求问，提交模拟交易之后报错AttributeError: 'DataFrame' object has no attribute 'instrument'
由bql6wtb2创建，最终由small_q 被浏览 31 用户
新手课程里面的源码，运行回测是好的，但是提交模拟之后就会报错。错误和源码附上，求助大神帮我解答一下，谢谢
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-1-b2f5bb4c55e4> in <module>
191
192 # @module(position="241,670", comment='', comment_collapsed=True)
--> 193 m6 = M.hftrade.v2(
194 instruments=m1.data,
195 options_data=m5.data,
/var/app/enabled/biglearning/module2/common/modulemanagerv2.cpython-38-x86_64-linux-gnu.so in biglearning.module2.common.modulemanagerv2.BigQuantModuleVersion.__call__()
/var/app/enabled/biglearning/module2/common/moduleinvoker.cpython-38-x86_64-linux-gnu.so in biglearning.module2.common.moduleinvoker.module_invoke()
/var/app/enabled/biglearning/module2/common/moduleinvoker.cpython-38-x86_64-linux-gnu.so in biglearning.module2.common.moduleinvoker._invoke_with_cache()
/var/app/enabled/biglearning/module2/common/moduleinvoker.cpython-38-x86_64-linux-gnu.so in biglearning.module2.common.moduleinvoker._invoke_with_cache()
/var/app/enabled/biglearning/module2/common/moduleinvoker.cpython-38-x86_64-linux-gnu.so in biglearning.module2.common.moduleinvoker._module_run()
/var/app/enabled/biglearning/module2/modules/hftrade/v2/__init__.cpython-38-x86_64-linux-gnu.so in biglearning.module2.modules.hftrade.v2.__init__.bigquant_run()
/var/app/enabled/biglearning/module2/modules/hftrade/v2/__init__.cpython-38-x86_64-linux-gnu.so in biglearning.module2.modules.hftrade.v2.__init__.bigquant_run.do_paper_run()
/var/app/enabled/biglearning/module2/common/modulemanagerv2.cpython-38-x86_64-linux-gnu.so in biglearning.module2.common.modulemanagerv2.BigQuantModuleVersion.__call__()
/var/app/enabled/biglearning/module2/common/moduleinvoker.cpython-38-x86_64-linux-gnu.so in biglearning.module2.common.moduleinvoker.module_invoke()
/var/app/enabled/biglearning/module2/common/moduleinvoker.cpython-38-x86_64-linux-gnu.so in biglearning.module2.common.moduleinvoker._invoke_with_cache()
/var/app/enabled/biglearning/module2/common/moduleinvoker.cpython-38-x86_64-linux-gnu.so in biglearning.module2.common.moduleinvoker._invoke_with_cache()
/var/app/enabled/biglearning/module2/common/moduleinvoker.cpython-38-x86_64-linux-gnu.so in biglearning.module2.common.moduleinvoker._module_run()
/var/app/enabled/biglearning/module2/modules/hfpapertrading/v1/__init__.cpython-38-x86_64-linux-gnu.so in biglearning.module2.modules.hfpapertrading.v1.__init__.BigQuantModule.run()
/var/app/enabled/biglearning/module2/modules/hfpapertrading/v1/__init__.cpython-38-x86_64-linux-gnu.so in biglearning.module2.modules.hfpapertrading.v1.__init__.BigQuantModule.run()
/var/app/enabled/biglearning/module2/modules/hfpapertrading/v1/paper_test_helper.cpython-38-x86_64-linux-gnu.so in biglearning.module2.modules.hfpapertrading.v1.paper_test_helper.PaperTestHelper.get_orders()
/usr/local/python3/lib/python3.8/site-packages/pandas/core/generic.py in __getattr__(self, name)
5460 if self._info_axis._can_hold_identifiers_and_holds_name(name):
5461 return self[name]
-> 5462 return object.__getattribute__(self, name)
5463
5464 def __setattr__(self, name: str, value) -> None:
AttributeError: 'DataFrame' object has no attribute 'instrument'
源码如下，有没有大神帮我解答一下，谢谢
# 显式导入 BigQuant 相关 SDK 模块
from bigdatasource.api import DataSource
from bigdata.api.datareader import D
from biglearning.api import M
from biglearning.api import tools as T
from biglearning.module2.common.data import Outputs
import pandas as pd
import numpy as np
import math
import warnings
import datetime
from zipline.finance.commission import PerOrder
from zipline.api import get_open_orders
from zipline.api import symbol
from bigtrader.sdk import *
from bigtrader.utils.my_collections import NumPyDeque
from bigtrader.constant import OrderType
from bigtrader.constant import Direction
# <aistudiograph>
# @param(id="m6", name="initialize")
# 交易引擎：初始化函数，只执行一次
def m6_initialize_bigquant_run(context):
#读取数据
context.ranker_prediction = context.options['data'].read_df()
