{"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"-41:sql","from_node_id":"-42:data"},{"to_node_id":"-72:data","from_node_id":"-41:data"}],"nodes":[{"node_id":"-42","module_id":"BigQuantSpace.input_features_dai.input_features_dai-v6","parameters":[{"name":"sql","value":"-- pragma enable_pushdown_window;\n-- 使用DAI SQL获取数据，构建因子等，如下是一个例子作为参考\n-- DAI SQL 语法: https://bigquant.com/wiki/doc/dai-PLSbc1SbZX#h-sql%E5%85%A5%E9%97%A8%E6%95%99%E7%A8%8B\n\nSELECT\n -- 【在这里输入因子表达式】\n -- DAI SQL 算子/函数: https://bigquant.com/wiki/doc/dai-PLSbc1SbZX#h-%E5%87%BD%E6%95%B0\n -- 数据&字段: 数据文档 https://bigquant.com/data/home\n -- 使用在时间截面的total_market_cap排名、五日日均成交量m_avg(amount_0, 5)作为本模版的两个因子\n -- 这里使用了DAI提供的c_normalize函数对因子进行z-score标准化处理以在多因子线性合成时不被量纲影响\n -- 还可以增加系数调节因子权重，这里对两个因子简单地添加了0.4、0，6的系数，在这个示例中没有经济学意义\n -- 另外可以使用DAI提供的cut_outliers函数去极值，c_indneutralize、c_neutralize函数进行行业、行业市值中性化\n 0.4 * c_normalize(total_market_cap) + 0.6 * c_normalize(m_avg(amount_0, 5)) AS score,\n\n -- 日期，这是每个股票每天的数据\n date,\n -- 股票代码，代表每一支股票\n instrument\n-- 预计算因子和数据 cn_stock_factors https://bigquant.com/data/datasources/cn_stock_factors\nFROM cn_stock_factors\n\n-- where 数据过滤\nWHERE\n\t-- 剔除ST股票\n st_status = 0\n -- 上市天数 > 270, 过滤掉新股\n AND list_days > 270\n -- 要求 pe > 0，-- 表示注释\n -- AND pe_ttm > 0\n -- 非停牌股\n AND suspended = 0\n -- 不属于北交所\n AND list_sector < 4\n\n -- 阳线 放量\n AND close_1 > open_1\n AND amount_1 / amount_2 > 3\n \n-- QUALIFY 数据过滤，支持过滤窗口函数，在 WHERE 之后才执行窗口函数。这里简化了一下，都放到QUALIFY了。对于专业用户建议分开WHERE和QUALIFY，有更好的升性能和准确性\nQUALIFY\n \n -- 去掉有空值的行\n COLUMNS(*) IS NOT NULL\n \n -- 一线穿3线\n AND m_avg(close,5) BETWEEN open_1 AND close_1\n AND m_avg(close,10) BETWEEN open_1 AND close_1\n AND m_avg(close,20) BETWEEN open_1 AND close_1\n AND m_max(change_ratio, 10) > 0.97\n \n -- \n AND close_1 / m_min(low, 10) > 1.09\n\n\n\n-- 按因子值排名，从小到大\nORDER BY date, score\n","type":"Literal","bound_global_parameter":null}],"input_ports":[],"output_ports":[{"name":"data","node_id":"-42"}],"cacheable":true,"seq_num":1,"comment":"通过SQL调用数据、因子和表达式等构建策略逻辑","comment_collapsed":false,"x":-419,"y":-156},{"node_id":"-41","module_id":"BigQuantSpace.extract_data_dai.extract_data_dai-v7","parameters":[{"name":"start_date","value":"2023-01-01","type":"Literal","bound_global_parameter":null},{"name":"start_date_bound_to_trading_date","value":"True","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2023-12-31","type":"Literal","bound_global_parameter":null},{"name":"end_date_bound_to_trading_date","value":"True","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":"90","type":"Literal","bound_global_parameter":null},{"name":"debug","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"sql","node_id":"-41"}],"output_ports":[{"name":"data","node_id":"-41"}],"cacheable":true,"seq_num":2,"comment":"抽取数据，设置数据开始时间和结束时间，并绑定模拟交易","comment_collapsed":false,"x":-371,"y":-25},{"node_id":"-72","module_id":"BigQuantSpace.bigtrader.bigtrader-v9","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"initialize","value":"# 交易引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n # 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\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n\n # 持有期/调仓周期，1天，3天，5天等\n context.holding_days = 1\n # 设置买入股票数量\n context.target_hold_count = 10\n # 每只股票的目标权重\n context.target_percent_per_instrument = 1.0 / context.target_hold_count\n","type":"Literal","bound_global_parameter":null},{"name":"before_trading_start","value":"# 交易引擎：每个单位时间开盘前调用一次。