陈雨作业，0805

由bqtzejx8创建，最终由bqtzejx8更新于2025-08-10 07:53 被浏览 8 用户

非常感谢张伟同学和四金同学的作业能给我看，我还好多代码不会写。

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=0.0003, sell_cost=0.0013, 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

    current_date = data.current_dt.strftime("%Y-%m-%d")
    print(f"========== current_date {current_date} \n")
    # 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"] == current_date]
    target_instruments = list(today_df["instrument"])
    # 获取当前已持有股票
    holding_instruments = list(context.get_account_positions().keys())

    # =========== 大盘风控
    market_data = context.options['data'].read();
    current_day_data2 = market_data[market_data["date"] == current_date]

    market_chg = current_day_data2["market_chg"].iloc[0] - 1 
    print(f"============ {current_date}，近3日大盘涨跌幅{market_chg}")
    if(market_chg < -0.05):
        print(f"============ {current_date}，近3日大盘涨跌幅{market_chg}, 触发大盘风控，全仓卖出")
        # 获取当前已持有股票
        for instrument in holding_instruments:
            print(f"============ {current_date},stock {instrument},大盘风控卖出")
            context.order_target_percent(instrument, 0)
        return

    # =========== 个股止盈
    for instrument in holding_instruments:
        # 股票买入价
        stock_cost = context.get_position(instrument).cost_price
        # 股票当前价格
        stock_market_price = context.get_position(instrument).last_price
        stock_chg = (stock_market_price / stock_cost ) - 1 
        print(f"============ {current_date},stock {instrument},chgPrice  {stock_chg}")
        if (stock_chg < -0.01):
            # 触发止损
            print(f"============ {current_date},stock {instrument},chgPrice  {stock_chg}，止损卖出")
            context.order_target_percent(instrument, 0)
        elif(stock_chg > 0.03):
            # 触发止盈
            print(f"============ {current_date},stock {instrument},chgPrice  {stock_chg}，止盈卖出")
            context.order_target_percent(instrument, 0)


    # =========== 正常换手
    # 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="56,-726", comment="""因子特征""", comment_collapsed=True)
m1 = M.input_features_dai.v30(
    mode="""表达式""",
    expr="""-- DAI SQL 算子/函数: https://bigquant.com/wiki/doc/dai-PLSbc1SbZX#h-%E5%87%BD%E6%95%B0
-- 数据&字段: 数据文档 https://bigquant.com/data/home
-- 数据使用: 表名.字段名, 对于没有指定表名的列，会从 expr_tables 推断
close
m_lag(close, 10) as pre_close
close / pre_close as market_chg
""",
    expr_filters="""instrument = '000001.SH'""",
    expr_tables="""cn_stock_index_bar1d""",
    extra_fields="""date, instrument""",
    order_by="""date, instrument""",
    expr_drop_na=True,
    sql="""SELECT date, instrument, m_stddev(turn, 30) AS turn_std, sw2021_level1, 
turn_std - AVG(turn_std) OVER (PARTITION BY sw2021_level1) AS score
FROM cn_stock_prefactors 
WHERE list_sector = 1
AND is_risk_warning = 0
AND close / adjust_factor > 5
AND list_days >= 200

QUALIFY COLUMNS(*) IS NOT NULL
ORDER BY date""",
    extract_data=False,
    m_name="""m1"""
)

# @module(position="86,-560", comment="""抽取预测数据""", comment_collapsed=True)
m6 = M.extract_data_dai.v20(
    sql=m1.data,
    start_date="""2019-01-01""",
    start_date_bound_to_trading_date=True,
    end_date="""2024-04-29""",
    end_date_bound_to_trading_date=True,
    before_start_days=90,
    keep_before=False,
    debug=False,
    m_name="""m6"""
)

# @module(position="-487,-832", comment="""因子特征""")
m2 = M.input_features_dai.v30(
    mode="""SQL""",
    expr="""-- DAI SQL 算子/函数: https://bigquant.com/wiki/doc/dai-PLSbc1SbZX#h-%E5%87%BD%E6%95%B0
-- 数据&字段: 数据文档 https://bigquant.com/data/home
-- 数据使用: 表名.字段名, 对于没有指定表名的列，会从 expr_tables 推断

m_stddev(turn, 30) AS score
-- 使用 float 类型。默认是高精度 decimal.Decimal, 不能和float直接相乘""",
    expr_filters="""list_sector = 1
is_risk_warning = 0
close / adjust_factor > 5
list_days >= 200""",
    expr_tables="""cn_stock_prefactors_community""",
    extra_fields="""date, instrument""",
    order_by="""date, instrument""",
    expr_drop_na=True,
    sql="""SELECT date, instrument, m_stddev(turn, 30) AS turn_std, sw2021_level1, 
turn_std - AVG(turn_std) OVER (PARTITION BY sw2021_level1) AS score
FROM cn_stock_prefactors 
WHERE list_sector = 1
AND is_risk_warning = 0
AND close / adjust_factor > 5
AND list_days >= 200

QUALIFY COLUMNS(*) IS NOT NULL
ORDER BY date""",
    extract_data=False,
    m_name="""m2"""
)

# @module(position="-305,-645", comment="""持股数量、打分到仓位""")
m3 = M.score_to_position.v4(
    input_1=m2.data,
    score_field="""score ASC""",
    hold_count=5,
    position_expr="""-- DAI SQL 算子/函数: https://bigquant.com/wiki/doc/dai-PLSbc1SbZX#h-%E5%87%BD%E6%95%B0
-- 在这里输入表达式, 每行一个表达式, 输出仓位字段必须命名为 position, 模块会进一步做归一化
-- 排序倒数: 1 / score_rank AS position
-- 对数下降: 1 / log2(score_rank + 1) AS position
-- TODO 拟合、最优化 ..

-- 等权重分配
1 AS position
""",
    total_position=1,
    extract_data=False,
    m_name="""m3"""
)

# @module(position="-245,-493", comment="""抽取预测数据""")
m4 = M.extract_data_dai.v20(
    sql=m3.data,
    start_date="""2019-01-01""",
    start_date_bound_to_trading_date=True,
    end_date="""2024-04-29""",
    end_date_bound_to_trading_date=True,
    before_start_days=90,
    keep_before=False,
    debug=False,
    m_name="""m4"""
)

# @module(position="-157,-361", 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,
    options_data=m6.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=15000,
    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="""中证500指数""",
    plot_charts=True,
    debug=False,
    backtest_only=False,
    m_name="""m5"""
)
# </aistudiograph>

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