克隆策略

    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order.amount)\n # 设置price_field在[low，high]就能保证只要限价单价格在此范围都能成交，也符合实际情形\n context.fix_slippage = FixedPriceSlippage(price_field_buy='low', price_field_sell='close') # 限定价如果比最低价高，买单就能成交\n context.set_slippage(us_equities=context.fix_slippage) # us是universe的简写","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"handle_data","Value":"# 回测引擎：每日数据处理函数，每天执行一次\ndef bigquant_run(context, data):\n \n if context.trading_day_index >=5: # 这里是调试代码，因此只看前五天买入 情形，实际回测时，去掉这两行\n return \n \n \n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\n # 1. 资金分配\n # 平均持仓时间是hold_days，每日都将买入股票，每日预期使用 1/hold_days 的资金\n # 实际操作中，会存在一定的买入误差，所以在前hold_days天，等量使用资金；之后，尽量使用剩余资金（这里设置最多用等量的1.5倍）\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间（前 hold_days 天）\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.portfolio.positions.items()}\n\n # 2. 生成卖出订单：hold_days天之后才开始卖出；对持仓的股票，按机器学习算法预测的排序末位淘汰\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.portfolio.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities)])))\n\n # 这里示意按照指定价格成交，可以根据需要加入止损的逻辑判断\n for instrument in instruments:\n try:\n myprice = ranker_prediction[ranker_prediction.instrument==instrument]['my_price'].values[0]\n print(data.current_dt,instrument, myprice)\n context.order_target(context.symbol(instrument), 0, limit_price=myprice)\n except:\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\n\n # 3. 生成买入订单：按机器学习算法预测的排序，买入前面的stock_count只股票\n 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    In [6]:
    # 本代码由可视化策略环境自动生成 2021年6月22日18:13
# 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。


# 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
    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 = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
    # 设置每只股票占用的最大资金比例
    context.max_cash_per_instrument = 0.2
    context.options['hold_days'] = 5
    from zipline.finance.slippage import SlippageModel
    class FixedPriceSlippage(SlippageModel):
        def process_order(self, data, order, bar_volume=0, trigger_check_price=0):
            if order.limit is None:
                price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell
                price = data.current(order.asset, price_field)
            else:
                price = order.limit
            # 返回希望成交的价格和数量
            return (price, order.amount)
    # 设置price_field在[low，high]就能保证只要限价单价格在此范围都能成交，也符合实际情形
    context.fix_slippage = FixedPriceSlippage(price_field_buy='low', price_field_sell='close') # 限定价如果比最低价高，买单就能成交
    context.set_slippage(us_equities=context.fix_slippage) # us是universe的简写
# 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):
    
    if context.trading_day_index >=5: # 这里是调试代码，因此只看前五天买入 情形，实际回测时，去掉这两行
        return 
    
    
    # 按日期过滤得到今日的预测数据
    ranker_prediction = context.ranker_prediction[
        context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]

    # 1. 资金分配
    # 平均持仓时间是hold_days，每日都将买入股票，每日预期使用 1/hold_days 的资金
    # 实际操作中，会存在一定的买入误差，所以在前hold_days天，等量使用资金；之后，尽量使用剩余资金（这里设置最多用等量的1.5倍）
    is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间（前 hold_days 天）
    cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
    cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
    cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
    positions = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.portfolio.positions.items()}

    # 2. 生成卖出订单：hold_days天之后才开始卖出；对持仓的股票，按机器学习算法预测的排序末位淘汰
    if not is_staging and cash_for_sell > 0:
        equities = {e.symbol: e for e, p in context.portfolio.positions.items()}
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities)])))

        # 这里示意按照指定价格成交，可以根据需要加入止损的逻辑判断
        for instrument in instruments:
            try:
                myprice = ranker_prediction[ranker_prediction.instrument==instrument]['my_price'].values[0]
                print(data.current_dt,instrument, myprice)
                context.order_target(context.symbol(instrument), 0, limit_price=myprice)
            except:
                context.order_target(context.symbol(instrument), 0)
            cash_for_sell -= positions[instrument]
            if cash_for_sell <= 0:
                break

    # 3. 生成买入订单：按机器学习算法预测的排序，买入前面的stock_count只股票
    buy_cash_weights = context.stock_weights
    buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
    max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
    for i, instrument in enumerate(buy_instruments):
        cash = cash_for_buy * buy_cash_weights[i]
        if cash > max_cash_per_instrument - positions.get(instrument, 0):
            # 确保股票持仓量不会超过每次股票最大的占用资金量
            cash = max_cash_per_instrument - positions.get(instrument, 0)
        if cash > 0:
            myprice = ranker_prediction[ranker_prediction.instrument==instrument]['my_price'].values[0]
            print('======',data.current_dt,instrument, myprice)

            context.order_value(context.symbol(instrument), cash, limit_price=myprice)

# 回测引擎：准备数据，只执行一次
def m19_prepare_bigquant_run(context):
    pass


m1 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2015-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.advanced_auto_labeler.v2(
    instruments=m1.data,
    label_expr="""# #号开始的表示注释
# 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：5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, -5) / 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, 20)

# 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=True
)

m3 = M.input_features.v1(
    features="""# #号开始的表示注释
# 多个特征，每行一个，可以包含基础特征和衍生特征
# #号开始的表示注释
# 多个特征，每行一个，可以包含基础特征和衍生特征
return_5
return_10
return_20
avg_amount_0/avg_amount_5
avg_amount_5/avg_amount_20
rank_avg_amount_0/rank_avg_amount_5
rank_avg_amount_5/rank_avg_amount_10
rank_return_0
rank_return_5
rank_return_10
rank_return_0/rank_return_5
rank_return_5/rank_return_10
pe_ttm_0"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

