{"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"-41:sql","from_node_id":"-42:data"},{"to_node_id":"-52:data","from_node_id":"-41:data"}],"nodes":[{"node_id":"-42","module_id":"BigQuantSpace.input_features_dai.input_features_dai-v6","parameters":[{"name":"sql","value":"-- 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，这里对第二个因子简单地添加了0.9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*normalize(total_market_cap) + 0.6*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 \nINNER JOIN (\n SELECT date, instrument, industry_level1_name\n FROM cn_stock_industry_component\n WHERE industry_level1_name = '银行'\n \n )\nUSING(date, instrument) \n\n-- where 数据过滤\nWHERE\n st_status = 0\n\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 -- 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 -- 去掉有空值的行\n AND COLUMNS(*) IS NOT NULL\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":-288,"y":-180},{"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":"2023-10-01","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":-335,"y":-4},{"node_id":"-52","module_id":"BigQuantSpace.bigtrader.bigtrader-v7","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 - 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# 本代码由可视化策略环境自动生成 2023年12月28日 18:36
# 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="m3", name="initialize")
# 交易引擎：初始化函数，只执行一次
def m3_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="m3", name="before_trading_start")
# 交易引擎：每个单位时间开盘前调用一次。
def m3_before_trading_start_bigquant_run(context, data):
    # 盘前处理，订阅行情等
    pass

# @param(id="m3", name="handle_tick")
# 交易引擎：tick数据处理函数，每个tick执行一次
def m3_handle_tick_bigquant_run(context, tick):
    pass

# @param(id="m3", name="handle_data")
def m3_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="m3", 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 m3_handle_trade_bigquant_run(context, trade):
    pass

# @param(id="m3", 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 m3_handle_order_bigquant_run(context, order):
    pass

# @param(id="m3", name="after_trading")
# 交易引擎：盘后处理函数，每日盘后执行一次
def m3_after_trading_bigquant_run(context, data):
    pass


# @module(position="-288,-180", 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="""-- 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，这里对第二个因子简单地添加了0.9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*normalize(total_market_cap) + 0.6*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 
INNER JOIN (
        SELECT date, instrument, industry_level1_name
      FROM cn_stock_industry_component
      WHERE industry_level1_name = '银行'
      
    )
USING(date, instrument) 

-- where 数据过滤
WHERE
   st_status = 0

   
-- 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
    -- 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
    -- 去掉有空值的行
    AND COLUMNS(*) IS NOT NULL
-- 按因子值排名，从小到大
ORDER BY date, score
"""
)

# @module(position="-335,-4", 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='2023-10-01',
    end_date_bound_to_trading_date=True,
    before_start_days=90,
    debug=False
)

# @module(position="49,385", 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)
m3 = M.bigtrader.v7(
    data=m2.data,
    start_date='',
    end_date='',
    initialize=m3_initialize_bigquant_run,
    before_trading_start=m3_before_trading_start_bigquant_run,
    handle_tick=m3_handle_tick_bigquant_run,
    handle_data=m3_handle_data_bigquant_run,
    handle_trade=m3_handle_trade_bigquant_run,
    handle_order=m3_handle_order_bigquant_run,
    after_trading=m3_after_trading_bigquant_run,
    capital_base=100000,
    frequency='daily',
    product_type='股票',
    before_start_days=0,
    volume_limit=1,
    order_price_field_buy='open',
    order_price_field_sell='open',
    benchmark='000300.SH',
    plot_charts=True,
    disable_cache=True,
    debug=False,
    backtest_only=False
)
# </aistudiograph>

[2023-12-28 18:02:55.204468] INFO: moduleinvoker:1624193988.py:94: input_features_dai.v6 开始运行..
[2023-12-28 18:02:55.338299] INFO: moduleinvoker:1624193988.py:94: input_features_dai.v6 运行完成[0.13403s].
[2023-12-28 18:02:55.410327] INFO: moduleinvoker:1624193988.py:147: extract_data_dai.v7 开始运行..

2023-12-28 18:02:55 [info     ] start_date='2023-01-01', end_date='2023-10-01', query_start_date='2022-10-03' ..
2023-12-28 18:03:02 [info     ] data extracted: (22910, 3)

[2023-12-28 18:03:03.197121] INFO: moduleinvoker:1624193988.py:147: extract_data_dai.v7 运行完成[7.786791s].
[2023-12-28 18:03:03.299818] INFO: moduleinvoker:1624193988.py:158: bigtrader.v7 开始运行..

2023-12-28 18:03:03 [info     ] got metadata extra from input datasource
2023-12-28 18:03:03 [info     ] read data ..
2023-12-28 18:03:03 [info     ] start_date='2023-01-01', end_date='2023-10-01', instruments=42
2023-12-28 18:03:03 [info     ] bigtrader module V2.0.2
2023-12-28 18:03:03 [info     ] bigtrader engine v1.10.6 2023-12-26
2023-12-28 18:03:08 [info     ] backtest done, raw_perf_ds:dai.DataSource("_1904e41d034c433cad941fa1e2c14aaa")

[2023-12-28 18:03:08.432883] INFO: bigcharts.impl.render:render.py:494:render_chart Data is None, skip loading it to chart.

[2023-12-28 18:03:09.035771] INFO: moduleinvoker:1624193988.py:158: bigtrader.v7 运行完成[5.735933s].