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== data.current_dt.strftime('%Y-%m-%d')]\n\n now_stock = []\n sell_stock = []\n \n try:\n buy_list = list(ranker_prediction.instrument)[:stock_count]\n except:\n buy_list = []\n\n #获取每支票的买入金额，总资产/持仓数量\n cash_for_buy=context.portfolio.portfolio_value/stock_count\n\n\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n\n if len(equities) > 0:\n for i in equities.keys():\n last_sale_date = equities[i].last_sale_date\t# 上次交易日期\n delta_days = data.current_dt - last_sale_date \n hold_days = delta_days.days # 持仓天数\n if i not in buy_list and hold_days>=context.options['hold_days']:\n context.order_target(context.symbol(i), 0)\n sell_stock.append(i)\n stock_now = stock_now -1\n\n # 3. 生成买入订单\n buy_num = min(len(buy_list),stock_count)\n if buy_num>0 and len(buy_list)>0: \n cash_for_buy=context.portfolio.portfolio_value/10\n # 不再买入已经轮仓卖出和移动止损的股票,以防止出现空头持仓\n buy_instruments = [i for i in buy_list if i not in 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    In [1]:
    # 本代码由可视化策略环境自动生成 2023年11月23日 16:20
# 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="m19", name="initialize")
# 回测引擎：初始化函数，只执行一次
def m19_initialize_bigquant_run(context):
    # 加载预测数据
    context.ranker_prediction = context.options['data'].read_df()

    # 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))
    # 预测数据，通过options传入进来，使用 read_df 函数，加载到内存 (DataFrame)
    # 设置买入的股票数量，这里买入预测股票列表排名靠前的5只
    context.stock_count = 5
    # 每只的股票的权重，如下的权重分配会使得靠前的股票分配多一点的资金，[0.339160, 0.213986, 0.169580, ..]
    context.stock_weights = [1]
    # 设置每只股票占用的最大资金比例
    context.max_cash_per_instrument = 1
    context.options['hold_days'] = 13

# @param(id="m19", name="handle_data")
# 回测引擎：每日数据处理函数，每天执行一次
def m19_handle_data_bigquant_run(context, data):
    today = data.current_dt.strftime('%Y-%m-%d')
    equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
    stock_now = len(equities)
    stock_count = context.stock_count
    # 按日期过滤得到今日的预测数据
    # 加载预测数据
    ranker_prediction = context.ranker_prediction[
    context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]

    now_stock = []
    sell_stock = []
       
    try:
        buy_list = list(ranker_prediction.instrument)[:stock_count]
    except:
        buy_list = []

    #获取每支票的买入金额，总资产/持仓数量
    cash_for_buy=context.portfolio.portfolio_value/stock_count


    positions = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.perf_tracker.position_tracker.positions.items()}

    if len(equities) > 0:
            for i in equities.keys():
                last_sale_date = equities[i].last_sale_date	# 上次交易日期
                delta_days = data.current_dt - last_sale_date  
                hold_days = delta_days.days # 持仓天数
                if i not in buy_list and hold_days>=context.options['hold_days']:
                    context.order_target(context.symbol(i), 0)
                    sell_stock.append(i)
                    stock_now = stock_now -1

     # 3. 生成买入订单
    buy_num = min(len(buy_list),stock_count)
    if buy_num>0 and len(buy_list)>0:      
        cash_for_buy=context.portfolio.portfolio_value/10
        # 不再买入已经轮仓卖出和移动止损的股票,以防止出现空头持仓
        buy_instruments = [i for i in buy_list if i not in now_stock][:buy_num]
        j = 0
        for i, instrument in enumerate(buy_instruments):  
            cash_for_buy=min(cash_for_buy,context.portfolio.cash)
            #仓位不足5%，不进行买入动作
            if cash_for_buy<context.portfolio.portfolio_value*0.05 :
                break
            current_price = data.current(context.symbol(instrument), 'price')            
            amount = math.floor(cash_for_buy/current_price/100)*100
            context.order(context.symbol(instrument), amount)
            stock_now = stock_now + 1
            j = j + 1
# @param(id="m19", name="prepare")
# 回测引擎：准备数据，只执行一次
def m19_prepare_bigquant_run(context):
    pass


# @module(position="189,-4", comment='', comment_collapsed=True)
m1 = M.instruments.v2(
    start_date='2020-01-01',
    end_date='2022-12-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

# @module(position="77,188", comment='', comment_collapsed=True)
m2 = M.advanced_auto_labeler.v2(
    instruments=m1.data,
    label_expr="""# 0. 每行一个，顺序执行，从第二个开始，可以使用label字段
# 1. 可用数据字段见 https://bigquant.com/docs/develop/datasource/deprecated/history_data.html
#   添加benchmark_前缀，可使用对应的benchmark数据
# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.html>`_

