def run(self, save_trade_record=False):
self.initialize()
# 初始化 account_data
if self.account:
for account in self.account:
Environment.current_account_data = AccountData()
Environment.current_account_data.account_id = generate_random_id.generate_random_id(account)
Environment.current_account_data.total_balance = self.capital[account]
Environment.current_account_data.available = self.capital[account]
# Environment.logger(Environment.current_account_data.account_id, Environment.current_account_data.available)
Environment.bar_account_data_list.append(Environment.current_account_data)
# if self.run_mode == RunMode.TRADE.value:
# self.end = self._get_data.get_end_time_tag(benchmark=self.benchmark, period=Period.DAILY.value)
# 缓存数据开关，和bar_index的计算
if self.period == Period.DAILY.value:
self.daily_data_cache = True
elif self.period == Period.ONE_MIN.value:
self.one_min_data_cache = True
#
security_list = copy.copy(self.universe)
security_list = list(set(security_list))
if self._daily_data_cache:
Environment.daily_data = self._get_data.cache_all_stock_data(dividend_type=self.rights_adjustment)
Environment.index_daily_data = self._get_data.cache_all_index_data()
if self.one_min_data_cache:
Environment.one_min_data = self._get_data.cache_all_stock_data(period=Period.ONE_MIN.value)
if self.period == Period.DAILY.value:
Environment.benchmark_index = [i for i in Environment.index_daily_data['close'][self.benchmark].index
if self.start <= i <= self.end]
elif self.period == Period.ONE_MIN.value:
Environment.benchmark_index = [data_transfer.date_to_millisecond(str(int(i)), '%Y%m%d') for i in
Environment.one_min_data['open'].ix[self.benchmark].index
if i >= data_transfer.date_str_to_int(self.start)]
self.bar_index = 0
while True:
try:
# Environment.logger(self.time_tag, Environment.benchmark_index)
self.time_tag = Environment.benchmark_index[self.bar_index]
except IndexError:
if self.run_mode == RunMode.BACKTESTING.value:
if save_trade_record:
run_backtesting_analysis_engine(self)
break
# elif self.run_mode == RunMode.TRADE.value:
# 读取最新tick, 更新最新的分钟或者日线
# if 读取最新tick, 更新最新的分钟或者日线 == done:
# daily_data.append(new_day_data)
# self.bar_index += 1
# benchmark_index.append(new_day_time_tag)
else:
run_bar_engine(self)
def get_all_market_data(self,
stock_code=[],
field=[],
start="",
end="",
period=Period.DAILY.value,
rights_adjustment=RightsAdjustment.NONE.value):
"""
复权因子和skip_pause在这做
:param stock_code:
:param field:
:param start:
:param end:
:param period:
:param rights_adjustment:
:return: 代码-n，字段-n，时间-n, return dataframe 行-代码-timetag(多层索引)，列-字段
"""
if period == Period.DAILY.value:
db_name = DatabaseName.MARKET_DATA_DAILY.value
end = data_transfer.date_str_to_int(end)
values = []
colum = {"_id": 0, "timetag": 1}
for i in field:
colum[i] = 1
for stock in stock_code:
self.conn.check_connected()
stock_market_data = self.conn.select_colum(
db_name=db_name,
table=stock,
value={"timetag": {
"$lte": end
}},
colum=colum)
stock_market_data_list = list(stock_market_data)
if stock_market_data_list:
df = pd.DataFrame(stock_market_data_list)
values.append(
pd.DataFrame(df[field].values,
index=df['timetag'],
columns=field))
market_data = pd.concat(values, keys=stock_code)
elif period == Period.ONE_MIN.value:
db_name = DatabaseName.MARKET_DATA_ONE_MIN.value
return market_data
def run(self, save_trade_record=False):
self.initialize()
# 初始化 account_data
if self.account:
for account in self.account:
Environment.current_account_data = AccountData()
Environment.current_account_data.account_id = generate_random_id.generate_random_id(
account)
Environment.current_account_data.total_balance = self.capital[
account]
Environment.current_account_data.available = self.capital[
account]
Environment.bar_account_data_list.append(
Environment.current_account_data)
if self.run_mode == RunMode.TRADE.value:
self.end = self._get_data.get_end_timetag(
benchmark=self.benchmark, period=Period.DAILY.value)
# 缓存数据开关，和bar_index的计算
if self.period == Period.DAILY.value:
self.daily_data_cache = True
elif self.period == Period.ONE_MIN.value:
self.one_min_data_cache = True
stock_list = copy.copy(self.universe)
stock_list.append(self.benchmark)
stock_list = list(set(stock_list))
if self._daily_data_cache:
Environment.daily_data = self._get_data.get_all_market_data(
stock_code=stock_list,
field=["open", "high", "low", "close", "volumn", "amount"],