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
)

m7 = M.join.v3(
    data1=m2.data,
    data2=m16.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m4 = M.dropnan.v2(
    input_data=m7.data
)

m5 = M.input_features.v1(
    features_ds=m3.data,
    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
my_price=shift(open_0,-1)/shift(adjust_factor_0,-1)"""
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2015-01-01'),
    end_date=T.live_run_param('trading_date', '2017-01-01'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m5.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m18 = M.derived_feature_extractor.v3(
    input_data=m17.data,
    features=m5.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m8 = M.dropnan.v2(
    input_data=m18.data
)

m6 = M.stock_ranker.v2(
    training_ds=m4.data,
    features=m3.data,
    predict_ds=m8.data,
    learning_algorithm='排序',
    number_of_leaves=30,
    minimum_docs_per_leaf=1000,
    number_of_trees=20,
    learning_rate=0.1,
    max_bins=1023,
    feature_fraction=1,
    data_row_fraction=1,
    ndcg_discount_base=1,
    slim_data=True
)

m20 = M.use_datasource.v1(
    instruments=m9.data,
    datasource_id='bar1d_CN_STOCK_A',
    start_date='',
    end_date=''
)

m23 = 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
my_price=shift(open,-1) * 0.99"""
)

m22 = M.derived_feature_extractor.v3(
    input_data=m20.data,
    features=m23.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m12 = M.data_join.v3(
    input_1=m6.predictions,
    input_2=m22.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m19 = M.trade.v4(
    instruments=m9.data,
    options_data=m12.data,
    start_date='',
    end_date='',
    initialize=m19_initialize_bigquant_run,
    handle_data=m19_handle_data_bigquant_run,
    prepare=m19_prepare_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='后复权',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.SHA'
)
    设置评估测试数据集，查看训练曲线
    [视频教程]StockRanker训练曲线
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-be751c4fbac54360a80fcfdf70d6f57c"}/bigcharts-data-end
    ====== 2015-01-05 15:00:00+00:00 300391.SZA 30.610800075531007
====== 2015-01-05 15:00:00+00:00 300038.SZA 27.439619464874266
====== 2015-01-05 15:00:00+00:00 300367.SZA 148.7338137817383
====== 2015-01-05 15:00:00+00:00 300302.SZA 37.468866233825686
====== 2015-01-05 15:00:00+00:00 300109.SZA 75.16554153442382
====== 2015-01-06 15:00:00+00:00 300380.SZA 65.14983558654785
====== 2015-01-06 15:00:00+00:00 300238.SZA 92.85747871398925
====== 2015-01-06 15:00:00+00:00 300367.SZA 158.86619247436522
====== 2015-01-06 15:00:00+00:00 300037.SZA 47.546145057678224
====== 2015-01-06 15:00:00+00:00 600446.SHA 266.02074645996095
====== 2015-01-07 15:00:00+00:00 300238.SZA 91.79636627197266
====== 2015-01-07 15:00:00+00:00 300380.SZA 63.140635299682614
====== 2015-01-07 15:00:00+00:00 300109.SZA 77.78646743774414
====== 2015-01-07 15:00:00+00:00 300139.SZA 88.01504997253419
====== 2015-01-07 15:00:00+00:00 300367.SZA 154.62289077758788
====== 2015-01-08 15:00:00+00:00 300139.SZA 90.86738777160645
====== 2015-01-08 15:00:00+00:00 300209.SZA 33.66209976196289
====== 2015-01-08 15:00:00+00:00 002534.SZA 18.985617141723633
====== 2015-01-08 15:00:00+00:00 000524.SZA 55.93267742156982
====== 2015-01-08 15:00:00+00:00 603019.SHA 39.8475
====== 2015-01-09 15:00:00+00:00 000981.SZA 20.162826404571533
====== 2015-01-09 15:00:00+00:00 002534.SZA 18.447989330291747
====== 2015-01-09 15:00:00+00:00 300139.SZA 91.60084671020508
====== 2015-01-09 15:00:00+00:00 000707.SZA 15.451341047286988
====== 2015-01-09 15:00:00+00:00 603518.SHA 30.165299320220946
    • 收益率33.0%
    • 年化收益率15.86%
    • 基准收益率-6.33%
    • 阿尔法0.2
    • 贝塔0.71
    • 夏普比率0.52
    • 胜率1.0
    • 盈亏比0.0
    • 收益波动率33.7%
    • 信息比率0.04
    • 最大回撤45.82%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-3410c3f5c11e4971ac934d2796778f0a"}/bigcharts-data-end
    In [10]:
    df = DataSource('bar1d_CN_STOCK_A').read(instruments=['300391.SZA'], start_date='2015-01-05', end_date='2015-01-06')
df[['date','open','low']]
    Out[10]:
    date open low
    0 2015-01-05 32.299999 30.379999
    1 2015-01-06 30.920000 30.410000
    In [8]:
    # 委托价格 （次日开盘价*0.99）
30.920000*0.99
    Out[8]:
    30.6108
    In [9]:
    ## 可以看到该价格与2015-01-06 里面的康悦科技的成交价30.61是一致的（成交价只显示到小数点后两位）。因为指定价大于当天的最低价 30.41 ,交易方向是买入，所以买入是成交的。