\ndef bigquant_run(context, data):\n # 盘前处理，订阅行情等\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"handle_tick","value":"# 交易引擎：tick数据处理函数，每个tick执行一次\ndef bigquant_run(context, tick):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"handle_data","value":"def bigquant_run(context, data):\n # 每 context.holding_days 个交易日调仓一次\n if context.trading_day_index % context.holding_days != 0:\n return\n\n # 获取当前日期\n current_date = data.current_dt.strftime(\"%Y-%m-%d\")\n # 获取当日数据\n current_day_data = context.data[context.data[\"date\"] == current_date]\n # 取前10只\n current_day_data = current_day_data.head(context.target_hold_count)\n # 获取当日目标持有股票\n target_hold_instruments = set(current_day_data[\"instrument\"])\n # 获取当前已持有股票\n current_hold_instruments = set(context.get_account_positions().keys())\n\n # 卖出不在目标持有列表中的股票\n for instrument in current_hold_instruments - target_hold_instruments:\n context.order_target_percent(instrument, 0)\n \n # 买入目标持有列表中的股票\n for instrument in target_hold_instruments - current_hold_instruments:\n context.order_target_percent(instrument, context.target_percent_per_instrument)\n","type":"Literal","bound_global_parameter":null},{"name":"handle_trade","value":"# 交易引擎：成交回报处理函数，每个成交发生时执行一次\ndef bigquant_run(context, trade):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"handle_order","value":"# 交易引擎：委托回报处理函数，每个委托变化时执行一次\ndef bigquant_run(context, order):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"after_trading","value":"# 交易引擎：盘后处理函数，每日盘后执行一次\ndef bigquant_run(context, data):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"capital_base","value":1000000,"type":"Literal","bound_global_parameter":null},{"name":"frequency","value":"daily","type":"Literal","bound_global_parameter":null},{"name":"product_type","value":"股票","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":0,"type":"Literal","bound_global_parameter":null},{"name":"volume_limit","value":1,"type":"Literal","bound_global_parameter":null},{"name":"order_price_field_buy","value":"open","type":"Literal","bound_global_parameter":null},{"name":"order_price_field_sell","value":"close","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000300.SH","type":"Literal","bound_global_parameter":null},{"name":"plot_charts","value":"True","type":"Literal","bound_global_parameter":null},{"name":"disable_cache","value":"False","type":"Literal","bound_global_parameter":null},{"name":"debug","value":"False","type":"Literal","bound_global_parameter":null},{"name":"backtest_only","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"data","node_id":"-72"},{"name":"options_data","node_id":"-72"},{"name":"history_ds","node_id":"-72"},{"name":"benchmark_ds","node_id":"-72"}],"output_ports":[{"name":"raw_perf","node_id":"-72"}],"cacheable":true,"seq_num":4,"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","comment_collapsed":false,"x":-251,"y":154}],"node_layout":"<node_postions><node_position Node='-42' Position='-419,-156,200,200'/><node_position Node='-41' Position='-371,-25,200,200'/><node_position Node='-72' Position='-251,154,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}