# 计算收益：15日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
shift(close, -15) / shift(open, -1)

# 极值处理：用1%和99%分位的值做clip
clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))

# 将分数映射到分类，这里使用20个分类
all_wbins(label, 30)

# 过滤掉一字涨停的情况 (设置label为NaN，在后续处理和训练中会忽略NaN的label)
# where(shift(high, -1) == shift(low, -1), NaN, label)""",
    start_date='',
    end_date='',
    benchmark='000300.SHA',
    drop_na_label=True,
    cast_label_int=False
)

# @module(position="1011,85", comment='预测数据，用于回测和模拟', comment_collapsed=False)
m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2023-01-01'),
    end_date=T.live_run_param('trading_date', '2023-11-22'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

# @module(position="560,-157", comment='', comment_collapsed=True)
m12 = M.input_features.v1(
    features="""rank_return_0
rank_return_1
group_rank(industry_sw_level2_0,return_1)
where(close_0>open_0,1,0)
rank((ts_max(close_0,30)-close_0)/(close_0-ts_min(close_0,30)+0.0001))
(close_0-mean(close_0,5))/close_0
volume_0/ts_max(volume_0,60)
rank_avg_turn_3
ta_bbands_lowerband_14_0
ta_macd_macd_12_26_9_0
ta_stoch_slowd_5_3_0_3_0_0
isZhangtToday=where(price_limit_status_0==3,1,0)
ts_argmax(isZhangtToday,30)
"""
)

# @module(position="601,-7", comment='', comment_collapsed=True)
m3 = M.input_features.v1(
    features_ds=m12.data,
    features="""#当天是否涨停
isZhangtToday=where((price_limit_status_0==3),1,0)
#近6日是否出现过涨停
my=where(ts_max(isZhangtToday,30)==1,1,0)"""
)

# @module(position="383,188", comment='', comment_collapsed=True)
m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

# @module(position="411,271", comment='', comment_collapsed=True)
m16 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

# @module(position="234,367", comment='', comment_collapsed=True)
m7 = M.join.v3(
    data1=m2.data,
    data2=m16.data,
    on='date,instrument',
    how='inner',
    sort=False
)

# @module(position="238,447", comment='', comment_collapsed=True)
m10 = M.filtet_st_stock.v7(
    input_1=m7.data
)

# @module(position="230,532", comment='', comment_collapsed=True)
m5 = M.filter.v3(
    input_data=m10.data_1,
    expr='my==1',
    output_left_data=False
)

# @module(position="230,634", comment='', comment_collapsed=True)
m13 = M.dropnan.v2(
    input_data=m5.data
)

# @module(position="734,279", comment='', comment_collapsed=True)
m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=220
)

# @module(position="756,374", comment='', comment_collapsed=True)
m18 = M.derived_feature_extractor.v3(
    input_data=m17.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

# @module(position="744,444", comment='', comment_collapsed=True)
m11 = M.filtet_st_stock.v7(
    input_1=m18.data
)

# @module(position="765,532", comment='', comment_collapsed=True)
m4 = M.filter.v3(
    input_data=m11.data_1,
    expr='my==1',
    output_left_data=False
)

# @module(position="747,641", comment='', comment_collapsed=True)
m14 = M.dropnan.v2(
    input_data=m4.data
)

# @module(position="416,817", comment='', comment_collapsed=True)
m6 = M.stock_ranker_train.v5(
    training_ds=m13.data,
    features=m12.data,
    learning_algorithm='排序',
    number_of_leaves=40,
    minimum_docs_per_leaf=500,
    number_of_trees=120,
    learning_rate=0.1,
    max_bins=1023,
    feature_fraction=1,
    m_lazy_run=False
)

# @module(position="577,917", comment='', comment_collapsed=True)
m8 = M.stock_ranker_predict.v5(
    model=m6.model,
    data=m14.data,
    m_lazy_run=False
)

# @module(position="579,1059", comment='', comment_collapsed=True)
m19 = M.trade.v4(
    instruments=m9.data,
    options_data=m8.predictions,
    start_date='',
    end_date='',
    initialize=m19_initialize_bigquant_run,
    handle_data=m19_handle_data_bigquant_run,
    prepare=m19_prepare_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.SHA'
)
# </aistudiograph>
    • 收益率65.96%
    • 年化收益率81.07%
    • 基准收益率-8.45%
    • 阿尔法0.98
    • 贝塔0.78
    • 夏普比率2.73
    • 胜率0.55
    • 盈亏比1.99
    • 收益波动率21.51%
    • 信息比率0.24
    • 最大回撤9.15%
    日期 时间 股票代码 股票名称 买/卖 数量 成交价 总成本 交易佣金
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    日期 股票代码 股票名称 持仓均价 收盘价 股数 持仓价值 收益
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    时间 级别 内容
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