构建策略时候可能会用到指数的相关因子,因子库中并没有计算。我们可以通过指数特征抽取模块计算大盘因子。
指定指数特征抽取模块中的指数代码,向前抽取天数,通过证券代码列表设置起止时间,通过特征因子列表输入因子表达式,注意指数行情数据只有open,close,high,low,amount和volume基础数据字段供因子表达式构建。
例如构建大盘过去5日收益率: close/shift(close,5)-1,模块会修改因子名称为bm_开头的新因子名称,并输出新的因子列表。
案例:
克隆策略
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In [107]:
# 本代码由可视化策略环境自动生成 2019年6月27日 16:33
# 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 m8_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 m8_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 m8_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 m8_before_trading_start_bigquant_run(context, data):
pass
m5 = 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
return_0
avg_turn_9/2"""
)
m6 = M.instruments.v2(
start_date='2015-01-01',
end_date='2018-01-01',
market='CN_STOCK_A',
instrument_list='',
max_count=0
)
m4 = M.general_feature_extractor.v7(
instruments=m6.data,
features=m5.data,
start_date='',
end_date='',
before_start_days=90
)
m11 = M.derived_feature_extractor.v3(
input_data=m4.data,
features=m5.data,
date_col='date',
instrument_col='instrument',
drop_na=True,
remove_extra_columns=True,
user_functions={}
)
m10 = M.advanced_auto_labeler.v2(
instruments=m6.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,
user_functions={}
)
m12 = M.join.v3(
data1=m10.data,
data2=m11.data,
on='date,instrument',
how='inner',
sort=False
)
m15 = 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
close/shift(close,5)-1
amount+1
ta_sma(close,5)
"""
)
m3 = M.index_feature_extract.v2(
input_1=m6.data,
input_2=m15.data,
before_days=100,
index='000300.HIX'
)
m1 = M.join.v3(
data1=m3.data_1,
data2=m12.data,
on='date',
how='inner',
sort=False
)
m7 = M.features_add.v1(
input_1=m3.data_2,
input_2=m5.data
)
m16 = M.instruments.v2(
start_date='2019-01-01',
end_date='2019-06-01',
market='CN_STOCK_A',
instrument_list='',
max_count=0
)
m14 = M.index_feature_extract.v2(
input_1=m16.data,
input_2=m15.data,
before_days=100,
index='000300.HIX'
)
m17 = M.general_feature_extractor.v7(
instruments=m16.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=True,
remove_extra_columns=True,
user_functions={}
)
m2 = M.join.v3(
data1=m18.data,
data2=m14.data_1,
on='date',
how='inner',
sort=False
)
m9 = M.stock_ranker.v2(
training_ds=m1.data,
features=m7.data_1,
predict_ds=m2.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,
slim_data=True
)
m8 = M.trade.v4(
instruments=m16.data,
options_data=m9.predictions,
start_date='',
end_date='',
initialize=m8_initialize_bigquant_run,
handle_data=m8_handle_data_bigquant_run,
prepare=m8_prepare_bigquant_run,
before_trading_start=m8_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='后复权',
product_type='股票',
plot_charts=True,
backtest_only=False,
benchmark=''
)
日志 60 条,错误日志
0 条
2019-06-27 16:31:19.980170 INFO: bigquant: input_features.v1 开始运行.
2019-06-27 16:31:20.279296 INFO: bigquant: 命中缓
2019-06-27 16:31:20.281567 INFO: bigquant: input_features.v1 运行完成[0.3014s]
2019-06-27 16:31:20.285033 INFO: bigquant: instruments.v2 开始运行.
2019-06-27 16:31:20.390050 INFO: bigquant: 命中缓
2019-06-27 16:31:20.392448 INFO: bigquant: instruments.v2 运行完成[0.107398s]
2019-06-27 16:31:20.458586 INFO: bigquant: general_feature_extractor.v7 开始运行.
2019-06-27 16:31:20.555506 INFO: bigquant: 命中缓
2019-06-27 16:31:20.557901 INFO: bigquant: general_feature_extractor.v7 运行完成[0.099326s]
2019-06-27 16:31:20.561928 INFO: bigquant: derived_feature_extractor.v3 开始运行.
2019-06-27 16:31:20.730321 INFO: bigquant: 命中缓
2019-06-27 16:31:20.732696 INFO: bigquant: derived_feature_extractor.v3 运行完成[0.170752s]
2019-06-27 16:31:20.735697 INFO: bigquant: advanced_auto_labeler.v2 开始运行.