end=self.end,
period=Period.DAILY.value)
if self.one_min_data_cache:
Environment.one_min_data = self._get_data.get_all_market_data(
stock_code=stock_list,
field=["open", "high", "low", "close", "volumn", "amount"],
end=self.end,
period=Period.ONE_MIN.value)
if self.period == Period.DAILY.value:
Environment.benchmark_index = [
data_transfer.date_to_millisecond(str(int(i)), '%Y%m%d') for i
in Environment.daily_data["open"].ix[self.benchmark].index
if i >= data_transfer.date_str_to_int(self.start)
]
elif self.period == Period.ONE_MIN.value:
Environment.benchmark_index = [
data_transfer.date_to_millisecond(str(int(i)), '%Y%m%d') for i
in Environment.one_min_data["open"].ix[self.benchmark].index
if i >= data_transfer.date_str_to_int(self.start)
]
# print(self.benchmark, self.start, self.end, self.period, self.rights_adjustment, self.run_mode)
self.bar_index = 0
while True:
try:
self.timetag = Environment.benchmark_index[self.bar_index]
except IndexError:
if self.run_mode == RunMode.BACKTESTING.value:
if save_trade_record:
run_backtesting_analysis_engine(self)
break
elif self.run_mode == RunMode.TRADE.value:
'''读取最新tick, 更新最新的分钟或者日线
if 读取最新tick, 更新最新的分钟或者日线 == done:
daily_data.append(new_day_data)
self.bar_index += 1
benchmark_index.append(new_day_timetag)
'''
pass
else:
date = int(
data_transfer.millisecond_to_date(millisecond=self.timetag,
format="%Y%m%d"))
run_bar_engine(self)
@abstractmethod
def initialize(self):
pass
@abstractmethod
def handle_bar(self, event):
pass
def get_market_data(self,
market_data,
stock_code=[],
field=[],
start="",
end="",
count=-1):
"""
从dataframe解析数据成最终的数据格式，count都在在这里做
因为停牌或者其他原因取不到数据的，１ ２ ３ 返回的是－１，其他返回的是pandas的空或者NaN，所以可以使用 ＞０判断是否取到值
:param market_data:
:param stock_code:
:param field:
:param start:
:param end:
:param count:
:return:
"""
if start != "":
start = data_transfer.date_str_to_int(start)
else:
start = 0
if end != "":
end = data_transfer.date_str_to_int(end)
else:
end = 0
# （１）代码-1，字段-1，时间-1, return float
if len(stock_code) == 1 and len(field) == 1 and (
start == end) and count == -1:
try:
return market_data[field[0]].ix[stock_code[0], end]
# 停牌或者其他情情况取不到数据的返回-1
except:
return -1
# （２）代码-n，字段-1，时间-1, return Series
elif len(stock_code) > 1 and len(field) == 1 and (
start == end) and count == -1:
result_dict = {}
for stock in stock_code:
try:
result_dict[stock] = market_data[field[0]].ix[stock, end]
except:
result_dict[stock] = -1
return pd.Series(result_dict)
# （３）代码-1，字段-n，时间-1, return Series
elif len(stock_code) == 1 and len(field) > 1 and (
start == end) and count == -1:
result_dict = {}
for field_one in field:
try:
result_dict[field_one] = market_data[field_one].ix[
stock_code[0], end]
except:
result_dict[field_one] = -1
return pd.Series(result_dict)
# （４）代码-1，字段-1，时间-n, return Series
elif len(stock_code) == 1 and len(field) == 1 and (
start != end) and count == -1:
try:
series = market_data[field[0]].ix[stock_code[0]]
except KeyError:
return pd.Series()
series = series[series.index >= start]
series = series[series.index <= end]
return series
# （５）代码-n，字段-1，时间-n, return dataframe 行-timetag，列-代码
elif len(stock_code) > 1 and len(field) == 1 and (start !=
end) and count == -1:
result_dict = {}
for stock in stock_code:
index = market_data.ix[stock].index
index = index[index <= end]
index = index[index >= start]
result_dict[stock] = market_data[field[0]].ix[stock][index]
return pd.DataFrame(result_dict)
# （６）代码-n，字段-n，时间-1, return dataframe 行-字段，列-代码
elif len(stock_code) > 1 and len(field) > 1 and (
start == end) and count == -1:
result_dict = {}
for stock in stock_code:
try:
result_dict[stock] = market_data.ix[stock].ix[end]
except:
result_dict[stock] = pd.Series()
return pd.DataFrame(result_dict).ix[field]
# （７）代码-1，字段-n，时间-n, return dataframe 行-timetag，列-字段
elif len(stock_code) == 1 and len(field) > 1 and (start !=
end) and count == -1:
index = market_data.ix[stock_code[0]].index
index = index[index <= end]
index = index[index >= start]
return market_data.ix[stock_code[0]][field].ix[index]
# 代码-n，字段-n，时间-n, return dataframe 行-代码-timetag(多层索引)，列-字段
else:
result_dict = {}
for stock in stock_code:
index = market_data.ix[stock].index
index = index[index <= end]
index = index[index >= start]
result_dict[stock] = market_data.ix[stock][field].ix[index]
return pd.concat(result_dict, keys=stock_code)
