def check(stocks, strategy, strategy_func):
end = None
m_filter = check_enter(end_date=end, strategy_fun=strategy_func)
results = list(filter(m_filter, stocks))
push.strategy(
'**************"{0}"**************\n{1}\n**************"{0}"**************\n'
.format(strategy, results))
def check_exit():
t_shelve = db.ShelvePersistence()
file = t_shelve.open()
for key in file:
code_name = file[key]['code_name']
data = utils.read_data(code_name)
if turtle_trade.check_exit(code_name, data):
push.strategy("{0} 达到退出条件".format(code_name))
del file[key]
elif turtle_trade.check_stop(code_name, data, file[key]):
push.strategy("{0} 达到止损条件".format(code_name))
del file[key]
file.close()
def check(code_name, data, end_date=None, threshold=60):
origin_data = data
if len(data) < threshold:
logging.debug("{0}:样本小于{1}天...\n".format(code_name, threshold))
return
data['ma60'] = pd.Series(tl.MA(data['close'].values, 60), index=data.index.values)
begin_date = data.iloc[0].date
if end_date is not None:
if end_date < begin_date: # 该股票在end_date时还未上市
logging.debug("{}在{}时还未上市".format(code_name, end_date))
return False
if end_date is not None:
mask = (data['date'] <= end_date)
data = data.loc[mask]
data = data.tail(n=threshold)
breakthrough_row = None
for index, row in data.iterrows():
if row['open'] < row['ma60'] <= row['close']:
if enter.check_volume(code_name, origin_data, row['date'], threshold):
breakthrough_row = row
if breakthrough_row is None:
return False
data_front = data.loc[(data['date'] < breakthrough_row['date'])]
data_end = data.loc[(data['date'] >= breakthrough_row['date'])]
for index, row in data_front.iterrows():
if not (-0.05 < (row['ma60'] - row['close']) / row['ma60'] < 0.2):
return False
push.strategy("股票{0} 突破日期：{1}".format(code_name, breakthrough_row['date']))
return True
def check(code_name, data, end_date=None, threshold=60):
if len(data) < 250:
logging.debug("{0}:样本小于250天...\n".format(code_name))
return
data['ma250'] = pd.Series(tl.MA(data['close'].values, 250),
index=data.index.values)
begin_date = data.iloc[0].date
if end_date is not None:
if end_date < begin_date: # 该股票在end_date时还未上市
logging.debug("{}在{}时还未上市".format(code_name, end_date))
return False
if end_date is not None:
mask = (data['date'] <= end_date)
data = data.loc[mask]
data = data.tail(n=threshold)
last_close = data.iloc[-1]['close']
# 区间最低点
lowest_row = data.iloc[-1]
# 区间最高点
highest_row = data.iloc[-1]
# 近期低点
recent_lowest_row = data.iloc[-1]
# 计算区间最高、最低价格
for index, row in data.iterrows():
if row['close'] > highest_row['close']:
highest_row = row
elif row['close'] < lowest_row['close']:
lowest_row = row
if lowest_row['volume'] == 0 or highest_row['volume'] == 0:
return False
data_front = data.loc[(data['date'] < highest_row['date'])]
data_end = data.loc[(data['date'] >= highest_row['date'])]
if data_front.empty:
return False
# 前半段由年线以下向上突破
if not (data_front.iloc[0]['close'] < data_front.iloc[0]['ma250']
and data_front.iloc[-1]['close'] > data_front.iloc[-1]['ma250']):
return False
if not data_end.empty:
# 后半段必须在年线以上运行（回踩年线）
for index, row in data_end.iterrows():
if row['close'] < row['ma250']:
return False
if row['close'] < recent_lowest_row['close']:
recent_lowest_row = row
date_diff = datetime.date(datetime.strptime(recent_lowest_row['date'], '%Y-%m-%d')) - \
datetime.date(datetime.strptime(highest_row['date'], '%Y-%m-%d'))
if not (timedelta(days=10) <= date_diff <= timedelta(days=50)):
return False
# 回踩伴随缩量
vol_ratio = highest_row['volume'] / recent_lowest_row['volume']
back_ratio = recent_lowest_row['close'] / highest_row['close']
if not (vol_ratio > 2 and back_ratio < 0.8):
return False
push.strategy("{0} 回撤幅度: {1}".format(code_name, 1 - back_ratio))
return True
def test_strategy():
settings.init()
strategy("")
strategy("1")