报错信息：ParserException: Parser Error: syntax error at or near "FROM" 是什么原因？
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源码如下：\n
from bigmodule import M
# <aistudiograph>
# @param(id="m5", name="initialize")
# 交易引擎：初始化函数，只执行一次
def m5_initialize_bigquant_run(context):
from bigtrader.finance.commission import PerOrder
# 系统已经设置了默认的交易手续费和滑点，要修改手续费可使用如下函数
context.set_commission(PerOrder(buy_cost=0.0002, sell_cost=0.0009, min_cost=5))
context.data.sort_values('score_rank', inplace=True)
# @param(id="m5", name="before_trading_start")
# 交易引擎：每个单位时间开盘前调用一次。
def m5_before_trading_start_bigquant_run(context, data):
# 盘前处理，订阅行情等
pass
# @param(id="m5", name="handle_tick")
# 交易引擎：tick数据处理函数，每个tick执行一次
def m5_handle_tick_bigquant_run(context, tick):
pass
# @param(id="m5", name="handle_data")
def m5_handle_data_bigquant_run(context, data):
import pandas as pd
# 下一个交易日不是调仓日，则不生成信号
if not context.rebalance_period.is_signal_date(data.current_dt.date()):
return
# 从传入的数据 context.data 中读取今天的信号数据
today_df = context.data[context.data["date"] == data.current_dt.strftime("%Y-%m-%d")]
target_instruments = list(today_df["instrument"])
# 获取当前已持有股票
holding_instruments = list(context.get_account_positions().keys())
# 卖出不在目标持有列表中的股票
for instrument in holding_instruments:
if instrument not in target_instruments:
context.order_target_percent(instrument, 0)
# 买入目标持有列表中的股票
for i, x in today_df.iterrows():
# 处理 null 或者 decimal.Decimal 类型等
position = 0.0 if pd.isnull(x.position) else float(x.position)
context.order_target_percent(x.instrument, position)
# @param(id="m5", 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 m5_handle_trade_bigquant_run(context, trade):
pass
# @param(id="m5", 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 m5_handle_order_bigquant_run(context, order):
pass
# @param(id="m5", name="after_trading")
# 交易引擎：盘后处理函数，每日盘后执行一次
def m5_after_trading_bigquant_run(context, data):
pass
# @module(position="-346,-871", comment="""使用基本信息对股票池过滤""")
m1 = M.cn_stock_basic_selector.v8(
exchanges=["""上交所""", """深交所"""],
list_sectors=["""主板""", """创业板""", """科创板"""],
indexes=["""中证1000"""],
st_statuses=["""正常"""],
margin_tradings=["""两融标的""", """非两融标的"""],
sw2021_industries=["""农林牧渔""", """采掘""", """基础化工""", """钢铁""", """有色金属""", """建筑建材""", """机械设备""", """电子""", """汽车""", """交运设备""", """信息设备""", """家用电器""", """食品饮料""", """纺织服饰""", """轻工制造""", """医药生物""", """公用事业""", """交通运输""", """房地产""", """金融服务""", """商贸零售""", """社会服务""", """信息服务""", """银行""", """非银金融""", """综合""", """建筑材料""", """建筑装饰""", """电力设备""", """国防军工""", """计算机""", """传媒""", """通信""", """煤炭""", """石油石化""", """环保""", """美容护理"""],
drop_suspended=True,
m_name="""m1"""
)
# @module(position="-315,-748", comment="""因子特征""")
m2 = M.input_features_dai.v30(
input_1=m1.data,
mode="""表达式""",
expr="""case when time between 93000 and 100000 then (nanmean(ask_price1) - nanmean(bid_price1)) / nanmean(close) else NULL end as BA_fac
case when time between 93000 and 103000 then (nanmean(bid_volume1)*0.5+nanmean(bid_volume2)*0.25+nanmean(bid_volume3)*0.125+nanmean(bid_volume4)*0.0625+nanmean(bid_volume5)*0.0625) else null end as _btemp
case when time between 93000 and 103000 then (nanmean(ask_volume1)*0.5+nanmean(ask_volume2)*0.25+nanmean(ask_volume3)*0.125+nanmean(ask_volume4)*0.0625+nanmean(ask_volume5)*0.0625) else NULL end as _atemp
(_btemp - _atemp) / (_btemp + _atemp) as ob_fac
case when time between 93000 and 100000 then (nanmean(close) - nanmean(open)) / nanmean(open) else null end as _pm
case when time between 93000 and 100000 then sum(volume) / nanmean(volume) else null end as _v
_pm * _v as vwpm_fac
BA_fac*0.3 + ob_fac*0.4 + vwpm_fac*0.3 as score
group by date,instrument
""",
expr_filters="""""",
expr_tables="""cpt_jyc_2025_stock_csi1000_bar1m""",
extra_fields="""date, instrument""",
order_by="""date, instrument""",
expr_drop_na=True,
extract_data=False,
m_name="""m2"""
)
# @module(position="-270,-626", comment="""持股数量、打分到仓位""")
m3 = M.score_to_position.v4(
input_1=m2.data,
score_field="""score DESC""",
hold_count=10,
position_expr="""1 / score_rank AS position
""",
total_position=1,
extract_data=False,
m_name="""m3"""
)
# @module(position="-241,-486", comment="""抽取预测数据""")
m4 = M.extract_data_dai.v20(
sql=m3.data,
start_date="""2023-01-01""",
start_date_bound_to_trading_date=True,
end_date="""2024-12-31""",
end_date_bound_to_trading_date=True,
before_start_days=7,
keep_before=False,
debug=False,
m_name="""m4"""
)
# @module(position="-212,-364", comment="""交易，日线，设置初始化函数和K线处理函数，以及初始资金、基准等""")
m5 = M.bigtrader.v47(
data=m4.data,
start_date="""""",
end_date="""""",
initialize=m5_initialize_bigquant_run,
before_trading_start=m5_before_trading_start_bigquant_run,
handle_tick=m5_handle_tick_bigquant_run,
handle_data=m5_handle_data_bigquant_run,
handle_trade=m5_handle_trade_bigquant_run,
handle_order=m5_handle_order_bigquant_run,
after_trading=m5_after_trading_bigquant_run,
capital_base=1000000,
frequency="""daily""",
product_type="""股票""",
rebalance_period_type="""交易日""",
rebalance_period_days="""5""",
rebalance_period_roll_forward=True,
backtest_engine_mode="""标准模式""",
before_start_days=0,
volume_limit=1,
order_price_field_buy="""open""",
order_price_field_sell="""open""",
benchmark="""沪深300指数""",
plot_charts=True,
debug=False,
backtest_only=False,
m_name="""m5"""
)
# </aistudiograph>
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