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    In [5]:
    # 本代码由可视化策略环境自动生成 2024年1月2日 22:56
# 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):
    #context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))

    
    #读取数据
    context.ranker_prediction = context.options['data'].read_df()
    context.ranker_prediction.set_index('date',inplace=True)
    

# @param(id="m4", name="handle_data")
# 交易引擎：bar数据处理函数，每个时间单位执行一次
def m4_handle_data_bigquant_run(context, data):
    
    # 调仓时间，1就是一天一调仓
    remainder = context.trading_day_index % 5
    #如果没到调仓期直接结束运行
    if remainder !=0:
        return
        
    
    import datetime
    #初始化
    
    #==================== 数据准备
    today = data.current_dt.strftime('%Y-%m-%d')
    time = data.current_dt

    #获取持仓
    holding_list  = [equity.symbol for equity in context.portfolio.positions]
    holding_num = len(holding_list)
    
    # 持有股票的数量
    #holding_num = len(holding_list)

    #读取当日数据
    try:
        today_data  = context.ranker_prediction.loc[today,:]
        today_data.reset_index(inplace=True)
    except:
        return
    
    
    # 根据权重计算排序得分
    today_data['std_score'] = today_data['std(amount,10)'].rank(ascending=True)
    today_data['return_score'] = today_data['product(close/open,5)'].rank(ascending=True)

    # 计算总得分并按照总得分进行排序
    today_data['total_score'] = today_data['std_score'] #+today_data['return_score']
    today_data.sort_values(by='total_score',ascending=True,inplace=True)
 
    
    #构建保留目标列表
    target_buy_list = today_data.instrument.to_list()[:2]

    #构建卖出列表
    for ins in holding_list:
        if ins not in target_buy_list:
            context.order_target(context.symbol(ins),0)
            #sell_list.append(ins)
        
    #print(today)
    #print('holding_list:',holding_list)
    #print('target_buy_list:',target_buy_list)
    
    #构建买入列表
    for ins in target_buy_list:
        if ins not in holding_list: 
            context.order_percent(context.symbol(ins),0.48)

# @param(id="m4", name="prepare")
# 回测引擎：准备数据，只执行一次
def m4_prepare_bigquant_run(context):
    pass

# @param(id="m4", name="before_trading_start")
# 回测引擎：每个单位时间开始前调用一次，即每日开盘前调用一次。
def m4_before_trading_start_bigquant_run(context, data):
    pass


# @module(position="303,-154", comment='', comment_collapsed=True)
m2 = M.input_features.v1(
    features="""
# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
product(close/open,5)
std(amount,10)"""
)

# @module(position="-11,-155", comment='', comment_collapsed=True)
m3 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2017-12-01'),
    end_date=T.live_run_param('trading_date', '2023-12-31'),
    market='CN_FUND',
    instrument_list="""510050.HOF
510300.HOF
510500.HOF
512100.HOF
159915.ZOF
515100.HOF
510900.HOF
159938.ZOF
159928.ZOF
512660.HOF
515220.HOF
159869.ZOF
159792.ZOF
512480.HOF
515790.HOF
512930.HOF
516160.HOF
""",
    max_count=0
)

# @module(position="67,-61", comment='', comment_collapsed=True)
m5 = M.use_datasource.v2(
    instruments=m3.data,
    datasource_id='bar1d_CN_FUND',
    start_date='',
    end_date='',
    before_start_days=30
)

# @module(position="129,57", comment='', comment_collapsed=True)
m1 = M.derived_feature_extractor.v3(
    input_data=m5.data,
    features=m2.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=True,
    user_functions={}
)

# @module(position="138,156", comment='', comment_collapsed=True)
m6 = M.standardlize.v12(
    input_1=m1.data,
    input_2=m2.data,
    standard_func='ZScoreNorm',
    columns_input=''
)

# @module(position="86,277", comment='', comment_collapsed=True)
m4 = M.trade.v4(
    instruments=m3.data,
    options_data=m6.data,
    start_date='',
    end_date='',
    initialize=m4_initialize_bigquant_run,
    handle_data=m4_handle_data_bigquant_run,
    prepare=m4_prepare_bigquant_run,
    before_trading_start=m4_before_trading_start_bigquant_run,
    volume_limit=0,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.HIX'
)
# </aistudiograph>
    Trade(回测/模拟)
    • 收益率96.51%
    • 年化收益率12.21%
    • 基准收益率-14.35%
    • 阿尔法0.15
    • 贝塔0.86
    • 夏普比率0.51
    • 胜率0.65
    • 盈亏比2.78
    • 收益波动率21.28%
    • 信息比率0.07
    • 最大回撤34.02%