复制链接

克隆策略

In [7]:

# 本代码由可视化策略环境自动生成 2022年7月28日 20:02
# 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 m4_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3只
    stock_count = 3
    # 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 = data.current(order.asset, self._price_field_buy)
            # 返回希望成交的价格和数量
            return (price, order.amount)
    # 设置price_field,默认是开盘买入，收盘卖出
    context.fix_slippage = FixedPriceSlippage(price_field_buy='open', price_field_sell='close')
    context.set_slippage(us_equities=context.fix_slippage)
# 回测引擎：每日数据处理函数，每天执行一次
def m4_handle_data_bigquant_run(context, data):
    # 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')]
    today = 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()}
    equities = {e.symbol: e for e, p in context.portfolio.positions.items()}
    
    if len(equities) == 0:
        print("equities are empty :",today)
    else:
        # 记录持仓中st的股票
        st_stock_list = []
        name_df = context.name_df
        name_today = name_df[name_df.date==today]
        for instrument in equities:
            name_instrument = name_today[name_today.instrument==instrument]['name'].values[0]
            # 如果股票状态变为了st 则卖出
            if 'ST' in name_instrument or '退' in name_instrument:
                # 指定一个limit_price,此时会以开盘价成交，这是由于初始化函数中改写了下单价格
                context.order_target(context.symbol(instrument), 0, limit_price=1.0)
                st_stock_list.append(instrument)
                cash_for_sell -= positions[instrument]
        if st_stock_list!=[]:
            print(today,'持仓出现st股/退市股',st_stock_list,'进行卖出处理')
 
    # 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:
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
            lambda x: x in equities)])))
        price_limit_status = context.price_limit_status
        status_today = price_limit_status[price_limit_status.date==today]
        for instrument in instruments:
            # 如果是st股票已经卖过了，就跳过
            if instrument in st_stock_list:
                continue
            # 如果涨停就跳过股票
            status_instrument = status_today[status_today.instrument==instrument]['price_limit_status'].values[0]
            if status_instrument>2:
                continue
            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:
            context.order_value(context.symbol(instrument), cash)


# 回测引擎：准备数据，只执行一次
def m4_prepare_bigquant_run(context):
    # 获取股票名称 用于过滤st和退市股
    context.name_df = DataSource('instruments_CN_STOCK_A').read()
    # 获取涨跌停状态
    context.price_limit_status = DataSource('stock_status_CN_STOCK_A').read(fields=['price_limit_status'])

def m4_before_trading_start_bigquant_run(context, data):
    df_price_limit_status=context.price_limit_status.set_index('date')
    today=data.current_dt.strftime('%Y-%m-%d')
    # 得到当前未完成订单
    for orders in get_open_orders().values():
        # 循环，撤销订单
        for _order in orders:
            ins=str(_order.sid.symbol)
            if data.can_trade(_order.sid):
                #判断一下如果当日涨停，则取消卖单
                if  df_price_limit_status[df_price_limit_status.instrument==ins].price_limit_status.loc[today]>2 and _order.amount<0:
                    cancel_order(_order)
                    print(today,'尾盘涨停取消卖单',ins)                     

m1 = M.instruments.v2(
    start_date='2018-01-01',
    end_date='2021-01-01',
    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/data_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/big_expr.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
# 公司成长水平&盈利水平
fs_roe_ttm_1
"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=0
)

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
)

m13 = M.chinaa_stock_filter.v1(
    input_data=m7.data,
    index_constituent_cond=['全部'],
    board_cond=['上证主板', '深证主板'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m10 = M.dropnan.v2(
    input_data=m13.data
)

m5 = M.stock_ranker_train.v6(
    training_ds=m10.data,
    features=m3.data,
    test_ds=m10.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,
    plot_charts=True,
    ndcg_discount_base=1,
    m_lazy_run=False
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2021-01-02'),
    end_date=T.live_run_param('trading_date', '2022-07-22'),
    market='CN_STOCK_A',
    instrument_list="""600001
600004
600005
600006
600007
600008
600009
600010
600011
600012""",
    max_count=0
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=0
)

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
)

m6 = M.chinaa_stock_filter.v1(
    input_data=m18.data,
    index_constituent_cond=['全部'],
    board_cond=['上证主板', '深证主板'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m11 = M.dropnan.v2(
    input_data=m6.data
)

m8 = M.stock_ranker_predict.v5(
    model=m5.model,
    data=m11.data,
    m_lazy_run=False
)

m4 = M.trade.v4(
    instruments=m9.data,
    options_data=m8.predictions,
    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=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=50000,
    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=''
)

[2022-07-28 20:00:36.427772] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-07-28 20:00:36.439579] INFO: moduleinvoker: 命中缓存

[2022-07-28 20:00:36.442610] INFO: moduleinvoker: instruments.v2 运行完成[0.014837s].

[2022-07-28 20:00:36.472893] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-07-28 20:00:36.486741] INFO: moduleinvoker: 命中缓存

[2022-07-28 20:00:36.489241] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.016351s].

[2022-07-28 20:00:36.499418] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-07-28 20:00:36.537129] INFO: moduleinvoker: input_features.v1 运行完成[0.037716s].

[2022-07-28 20:00:36.698766] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-07-28 20:00:36.841788] WARNING: bigdatasource: cannot find filed [fs_roe_ttm_1] table in field_table_map!

[2022-07-28 20:00:36.846383] INFO: 基础特征抽取: 年份 2018, 特征行数=0

[2022-07-28 20:00:36.854345] WARNING: bigdatasource: cannot find filed [fs_roe_ttm_1] table in field_table_map!

[2022-07-28 20:00:36.857538] INFO: 基础特征抽取: 年份 2019, 特征行数=0

[2022-07-28 20:00:36.879637] WARNING: bigdatasource: cannot find filed [fs_roe_ttm_1] table in field_table_map!

[2022-07-28 20:00:36.889376] INFO: 基础特征抽取: 年份 2020, 特征行数=0

[2022-07-28 20:00:36.911561] WARNING: bigdatasource: cannot find filed [fs_roe_ttm_1] table in field_table_map!

[2022-07-28 20:00:36.915640] INFO: 基础特征抽取: 年份 2021, 特征行数=0

[2022-07-28 20:00:36.964263] ERROR: moduleinvoker: module name: general_feature_extractor, module version: v7, trackeback: Exception: no features extracted.

---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
<ipython-input-7-13cc927300a7> in <module>
    164 )
    165 
--> 166 m15 = M.general_feature_extractor.v7(
    167     instruments=m1.data,
    168     features=m3.data,

Exception: no features extracted.

In [ ]:

m8.start_date

In [ ]:

m8.end_date

In [ ]:

m8.predictions.read_df()