克隆策略

    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    In [8]:
    # 本代码由可视化策略环境自动生成 2021年1月28日16:12
# 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 m12_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.hold_days = 5

# 回测引擎：每日数据处理函数，每天执行一次
def m12_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.hold_days # 是否在建仓期间（前 hold_days 天）
    cash_avg = context.portfolio.portfolio_value / context.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StockRanker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()}
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
        # print('rank order for sell %s' % instruments)
        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StockRanker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 m12_prepare_bigquant_run(context):
    pass

# 回测引擎：每个单位时间开始前调用一次，即每日开盘前调用一次。
def m12_before_trading_start_bigquant_run(context, data):
    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
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
)

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
)

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=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

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
)

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

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

m12 = M.trade.v4(
    instruments=m9.data,
    options_data=m8.predictions,
    start_date='',
    end_date='',
    initialize=m12_initialize_bigquant_run,
    handle_data=m12_handle_data_bigquant_run,
    prepare=m12_prepare_bigquant_run,
    before_trading_start=m12_before_trading_start_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=''
)

m11 = M.input_features.v1(
    features="""score
"""
)

m6 = M.factorlens.v1(
    features=m11.data,
    user_factor_data=m8.predictions,
    title='因子分析: {factor_name}',
    start_date='2015-01-01',
    end_date='2017-01-01',
    rebalance_period=22,
    delay_rebalance_days=0,
    rebalance_price='close_0',
    stock_pool='全市场',
    quantile_count=5,
    commission_rate=0.0016,
    returns_calculation_method='累乘',
    benchmark='无',
    drop_price_limit_stocks=True,
    drop_st_stocks=True,
    drop_new_stocks=True,
    normalization=True,
    neutralization=['行业', '市值'],
    metrics=['因子表现概览', '因子分布', '因子行业分布', '因子市值分布', 'IC分析', '买入信号重合分析', '因子估值分析', '因子拥挤度分析', '因子值最大/最小股票', '表达式因子值', '多因子相关性分析'],
    factor_coverage=0.5
)
    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-dafd81f629d84e4ab41e362b6fdd1438"}/bigcharts-data-end
    • 收益率308.49%
    • 年化收益率106.83%
    • 基准收益率-6.33%
    • 阿尔法0.8
    • 贝塔0.94
    • 夏普比率1.88
    • 胜率0.62
    • 盈亏比0.74
    • 收益波动率41.79%
    • 信息比率0.17
    • 最大回撤47.66%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-36eea4e37998491392fc2b397f4ef65a"}/bigcharts-data-end

    因子分析: score

    { "type": "factor-track", "data": { "exprs": ["score"], "options": {"BacktestInterval": ["2015-01-01", "2017-01-01"], "Benchmark": "none", "StockPool": "all", "DropSTStocks": 1, "DropPriceLimitStocks": 1, "DropNewStocks": 1, "QuantileCount": 5, "CommissionRates": 0.0016, "Normalization": 1, "Neutralization": "industry,size", "RebalancePrice": "close_0", "DelayRebalanceDays": 0, "RebalancePeriod": 22, "ReturnsCalculationMethod": "cumprod", "FactorCoverage": 0.5, "_HASH": "810c32a12fc7daa877688be3f70140d5"} } }

    因子表现概览

      累计收益 近1年收益 近3月收益 近1月收益 近1周收益 昨日收益 最大回撤 盈亏比 胜率 夏普比率 收益波动率
    最小分位 -28.61% -32.54% -7.49% -7.19% -0.62% -0.13% 56.21% 0.72 0.57 -0.32 39.82%
    最大分位 59.97% -11.05% -1.91% -5.96% -0.24% -0.06% 38.57% 0.80 0.59 0.72 40.15%
    多空组合 -33.45% -13.00% -2.85% -0.63% -0.19% -0.03% 33.45% 0.62 0.39 -4.85 5.03%

    基本特征分析

    IC分析

    0.10

    0.05

    2.03

    100.00%

    买入信号重合分析

    因子估值分析

    因子拥挤度分析

    因子值最小的20只股票 (2016-12-30)

    股票名称 股票代码 因子值
    柘中股份 002346.SZA -0.4259
    中际装备 300308.SZA -0.3803
    易事特 300376.SZA -0.3450
    三全食品 002216.SZA -0.3350
    多喜爱 002761.SZA -0.2975
    中国国贸 600007.SHA -0.2580
    闽东电力 000993.SZA -0.2567
    环旭电子 601231.SHA -0.2453
    华塑控股 000509.SZA -0.2438
    太阳电缆 002300.SZA -0.2438
    东方铁塔 002545.SZA -0.2438
    焦作万方 000612.SZA -0.2400
    巨力索具 002342.SZA -0.2372
    福日电子 600203.SHA -0.2313
    苏大维格 300331.SZA -0.2262
    全信股份 300447.SZA -0.2251
    开元仪器 300338.SZA -0.2165
    新开普 300248.SZA -0.2150
    博世科 300422.SZA -0.2133
    中海达 300177.SZA -0.2133

    因子值最大的20只股票 (2016-12-30)

    股票名称 股票代码 因子值
    天润曲轴 002283.SZA 1.1895
    新晨科技 300542.SZA 1.2456
    暴风集团 300431.SZA 1.2461
    微光股份 002801.SZA 1.2975
    华媒控股 000607.SZA 1.3067
    创新股份 002812.SZA 1.3120
    海虹控股 000503.SZA 1.3263
    邦宝益智 603398.SHA 1.3874
    华源包装 002787.SZA 1.4337
    东音股份 002793.SZA 1.4589
    川金诺 300505.SZA 1.5263
    金轮股份 002722.SZA 1.5508
    西藏城投 600773.SHA 1.7015
    明家联合 300242.SZA 1.7180
    世名科技 300522.SZA 1.7258
    科大国创 300520.SZA 1.8230
    熊猫金控 600599.SHA 1.9648
    先进数通 300541.SZA 2.0487
    今天国际 300532.SZA 2.0744
    通合科技 300491.SZA 2.1003