%
def reduce_position_action(self, history_prices, current_price):
self.sell_position_percent_value(current_price=current_price, percent=0.5)
def is_clear_position(self, history_prices, current_price):
if int(self.position) > 0 and float(current_price['close']) < float(self.stop_loss_level):
return True
return super().is_clear_position(history_prices, current_price)
# 到达止损位，清仓出局
def clear_position_action(self, history_prices, current_price):
self.sell_position_percent_value(current_price=current_price,
percent=1)
def buy_fee(self):
return 5 #TODO 暂时固定按5元
def sell_fee(self):
return 5 #TODO 暂时固定按5元
def end_backtesting(self, history_prices, current_price):
if self.position > 0:
self.sell_position_percent_value(current_price=current_price,
percent=1)
def run(self):
if self.stock_code is None:
raise Exception("stock code can not be None")
self.stock = pd.DataFrame(self.offline_stock_action.query_by_stock_code(self.stock_code))
total_history_prices = self.offline_stock_action.query_all_history_prices(
self.stock_code)
mdi, pdi, adx = self.indicator_strategy.calculate_dmi(total_history_prices['high'],
total_history_prices['low'],
total_history_prices['close'])
dmis = {
'mdi': mdi,
'pdi': pdi,
'adx': adx,
'trade_day': total_history_prices['trade_day']
}
self.total_dmi = pd.DataFrame(dmis)
super().run_backtesting(total_history_prices=total_history_prices,
start_date=datetime.datetime(2019, 1, 1))
return float(round((self.balance - self.INIT_BALANCE)/self.INIT_BALANCE*100, 2))
class CCIBacktesing(BaseBackTesing):
def __init__(self):
BaseBackTesing.__init__(self)
self.stock_code = None
self.offline_stock_action = OfflineStockAction()
self.indicator_strategy = IndicatorStrategy()
self.cci = None
self.stock = None
def set_stock_code(self, stock_code):
self.stock_code = stock_code
def is_buy_position(self, history_prices, current_price):
pre_price = history_prices.tail(1)
pre_price = pre_price.reset_index(drop=True)
try:
pre_cci = self.cci[self.cci['trade_day'] == pre_price['trade_day']
[0]]
except IndexError:
print(pre_price['trade_day'][0])
raise Exception("ERROR")
cur_cci = self.cci[self.cci['trade_day'] == current_price['trade_day']]
if float(pre_cci['cci']) < 100 < float(cur_cci['cci']):
return True
return False
def buy_action(self, history_prices, current_price):
cur_cci = self.cci[self.cci['trade_day'] == current_price['trade_day']]
self.buy_position_percent_value(
current_price=current_price,
percent=round(float(cur_cci['cci']) / 100, 1) - 0.5)
def is_sell_position(self, history_prices, current_price):
pre_price = history_prices.tail(1)
pre_price = pre_price.reset_index(drop=True)
pre_cci = self.cci[self.cci['trade_day'] == pre_price['trade_day'][0]]
cur_cci = self.cci[self.cci['trade_day'] == current_price['trade_day']]
if float(pre_cci['cci']) > 100 > float(cur_cci['cci']):
return True
return False
def sell_action(self, history_prices, current_price):
if self.position > 0:
self.sell_position_percent_value(current_price=current_price,
percent=1)
def is_add_position(self, history_prices, current_price):
super().is_add_position(history_prices=history_prices,
current_price=current_price)
def add_position_action(self, history_prices, current_price):
super().add_position_action(history_prices=history_prices,
current_price=current_price)
def is_reduce_position(self, history_prices, current_price):
super().is_reduce_position(history_prices=history_prices,
current_price=current_price)
def reduce_position_action(self, history_prices, current_price):
super().reduce_position_action(history_prices=history_prices,
current_price=current_price)
def is_clear_position(self, history_prices, current_price):
if self.position > 0:
profit = self.balance - self.INIT_BALANCE
if profit < 0 and (-profit) / self.INIT_BALANCE > 0.08:
return True
return False
def clear_position_action(self, history_prices, current_price):
super().sell_position_percent_value(current_price=current_price,
percent=1)
def buy_fee(self):
return 5
def sell_fee(self):
return 5
def end_backtesting(self, history_prices, current_price):
if self.position > 0:
self.sell_position_percent_value(current_price=current_price,
percent=1)
def buy_position_percent_value(self, current_price, percent):
super().buy_position_percent_value(current_price, percent)
def sell_position_percent_value(self, current_price, percent):
super().sell_position_percent_value(current_price, percent)
def run(self):
if self.stock_code is None:
raise Exception("stock code can not be None")
self.stock = pd.DataFrame(
self.offline_stock_action.query_by_stock_code(self.stock_code))
total_history_prices = self.offline_stock_action.query_all_history_prices(
self.stock_code)
ccis = self.indicator_strategy.calculate_cci(
total_history_prices['high'], total_history_prices['low'],
total_history_prices['close'])
cci = {'cci': ccis, 'trade_day': total_history_prices['trade_day']}
self.cci = pd.DataFrame(cci)
super().run_backtesting(total_history_prices=total_history_prices,
start_date=datetime.datetime(2018, 1, 1))
return float(
round((self.balance - self.INIT_BALANCE) / self.INIT_BALANCE * 100,
2))