克隆策略

    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label)\n","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000300.SHA","type":"Literal","bound_global_parameter":null},{"name":"drop_na_label","value":"True","type":"Literal","bound_global_parameter":null},{"name":"cast_label_int","value":"True","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"return_5\nreturn_10\nreturn_20\navg_amount_5/avg_amount_10\nreturn_5/return_10\nta_cci_14_0\nturn_5/avg_turn_10\navg_amount_0/avg_amount_5\nswing_volatility_5_0/swing_volatility_60_0\namount_10/amount_30\nrank_volatility_10_0\nreturn_0\nta_ema_5_0/close_0\nclose_0/low_0\nta_ema_20_0/close_0\nopen_0/close_1\nhigh_1/close_0\nta_ema_30_0/close_0\nta_rsi(close_0, 6)\namount_0/amount_1\n# 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回测引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.ranker_prediction = context.options['data'].read_df()\n\n # 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\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n # 预测数据，通过options传入进来，使用 read_df 函数，加载到内存 (DataFrame)\n # 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\n stock_count = 4\n # 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, ..]\n context.stock_weights = [1 / stock_count for i in range(0, stock_count)]\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 1\n context.options['hold_days'] = 1\n","type":"Literal","bound_global_parameter":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 # 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.perf_tracker.position_tracker.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.perf_tracker.position_tracker.positions.items()}\n tmp = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n instruments = equities\n# # print('rank order for sell %s' % instruments)\n for instrument in instruments:\n# if instrument in tmp:\n# print(\"涨停,不卖出\",instrument)\n# continue\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 buy_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n \n context.order_value(context.symbol(instrument), cash)\n","type":"Literal","bound_global_parameter":null},{"name":"prepare","value":"# 回测引擎：准备数据，只执行一次\ndef bigquant_run(context):\n 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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\ndef bigquant_run(input_1, input_2, input_3):\n # 示例代码如下。在这里编写您的代码\n df = input_1.read_df()\n ins = m1.data.read_pickle()['instruments']\n start = m1.data.read_pickle()['start_date']\n end = m1.data.read_pickle()['end_date']\n \n df1 = D.features(ins,start,end,fields=['rank_turn_0','rank_amount_0','st_status_0'])\n df_final = pd.merge(df,df1,on=['date','instrument'])\n df_final = df_final[df_final['instrument'].str.startswith(\"688\") == False]\n df_final = df_final[df_final['instrument'].str.startswith(\"3\") == False]\n\n df_final = df_final[df_final[\"st_status_0\"] == 0]\n df_final = df_final[df_final['rank_turn_0'] >= 0.9]\n df_final = df_final[df_final['rank_amount_0'] >= 0.85]\n print(\"用于训练的样本总个数为\",len(df_final))\n print(df_final.iloc[0])\n data_1 = DataSource.write_df(df_final)\n return Outputs(data_1=data_1, data_2=None, data_3=None)\n","type":"Literal","bound_global_parameter":null},{"name":"post_run","value":"# 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    In [104]:
    # 本代码由可视化策略环境自动生成 2021年9月17日 18:20
# 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。


# 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):
    # 示例代码如下。在这里编写您的代码
    df = input_1.read_df()
    ins = m1.data.read_pickle()['instruments']
    start = m1.data.read_pickle()['start_date']
    end = m1.data.read_pickle()['end_date']
    
    df1 = D.features(ins,start,end,fields=['rank_turn_0','rank_amount_0','st_status_0'])
    df_final = pd.merge(df,df1,on=['date','instrument'])
    df_final = df_final[df_final['instrument'].str.startswith("688") == False]
    df_final = df_final[df_final['instrument'].str.startswith("3") == False]

    df_final = df_final[df_final["st_status_0"] == 0]
    df_final = df_final[df_final['rank_turn_0'] >= 0.9]
    df_final = df_final[df_final['rank_amount_0'] >= 0.85]
    print("用于训练的样本总个数为",len(df_final))
    print(df_final.iloc[0])
    data_1 = DataSource.write_df(df_final)
    return Outputs(data_1=data_1, data_2=None, data_3=None)