# 本代码由可视化策略环境自动生成 2024年2月19日 13:50
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码，粘贴到新建的代码单元或者策略，然后修改。
 
# 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="m4", name="initialize")
# 交易引擎：初始化函数，只执行一次
def m4_initialize_bigquant_run(context):
    # 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))

    # 持有期/调仓周期，1天，3天，5天等
    context.holding_days = 1
    # 设置买入股票数量
    context.target_hold_count = 10
    # 每只股票的目标权重
    context.target_percent_per_instrument = 1.0 / context.target_hold_count

# @param(id="m4", name="before_trading_start")
# 交易引擎：每个单位时间开盘前调用一次。
def m4_before_trading_start_bigquant_run(context, data):
    # 盘前处理，订阅行情等
    pass

# @param(id="m4", name="handle_tick")
# 交易引擎：tick数据处理函数，每个tick执行一次
def m4_handle_tick_bigquant_run(context, tick):
    pass

# @param(id="m4", name="handle_data")
def m4_handle_data_bigquant_run(context, data):
    # 每 context.holding_days 个交易日调仓一次
    if context.trading_day_index % context.holding_days != 0:
        return

    # 获取当前日期
    current_date = data.current_dt.strftime("%Y-%m-%d")
    # 获取当日数据
    current_day_data = context.data[context.data["date"] == current_date]
    # 取前10只
    current_day_data = current_day_data.head(context.target_hold_count)
    # 获取当日目标持有股票
    target_hold_instruments = set(current_day_data["instrument"])
    # 获取当前已持有股票
    current_hold_instruments = set(context.get_account_positions().keys())

    # 卖出不在目标持有列表中的股票
    for instrument in current_hold_instruments - target_hold_instruments:
        context.order_target_percent(instrument, 0)
        
    # 买入目标持有列表中的股票
    for instrument in target_hold_instruments - current_hold_instruments:
        context.order_target_percent(instrument, context.target_percent_per_instrument)

# @param(id="m4", 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 m4_handle_trade_bigquant_run(context, trade):
    pass

# @param(id="m4", 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 m4_handle_order_bigquant_run(context, order):
    pass

# @param(id="m4", name="after_trading")
# 交易引擎：盘后处理函数，每日盘后执行一次
def m4_after_trading_bigquant_run(context, data):
    pass


# @module(position="-419,-156", comment='通过SQL调用数据、因子和表达式等构建策略逻辑', comment_collapsed=False)
m1 = M.input_features_dai.v6(
    sql="""-- pragma enable_pushdown_window;
-- 使用DAI SQL获取数据，构建因子等，如下是一个例子作为参考
-- DAI SQL 语法: https://bigquant.com/wiki/doc/dai-PLSbc1SbZX#h-sql%E5%85%A5%E9%97%A8%E6%95%99%E7%A8%8B

SELECT
    -- 【在这里输入因子表达式】
    -- 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
    -- 使用在时间截面的total_market_cap排名、五日日均成交量m_avg(amount_0, 5)作为本模版的两个因子
    -- 这里使用了DAI提供的c_normalize函数对因子进行z-score标准化处理以在多因子线性合成时不被量纲影响
    -- 还可以增加系数调节因子权重，这里对两个因子简单地添加了0.4、0，6的系数，在这个示例中没有经济学意义
    -- 另外可以使用DAI提供的cut_outliers函数去极值，c_indneutralize、c_neutralize函数进行行业、行业市值中性化
    0.4 * c_normalize(total_market_cap) + 0.6 * c_normalize(m_avg(amount_0, 5)) AS score,

    -- 日期，这是每个股票每天的数据
    date,
    -- 股票代码，代表每一支股票
    instrument
-- 预计算因子和数据 cn_stock_factors https://bigquant.com/data/datasources/cn_stock_factors
FROM cn_stock_factors