2019-06-27 16:31:20.798574 INFO: bigquant: 命中缓
2019-06-27 16:31:20.807942 INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.072238s]
2019-06-27 16:31:20.811297 INFO: bigquant: join.v3 开始运行.
2019-06-27 16:31:20.891029 INFO: bigquant: 命中缓
2019-06-27 16:31:20.896821 INFO: bigquant: join.v3 运行完成[0.085511s]
2019-06-27 16:31:20.900394 INFO: bigquant: input_features.v1 开始运行.
2019-06-27 16:31:20.963844 INFO: bigquant: 命中缓
2019-06-27 16:31:20.966598 INFO: bigquant: input_features.v1 运行完成[0.066193s]
2019-06-27 16:31:20.979865 INFO: bigquant: index_feature_extract.v2 开始运行.
2019-06-27 16:31:21.240578 INFO: bigquant: 命中缓
2019-06-27 16:31:21.242836 INFO: bigquant: index_feature_extract.v2 运行完成[0.262969s]
2019-06-27 16:31:21.246582 INFO: bigquant: join.v3 开始运行.
2019-06-27 16:31:21.394964 INFO: bigquant: 命中缓
2019-06-27 16:31:21.397001 INFO: bigquant: join.v3 运行完成[0.150416s]
2019-06-27 16:31:21.402879 INFO: bigquant: features_add.v1 开始运行.
2019-06-27 16:31:21.536595 INFO: bigquant: 命中缓
2019-06-27 16:31:21.539042 INFO: bigquant: features_add.v1 运行完成[0.13615s]
2019-06-27 16:31:21.542973 INFO: bigquant: instruments.v2 开始运行.
2019-06-27 16:31:21.625088 INFO: bigquant: 命中缓
2019-06-27 16:31:21.627739 INFO: bigquant: instruments.v2 运行完成[0.084748s]
2019-06-27 16:31:21.632878 INFO: bigquant: index_feature_extract.v2 开始运行.
2019-06-27 16:31:22.049900 INFO: bigquant: 命中缓
2019-06-27 16:31:22.051927 INFO: bigquant: index_feature_extract.v2 运行完成[0.41905s]
2019-06-27 16:31:22.154042 INFO: bigquant: general_feature_extractor.v7 开始运行.
2019-06-27 16:31:22.291052 INFO: bigquant: 命中缓
2019-06-27 16:31:22.297695 INFO: bigquant: general_feature_extractor.v7 运行完成[0.143638s]
2019-06-27 16:31:22.300908 INFO: bigquant: derived_feature_extractor.v3 开始运行.
2019-06-27 16:31:22.378766 INFO: bigquant: 命中缓
2019-06-27 16:31:22.381183 INFO: bigquant: derived_feature_extractor.v3 运行完成[0.080265s]
2019-06-27 16:31:22.384514 INFO: bigquant: join.v3 开始运行.
2019-06-27 16:31:22.459934 INFO: bigquant: 命中缓
2019-06-27 16:31:22.465530 INFO: bigquant: join.v3 运行完成[0.080999s]
2019-06-27 16:31:22.472007 INFO: bigquant: stock_ranker.v2 开始运行.
2019-06-27 16:31:22.711792 INFO: bigquant: 命中缓
2019-06-27 16:31:22.981622 INFO: bigquant: stock_ranker.v2 运行完成[0.509601s]
2019-06-27 16:31:23.138535 INFO: bigquant: backtest.v8 开始运行.
2019-06-27 16:31:23.142211 INFO: bigquant: biglearning backtest:V8.2.
2019-06-27 16:31:23.144309 INFO: bigquant: product_type:stock by specifie
2019-06-27 16:31:23.379068 INFO: bigquant: cached.v2 开始运行.
2019-06-27 16:31:23.526450 INFO: bigquant: 命中缓
2019-06-27 16:31:23.528956 INFO: bigquant: cached.v2 运行完成[0.14989s]
2019-06-27 16:31:30.941657 INFO: algo: TradingAlgorithm V1.5.
2019-06-27 16:31:33.787299 INFO: algo: trading transform..
2019-06-27 16:31:37.305038 INFO: Performance: Simulated 99 trading days out of 99
2019-06-27 16:31:37.307593 INFO: Performance: first open: 2019-01-02 09:30:00+00:0
2019-06-27 16:31:37.309895 INFO: Performance: last close: 2019-05-31 15:00:00+00:0
2019-06-27 16:31:46.540824 INFO: bigquant: backtest.v8 运行完成[23.402275s]