def test_get_or_create_library_not_exist():
lib_name = '{}{}'.format(RUN_TEST_LIBRARY, random.randint(1, 100))
lib = models.get_or_create_library(lib_name)
assert isinstance(lib, arctic.store.version_store.VersionStore)
models.drop_library(lib_name)
assert models.get_library(lib_name) is None
def __init__(self, coll_name):
self._lib_name = conf.CN_STOCK_LIBNAME
self._coll_name = coll_name
self._library = get_or_create_library(self._lib_name)
self._unused_cols = [
'price_change', 'p_change', 'ma5', 'ma10', 'ma20', 'v_ma5',
'v_ma10', 'v_ma20', 'turnover'
]
self._new_added_colls = []
def get_params(cls, stock_id):
"""
Get the params of the stock_id for this strategy.
:param stockid:
:return: dict(like dict(ma_periods=dict(ma_period_s=0, ma_period_l=0, stock_id='0')))
"""
lib = get_or_create_library(conf.STRATEGY_PARAMS_LIBNAME)
symbol = cls.name
params_list = lib.read(symbol).data
params = params_list.loc[stock_id, 'params']
return params
def write_daily_alert(cls, symbol, stock_id, action):
"""
write daily stock alert to MongoDB.
:param symbol: Arctic symbol
:param data: dict, like: {'stock': '000651', 'action': 'buy/sell'}
:return: None
"""
lib = get_or_create_library(conf.DAILY_STOCK_ALERT_LIBNAME)
data = {'stock': stock_id, 'action': action}
df = pd.DataFrame([data], columns=data.keys())
if symbol in lib.list_symbols():
lib.append(symbol, df)
else:
lib.write(symbol, df)
def test_get_or_create_library_exist():
lib = models.get_or_create_library(RUN_TEST_LIBRARY)
assert isinstance(lib, arctic.store.version_store.VersionStore)
"""
pool = gevent.pool.Pool(pool_size)
for i in range(len(stocks) // pool_size + 1):
start = i * pool_size
end = (i + 1) * pool_size
lst = stocks[start:end]
logger.debug(f'download delta data for stock list: {lst}')
for stock in lst:
pool.spawn(bdt.TsHisData.download_one_delta_data, stock)
pool.join(timeout=30)
if __name__ == '__main__':
# make sure the library exists
models.get_or_create_library(conf.CN_STOCK_LIBNAME)
# download_delta_data(['000651', '000001'])
# hs300s = ts.get_hs300s()
# stock_pools = hs300s['code'].tolist() if 'code' in hs300s else []
# 查询当前所有正常上市交易的股票列表
data = pro.stock_basic(exchange='SZSE', list_status='L', fields='ts_code,symbol,name,area,industry,list_date')
stock_pools = data['ts_code'].tolist() if 'ts_code' in data else []
# stock_pools = ['600000.SH','600036.SH']
if not stock_pools:
logger.warning('can not stock pools return empty.')
stock_pools = models.get_cn_stocks()
:return: None
"""
task = btasks.Task(bsm.MATrendStrategy, stock)
params = task.train()
# write stock params to MongoDB
symbol = conf.STRATEGY_PARAMS_MA_SYMBOL
models.save_training_params(symbol, params)
def main(stock_pools):
"""
Get all stocks and train params for each stock.
:param stock_pools: list, the stock code list.
:return: None
"""
for stock in stock_pools:
train(stock)
if __name__ == '__main__':
# create params library if not exist
models.get_or_create_library(conf.STRATEGY_PARAMS_LIBNAME)
cn_stocks = models.get_cn_stocks()
main(cn_stocks)
# training 时会写数据到 `conf.DAILY_STOCK_ALERT_LIBNAME`
models.drop_library(conf.DAILY_STOCK_ALERT_LIBNAME)