# 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

# 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 m8_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    df = input_1.read_df()
    ins = m9.data.read_pickle()['instruments']
    start = m9.data.read_pickle()['start_date']
    end = m9.data.read_pickle()['end_date']
    
    df1 = D.features(ins,start,end,fields=['rank_turn_0','rank_amount_0','st_status_0'])
    df_final = pd.merge(df,df1,on=['date','instrument'])
    df_final = df_final[df_final['instrument'].str.startswith("688") == False]
    df_final = df_final[df_final['instrument'].str.startswith("3") == False]

    df_final = df_final[df_final["st_status_0"] == 0]
    df_final = df_final[df_final['rank_turn_0'] >= 0.9]
    df_final = df_final[df_final['rank_amount_0'] >= 0.85]
    print("用于回测的样本总个数为",len(df_final))
    data_1 = DataSource.write_df(df_final)
    return Outputs(data_1=data_1, data_2=None, data_3=None)

# 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 m8_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 = 4
    # 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 / stock_count 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 = 1
    context.options['hold_days'] = 1

# 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.perf_tracker.position_tracker.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.perf_tracker.position_tracker.positions.items()}
        tmp = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
        instruments = equities
#         # print('rank order for sell %s' % instruments)
        for instrument in instruments:
#             if instrument in tmp:
#                 print("涨停,不卖出",instrument)
#                 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 m19_prepare_bigquant_run(context):
    pass


m1 = M.instruments.v2(
    start_date='2015-01-01',
    end_date='2019-1-01',
    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, -2) / 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="""return_5
return_10
return_20
avg_amount_5/avg_amount_10
return_5/return_10
ta_cci_14_0
turn_5/avg_turn_10
avg_amount_0/avg_amount_5
swing_volatility_5_0/swing_volatility_60_0
amount_10/amount_30
rank_volatility_10_0
return_0
ta_ema_5_0/close_0
close_0/low_0
ta_ema_20_0/close_0
open_0/close_1
high_1/close_0
ta_ema_30_0/close_0
ta_rsi(close_0, 6)
amount_0/amount_1
# return_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
)

m6 = M.cached.v3(
    input_1=m7.data,
    run=m6_run_bigquant_run,
    post_run=m6_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m4 = M.dropnan.v2(
    input_data=m6.data_1
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2019-01-01'),
    end_date=T.live_run_param('trading_date', '2021-08-20'),
    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=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
)

m8 = M.cached.v3(
    input_1=m18.data,
    run=m8_run_bigquant_run,
    post_run=m8_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m5 = M.dropnan.v2(
    input_data=m8.data_1
)

m10 = M.lightgbm.v2(
    training_ds=m4.data,
    features=m3.data,
    predict_ds=m5.data,
    num_boost_round=79,
    objective='排序(ndcg)',
    num_class=1,
    num_leaves=60,
    learning_rate=0.1,
    min_data_in_leaf=900,
    max_bin=1023,
    key_cols='date,instrument',
    group_col='date',
    random_seed=101,
    other_train_parameters={'n_jobs':4,'label_gain':','.join([str(x) for x in range(20)]),"max_position":29,"eval_at":"1,3,5,10"}
)

m19 = M.trade.v4(
    instruments=m9.data,
    options_data=m10.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=100000,
    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'
)
    • 收益率1051.26%
    • 年化收益率160.94%
    • 基准收益率58.41%
    • 阿尔法1.43
    • 贝塔0.65
    • 夏普比率2.64
    • 胜率0.53
    • 盈亏比1.26
    • 收益波动率38.01%
    • 信息比率0.14
    • 最大回撤25.04%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-76518b4e9b4240d0b22fc158f6301ab1"}/bigcharts-data-end
    In [105]:
    m10.feature_gains.read().sort_values('gain',ascending=False)
    Out[105]:
    feature gain
    8 swing_volatility_5_0/swing_volatility_60_0 304
    10 rank_volatility_10_0 291
    5 ta_cci_14_0 288
    0 return_5 287
    19 amount_0/amount_1 286
    3 avg_amount_5/avg_amount_10 284
    13 close_0/low_0 271
    15 open_0/close_1 260
    2 return_20 249
    7 avg_amount_0/avg_amount_5 244
    6 turn_5/avg_turn_10 233
    11 return_0 231
    18 ta_rsi(close_0, 6) 230
    4 return_5/return_10 212
    9 amount_10/amount_30 207
    17 ta_ema_30_0/close_0 203
    16 high_1/close_0 200
    12 ta_ema_5_0/close_0 150
    1 return_10 131
    14 ta_ema_20_0/close_0 100
    In [ ]: