通过sql创建的SCORE为什么无法传输到M4模块

由bqbkm8ac创建，最终由bq7zuymm更新于2025-03-11 10:50 被浏览 8 用户

from bigmodule import M
import dai

# <aistudiograph>

# 初始化
# @param(id="m1", name="initialize")
def m1_initialize_bigquant_run(context):
    from bigtrader.finance.commission import PerOrder
    # 设置交易费率
    context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))

# 数据处理模块
# @param(id="m1", name="handle_data")
def m1_handle_data_bigquant_run(context, data):
    import pandas as pd

    # 判断是否为调仓周期
    if not context.rebalance_period.is_signal_date(data.current_dt.date()):
        return

    # 读取当前日期数据
    today_df = context.data[context.data["date"] == data.current_dt.strftime("%Y-%m-%d")]
    target_instruments = set(today_df["instrument"])

    # 获取当前持有股票
    holding_instruments = set(context.get_account_positions().keys())

    # 卖出不在目标持有列表中的股票
    for instrument in holding_instruments - 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():
        position = 0.0 if pd.isnull(x.position) else float(x.position)
        context.order_target_percent(x.instrument, position)

# 股票池筛选模块
# @module(comment="使用基本信息对股票池过滤")
m2 = M.cn_stock_basic_selector.v8(
    exchanges=["上交所", "深交所"],
    list_sectors=["主板", "科创板", "创业板"],
    indexes=["中证500", "上证指数", "创业板指", "深证成指", "上证50", "科创50"],
    st_statuses=["正常"],
    drop_suspended=True,
    m_name="m2"
)


# 因子特征模块
# @module(comment="因子特征")
m3 = M.input_features_dai.v30(
    input_1=m2.data,
    mode="表达式",
    expr="""
    """,  # Ensure triple quotes are correctly closed here.
    expr_filters="",
    expr_tables="cn_stock_prefactors",
    extra_fields="date, instrument",
    order_by="date, instrument",
    sql = """
SELECT
    date,
    instrument,
    0.4 * (
        c_zscore(roe_avg_ttm) * 0.3 + 
        (1/c_pct_rank(pe_ttm)) * 0.3 + 
        (1/c_pct_rank(pb)) * 0.2 + 
        c_zscore(cce_ps_ttm) * 0.2
    ) + 0.6 * (
        m_ta_sma(turn, 10) * 0.7 +
        c_pct_rank(volume/m_avg(volume, 5)) * 0.3  
    ) AS SCORE
FROM
    cn_stock_prefactors
WHERE
    st_status = 0
QUALIFY
    COLUMNS(*) IS NOT NULL
ORDER BY 
    date, 
    instrument,
    SCORE
""",
    extract_data=False,
    m_name="m3"
)

# 仓位分配模块
# @module(comment="持股数量、打分到仓位")
m4 = M.score_to_position.v4(
    input_1=m3.data,
    score_field="sorce",
    hold_count=5,
    position_expr="1 AS position",
    total_position=1,
    extract_data=False,
    m_name="m4"
)

# 数据抽取模块
# @module(comment="抽取预测数据")
m5 = M.extract_data_dai.v18(
    sql=m4.data,
    start_date="2022-01-01",
    start_date_bound_to_trading_date=True,
    end_date="2024-12-31",
    end_date_bound_to_trading_date=True,
    before_start_days=90,
    keep_before=False,
    debug=False,
    m_name="m5"
)

# 交易模块
# @module(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")
m6 = M.bigtrader.v35(
    data=m5.data,
    start_date="",
    end_date="",
    initialize=m1_initialize_bigquant_run,
    handle_data=m1_handle_data_bigquant_run,
    capital_base=500000,
    frequency="daily",
    product_type="股票",
    rebalance_period_type="交易日",
    rebalance_period_days="20",
    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="m6"
)
# </aistudiograph>

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