克隆策略

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# 本代码由可视化策略环境自动生成 2021年5月13日15:55
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# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m6_run_bigquant_run(input_1, input_2, input_3):
    df = input_1.read()

    result = df[['date', 'total_down_7_10', 'total_limit_up', 'total_limit_down']].groupby(['date'], as_index=False).apply(lambda x:x.head(1))

    result.index = result.index.droplevel(0)
    T.plot(result.set_index('date'))
  
    return Outputs()
# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m6_post_run_bigquant_run(outputs):
    return outputs

# 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

# 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):
    # 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)])))

        for instrument in instruments:
            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 m19_prepare_bigquant_run(context):
    pass


m3 = M.input_features.v1(
    features="""# 收益
ret = return_0 - 1

# 是否为ST股
stock_status_CN_STOCK_A__st_status

# 是否为新股
list_days_0

# 涨跌停状态
stock_status_CN_STOCK_A__price_limit_status

# 是否涨停
limit_up = where(stock_status_CN_STOCK_A__price_limit_status == 3, 1, 0)

# 是否跌停
limit_down = where(stock_status_CN_STOCK_A__price_limit_status == 1, 1, 0)

# 是否跌幅 0.07 - 0.1
down_7_10 = where((ret < -0.07) & (ret > -0.1), 1, 0)

# 分行业统计涨停数量
industry_limit_up = group_sum(industry_sw_level1_0, limit_up)

# 分行业统计跌停数量
industry_limit_down = group_sum(industry_sw_level1_0, limit_down)

# 分行业情绪
industry_mood = industry_limit_up - industry_limit_down - group_sum(industry_sw_level1_0, down_7_10)

# 全行业
total_limit_up = group_sum(date, limit_up)
total_limit_down = group_sum(date, limit_down)
total_mood = total_limit_up - total_limit_down - group_sum(date, down_7_10)
total_down_7_10 = group_sum(date, down_7_10)"""
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2016-01-01'),
    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=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m12 = M.filter.v3(
    input_data=m17.data,
    expr='stock_status_CN_STOCK_A__st_status == 0 and list_days_0 > 10',
    output_left_data=False
)

m18 = M.derived_feature_extractor.v3(
    input_data=m12.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m10 = M.dropnan.v2(
    input_data=m18.data
)

m1 = M.instruments.v2(
    start_date='2014-01-01',
    end_date='2015-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=10
)

m11 = M.filter.v3(
    input_data=m15.data,
    expr='stock_status_CN_STOCK_A__st_status == 0 and list_days_0 > 10',
    output_left_data=False
)

m16 = M.derived_feature_extractor.v3(
    input_data=m11.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m6 = M.cached.v3(
    input_1=m16.data,
    run=m6_run_bigquant_run,
    post_run=m6_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

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
)

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

m5 = M.dropnan.v2(
    input_data=m7.data
)

m4 = M.stock_ranker_train.v6(
    training_ds=m5.data,
    features=m3.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,
    m_lazy_run=False
)

m8 = M.stock_ranker_predict.v5(
    model=m4.model,
    data=m10.data,
    m_lazy_run=False
)

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=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.SHA'
)

[2021-05-13 15:53:27.755560] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-05-13 15:53:27.769377] INFO: moduleinvoker: 命中缓存

[2021-05-13 15:53:27.771374] INFO: moduleinvoker: input_features.v1 运行完成[0.015853s].

[2021-05-13 15:53:27.773680] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-05-13 15:53:27.796506] INFO: moduleinvoker: 命中缓存

[2021-05-13 15:53:27.798056] INFO: moduleinvoker: instruments.v2 运行完成[0.024369s].

[2021-05-13 15:53:27.813718] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-05-13 15:53:27.822161] INFO: moduleinvoker: 命中缓存

[2021-05-13 15:53:27.823736] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.010044s].

[2021-05-13 15:53:27.828297] INFO: moduleinvoker: filter.v3 开始运行..

[2021-05-13 15:53:27.849579] INFO: moduleinvoker: 命中缓存

[2021-05-13 15:53:27.851309] INFO: moduleinvoker: filter.v3 运行完成[0.02303s].

[2021-05-13 15:53:27.853995] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-05-13 15:53:27.861607] INFO: moduleinvoker: 命中缓存

[2021-05-13 15:53:27.863096] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.009097s].

[2021-05-13 15:53:27.868853] INFO: moduleinvoker: cached.v3 开始运行..

[2021-05-13 15:53:28.522447] INFO: moduleinvoker: cached.v3 运行完成[0.653409s].

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