-- where 数据过滤
WHERE
	-- 剔除ST股票
    st_status = 0
    -- 上市天数 > 270, 过滤掉新股
    AND list_days > 270
    -- 要求 pe > 0，-- 表示注释
    -- AND pe_ttm > 0
    -- 非停牌股
    AND suspended = 0
    -- 不属于北交所
    AND list_sector < 4

    -- 阳线 放量
    AND close_1 > open_1
    AND amount_1 / amount_2 > 3
   
-- QUALIFY 数据过滤，支持过滤窗口函数，在 WHERE 之后才执行窗口函数。这里简化了一下，都放到QUALIFY了。对于专业用户建议分开WHERE和QUALIFY，有更好的升性能和准确性
QUALIFY
    
    -- 去掉有空值的行
    COLUMNS(*) IS NOT NULL
    
    -- 一线穿3线
    AND m_avg(close,5) BETWEEN open_1 AND close_1
    AND m_avg(close,10) BETWEEN open_1 AND close_1
    AND m_avg(close,20) BETWEEN open_1 AND close_1
    AND m_max(change_ratio, 10) > 0.97
    
    -- 
    AND close_1 / m_min(low, 10) > 1.09



-- 按因子值排名，从小到大
ORDER BY date, score
"""
)

# @module(position="-371,-25", comment='抽取数据，设置数据开始时间和结束时间，并绑定模拟交易', comment_collapsed=False)
m2 = M.extract_data_dai.v7(
    sql=m1.data,
    start_date='2023-01-01',
    start_date_bound_to_trading_date=True,
    end_date='2023-12-31',
    end_date_bound_to_trading_date=True,
    before_start_days=90,
    debug=False
)

# @module(position="-251,154", 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', comment_collapsed=False)
m4 = M.bigtrader.v9(
    data=m2.data,
    start_date='',
    end_date='',
    initialize=m4_initialize_bigquant_run,
    before_trading_start=m4_before_trading_start_bigquant_run,
    handle_tick=m4_handle_tick_bigquant_run,
    handle_data=m4_handle_data_bigquant_run,
    handle_trade=m4_handle_trade_bigquant_run,
    handle_order=m4_handle_order_bigquant_run,
    after_trading=m4_after_trading_bigquant_run,
    capital_base=1000000,
    frequency='daily',
    product_type='股票',
    before_start_days=0,
    volume_limit=1,
    order_price_field_buy='open',
    order_price_field_sell='close',
    benchmark='000300.SH',
    plot_charts=True,
    disable_cache=False,
    debug=False,
    backtest_only=False
)
# </aistudiograph>

[2024-02-19 13:50:11.107989] INFO: moduleinvoker:2011061236.py:94: input_features_dai.v6 开始运行..
[2024-02-19 13:50:11.186336] INFO: moduleinvoker:2011061236.py:94: input_features_dai.v6 运行完成[0.078369s].
[2024-02-19 13:50:11.209631] INFO: moduleinvoker:2011061236.py:156: extract_data_dai.v7 开始运行..

2024-02-19 13:50:11 [info     ] start_date='2023-01-01', end_date='2023-12-31', query_start_date='2022-10-03' ..
2024-02-19 13:50:24 [info     ] data extracted: (0, 3)

[2024-02-19 13:50:24.757906] INFO: moduleinvoker:2011061236.py:156: extract_data_dai.v7 运行完成[13.548276s].
[2024-02-19 13:50:24.785682] INFO: moduleinvoker:2011061236.py:167: bigtrader.v9 开始运行..

2024-02-19 13:50:24 [info     ] got metadata extra from input datasource
2024-02-19 13:50:24 [info     ] read data ..