context.ranker_prediction.set_index('date',inplace=True)
#print(context.ranker_prediction)
# @param(id="m6", name="before_trading_start")
# 交易引擎：每个单位时间开盘前调用一次。
def m6_before_trading_start_bigquant_run(context, data):
# 盘前处理，订阅行情等
pass
# @param(id="m6", name="handle_tick")
# 交易引擎：tick数据处理函数，每个tick执行一次
def m6_handle_tick_bigquant_run(context, tick):
pass
# @param(id="m6", name="handle_data")
# 交易引擎：bar数据处理函数，每个时间单位执行一次
def m6_handle_data_bigquant_run(context, data):
#context = 回测引擎
#context内部 会有一些功能～ 是通过 context.xxx 来使用的
#data
#调仓期的控制
remainder = context.trading_day_index % 5
#如果没到调仓期直接结束运行
if remainder !=0:
return
import datetime
#初始化
buy_list = [] #买入列表
sell_list = [] #卖出列表
#==================== 数据准备
today = data.current_dt.strftime('%Y-%m-%d') #读取当天日期
time = data.current_dt
account_pos = context.get_account_positions()
holding_list = list({key: value for key, value in account_pos.items() if value.avail_qty > 0}.keys())
holding_num = len(holding_list)
#读取当日数据
try:
today_data = context.ranker_prediction.loc[today,:]
today_data.reset_index(inplace=True)
except:
return
#策略
today_data=today_data[today_data['上市时间'] >= 365] #上市时间的过滤
today_data=today_data[today_data['市盈率ttm'] >= 0] #财务数据过滤
today_data=today_data[today_data['换手排名'] <= 0.4]
today_data.sort_values(by='市值',ascending=True,inplace=True) #市值排序
#构建目标列表
target_list = today_data.instrument.to_list()[:40]
#构建卖出列表
for ins in holding_list:
if ins not in target_list:
sell_list.append(ins)
#构建买入列表
for ins in target_list:
if ins not in holding_list:
buy_list.append(ins)
#先卖
for ins in sell_list:
context.order_target(ins,0)
#等权买
for ins in buy_list:
context.order_target_percent(ins,0.025)
# @param(id="m6", name="handle_trade")
# 交易引擎：成交回报处理函数，每个成交发生时执行一次
def m6_handle_trade_bigquant_run(context, trade):
pass
# @param(id="m6", name="handle_order")
# 交易引擎：委托回报处理函数，每个委托变化时执行一次
def m6_handle_order_bigquant_run(context, order):
pass
# @param(id="m6", name="after_trading")
# 交易引擎：盘后处理函数，每日盘后执行一次
def m6_after_trading_bigquant_run(context, data):
pass
# @module(position="-61,137", comment='', comment_collapsed=True)
m1 = M.instruments.v2(
start_date=T.live_run_param('trading_date', '2022-01-01'),
end_date=T.live_run_param('trading_date', '2022-12-31'),
market='CN_STOCK_A',
instrument_list='',
max_count=0
)
# @module(position="658,76", comment='', comment_collapsed=True)
m2 = M.input_features.v1(
features="""
#构建一个因子。 open-close的绝对值
市值=market_cap_0
市盈率ttm=pe_ttm_0
上市时间=list_days_0
换手排名=rank_turn_0
"""
)
# @module(position="291,266", comment='', comment_collapsed=True)
m3 = M.general_feature_extractor.v7(
instruments=m1.data,
features=m2.data,
start_date='',
end_date='',
before_start_days=90
)
# @module(position="386,411", comment='', comment_collapsed=True)
m4 = M.derived_feature_extractor.v3(
input_data=m3.data,
features=m2.data,
date_col='date',
instrument_col='instrument',
drop_na=True,
remove_extra_columns=True,
user_functions={}
)
# @module(position="366,501", comment='', comment_collapsed=True)
m5 = M.chinaa_stock_filter.v1(
input_data=m4.data,
index_constituent_cond=['全部'],
board_cond=['上证主板', '深证主板', '创业板'],
industry_cond=['全部'],
st_cond=['正常'],
delist_cond=['非退市'],
output_left_data=False
)
# @module(position="241,670", comment='', comment_collapsed=True)
m6 = M.hftrade.v2(
instruments=m1.data,
options_data=m5.data,
start_date='',
end_date='',
initialize=m6_initialize_bigquant_run,
before_trading_start=m6_before_trading_start_bigquant_run,
handle_tick=m6_handle_tick_bigquant_run,
handle_data=m6_handle_data_bigquant_run,
handle_trade=m6_handle_trade_bigquant_run,
handle_order=m6_handle_order_bigquant_run,
after_trading=m6_after_trading_bigquant_run,
capital_base=1000000,
frequency='daily',
price_type='真实价格',
product_type='股票',
before_start_days='0',
volume_limit=1,
order_price_field_buy='close',
order_price_field_sell='open',
benchmark='000300.HIX',
plot_charts=True,
disable_cache=False,
replay_bdb=False,
show_debug_info=False,
backtest_only=False
)
# </aistudiograph>
\