克隆策略
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In [6]:
# 本代码由可视化策略环境自动生成 2017年10月9日 17:16
# 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。
m1 = M.instruments.v2(
start_date='2010-01-01',
end_date='2015-01-01',
market='CN_STOCK_A',
max_count=0
)
m2 = M.advanced_auto_labeler.v2(
instruments=m1.data,
start_date='2010-01-01',
end_date='2015-01-01',
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)
""",
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_15
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
"""
)
m4 = M.general_feature_extractor.v6(
instruments=m1.data,
features=m3.data,
start_date='2010-01-01',
end_date='2015-01-01'
)
m5 = M.derived_feature_extractor.v2(
input_data=m4.data,
features=m3.data,
date_col='date',
instrument_col='instrument'
)
m6 = M.join.v3(
data1=m2.data,
data2=m5.data,
on='date,instrument',
how='inner',
sort=False
)
m11 = M.dropnan.v1(
input_data=m6.data
)
m13 = M.linear_sgd_train.v2(
training_ds=m11.data,
features=m3.data,
loss='auto',
penalty='l2',
alpha=0.0001,
n_iter=5,
shuffle=True,
eta0=0.1,
algo='classifier'
)
m7 = 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',
max_count=0
)
m8 = M.general_feature_extractor.v6(
instruments=m7.data,
features=m3.data,
start_date=T.live_run_param('trading_date', '2015-01-01'),
end_date=T.live_run_param('trading_date', '2017-01-01')
)
m9 = M.derived_feature_extractor.v2(
input_data=m8.data,
features=m3.data,
date_col='date',
instrument_col='instrument'
)
m12 = M.dropnan.v1(
input_data=m9.data
)
m14 = M.linear_sgd_predict.v2(
model=m13.model,
data=m12.data
)
# 回测引擎:每日数据处理函数,每天执行一次
def m10_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 m10_prepare_bigquant_run(context):
pass
# 回测引擎:初始化函数,只执行一次
def m10_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
m10 = M.trade.v3(
instruments=m7.data,
options_data=m14.predictions,
start_date='2015-01-01',
end_date='2017-01-01',
handle_data=m10_handle_data_bigquant_run,
prepare=m10_prepare_bigquant_run,
initialize=m10_initialize_bigquant_run,
volume_limit=0.025,
order_price_field_buy='open',
order_price_field_sell='close',
capital_base=1000000,
benchmark='000300.SHA',
auto_cancel_non_tradable_orders=True,
data_frequency='daily',
plot_charts=True,
backtest_only=False
)