[2024-02-19 13:50:24.851920] ERROR: moduleinvoker:2011061236.py:167: module name: bigtrader, module version: v9, trackeback: ValueError: NaTType does not support strftime

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[5], line 167
    156 m2 = M.extract_data_dai.v7(
    157     sql=m1.data,
    158     start_date='2023-01-01',
   (...)
    163     debug=False
    164 )
    166 # @module(position="-251,154", 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', comment_collapsed=False)
--> 167 m4 = M.bigtrader.v9(
    168     data=m2.data,
    169     start_date='',
    170     end_date='',
    171     initialize=m4_initialize_bigquant_run,
    172     before_trading_start=m4_before_trading_start_bigquant_run,
    173     handle_tick=m4_handle_tick_bigquant_run,
    174     handle_data=m4_handle_data_bigquant_run,
    175     handle_trade=m4_handle_trade_bigquant_run,
    176     handle_order=m4_handle_order_bigquant_run,
    177     after_trading=m4_after_trading_bigquant_run,
    178     capital_base=1000000,
    179     frequency='daily',
    180     product_type='股票',
    181     before_start_days=0,
    182     volume_limit=1,
    183     order_price_field_buy='open',
    184     order_price_field_sell='close',
    185     benchmark='000300.SH',
    186     plot_charts=True,
    187     disable_cache=False,
    188     debug=False,
    189     backtest_only=False
    190 )
    191 # </aistudiograph>

File module2/common/modulemanagerv2.py:88, in biglearning.module2.common.modulemanagerv2.BigQuantModuleVersion.__call__()

File module2/common/moduleinvoker.py:370, in biglearning.module2.common.moduleinvoker.module_invoke()

File module2/common/moduleinvoker.py:292, in biglearning.module2.common.moduleinvoker._invoke_with_cache()

File module2/common/moduleinvoker.py:253, in biglearning.module2.common.moduleinvoker._invoke_with_cache()

File module2/common/moduleinvoker.py:210, in biglearning.module2.common.moduleinvoker._module_run()

File /var/app/data/custom_module/publicmodules/bigtrader/v9/__init__.py:301, in run(start_date, end_date, data, initialize, before_trading_start, handle_tick, handle_data, handle_trade, handle_order, after_trading, options_data, history_ds, benchmark_ds, capital_base, frequency, product_type, before_start_days, volume_limit, order_price_field_buy, order_price_field_sell, benchmark, plot_charts, disable_cache, debug, backtest_only, m_meta_kwargs)
    297     else:
    298         # use cppbacktest
    299         from .core.cppbacktest import BigQuantModule
--> 301     outputs = BigQuantModule(
    302         start_date=start_date,
    303         end_date=end_date,
    304         **trading_params,
    305         plot_charts=plot_charts,
    306         disable_cache=disable_cache,
    307         **m_meta_kwargs,
    308     ).run()
    309 elif backtest_only:
    310     run_mode = None

File /var/app/data/custom_module/publicmodules/bigtrader/v9/core/pybacktest/__init__.py:168, in BigQuantModule.__init__(self, start_date, end_date, data, prepare, initialize, after_trading, on_timer, on_message, before_trading_start, handle_data, handle_tick, handle_l2trade, handle_l2order, handle_trade, handle_order, capital_base, product_type, adjust_type, frequency, benchmark, plot_charts, options_data, debug, **kwargs)
    162     instruments = json.loads(data)
    163 elif isinstance(data, pd.DataFrame):
    164     instruments = {
    165         # "market": "CN_STOCK_A",
    166         "instruments": list(set(data["instrument"])),
    167         # TODO: to seconds?
--> 168         "start_date": data["date"].min().strftime("%Y-%m-%d"),
    169         "end_date": data["date"].max().strftime("%Y-%m-%d"),
    170     }
    171 else:
    172     raise Exception(f"not supported instruments type: {type(data)}")

File pandas/_libs/tslibs/nattype.pyx:72, in pandas._libs.tslibs.nattype._make_error_func.f()

ValueError: NaTType does not support strftime