平台很多新来的朋友对如何控制交易感觉不太容易上手，改版AI可视化模板后可以轻松实现交易控制。
标准AI模板的trade模块：
我们在做量化时经常需要的功能如下表所示：
关于仓位的控制如下表所示：
因子条件过滤/周期触发/止盈止损/大盘风控相关代码则可以参考下面的例子：
克隆策略
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回测引擎：每日数据处理函数，每天执行一次\ndef bigquant_run(context, data):\n \n #大盘风控模块，以上证指数5日涨幅为例，如果大盘下跌较多，全部卖出并结束当日操作\n today_date = data.current_dt.strftime('%Y-%m-%d')\n benckmarch_prices = data.history(context.symbols('000001.SHA'), ['close'], 5, '1d')['close']\n benckmarch_control = benckmarch_prices[context.symbol('000001.SHA')][-1] / benckmarch_prices[context.symbol('000001.SHA')][0]\n if benckmarch_control < 0.998:\n position_all = context.portfolio.positions.keys()\n for i in position_all:\n context.order_target(i, 0)\n print('日期',today_date,'大盘风控止损触发')\n return\n \n #周期控制模块\n if context.trading_day_index%3!=0:#以3天换一次仓为例\n return\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 #---------------------------START:止赢止损模块(含建仓期)--------------------\n today_date = data.current_dt.strftime('%Y-%m-%d')\n positions_stop={e.symbol:p.cost_basis \n for e,p in context.portfolio.positions.items()}\n # 新建当日止赢止损股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断\n current_stopwin_stock=[]\n current_stoploss_stock = [] \n if len(positions_stop)>0:\n for i in positions_stop.keys():\n stock_cost=positions_stop[i] \n stock_market_price=data.current(context.symbol(i),'price') \n # 赚3元且可以交易and not context.has_unfinished_sell_order(equities[i])\n if stock_market_price-stock_cost>=0.5 and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(i):\n context.order_target_percent(context.symbol(i),0) \n current_stopwin_stock.append(i)\n #print('日期：',today_date,'股票：',i,'出现止盈状况')\n print(today_date,'止盈股票列表',current_stopwin_stock)\n # 亏1元就止损and not context.has_unfinished_sell_order(equities[i])\n if stock_market_price - stock_cost <= -1 and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(i): \n context.order_target_percent(context.symbol(i),0) \n current_stoploss_stock.append(i)\n #print('日期：',today_date,'股票：',i,'出现止损状况')\n print(today_date,'止损股票列表',current_stoploss_stock)\n #--------------------------END: 止赢止损模块-----------------------------\n \n #--------------------------START：持有固定天数卖出(不含建仓期)---------------\n current_stopdays_stock = [] \n today = data.current_dt\n today_date = data.current_dt.strftime('%Y-%m-%d')\n # 不是建仓期（在前hold_days属于建仓期）\n if not is_staging:\n equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}\n if len(equities)>0:\n for i in equities:\n sid = equities[i].sid # 交易标的\n # 今天和上次交易的时间相隔hold_days就全部卖出 datetime.timedelta(context.options['hold_days'])也可以换成自己需要的天数，比如datetime.timedelta(5)\n if today-equities[i].last_sale_date>=datetime.timedelta(context.options['hold_days']) and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(equities[i]):\n context.order_target_percent(sid, 0)\n current_stopdays_stock.append(i)\n #print('日期：',today_date,'持有固定天数卖出股票',str(i))\n print(today_date,'固定天数卖出列表',current_stopdays_stock)\n #-------------------------------END:持有固定天数卖出--------------------------\n \n \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 instruments = 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 # print('rank order for sell %s' % instruments)\n for instrument in instruments:\n if instrument in current_stopwin_stock:\n continue\n if instrument in current_stoploss_stock:\n continue\n if instrument in current_stopdays_stock:\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 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In [18]:
# 本代码由可视化策略环境自动生成 2018年5月10日 21:11
# 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',
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, -1) / 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="""rank_avg_amount_5
rank_avg_turn_5
rank_volatility_5_0"""
)
m14 = M.input_features.v1(
features_ds=m3.data,
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
st_status_0
list_board_0
"""
)
m4 = M.general_feature_extractor.v6(
instruments=m1.data,
features=m14.data,
start_date='',
end_date='',
before_start_days=0
)
m5 = M.derived_feature_extractor.v2(
input_data=m4.data,
features=m14.data,
date_col='date',
instrument_col='instrument'
)
m15 = M.filter.v3(
input_data=m5.data,
expr='st_status_0==0',
output_left_data=False
)
m7 = M.join.v3(
data1=m2.data,
data2=m15.data,
on='date,instrument',
how='inner',
sort=False
)
m12 = M.dropnan.v1(
input_data=m7.data
)
m6 = M.stock_ranker_train.v5(
training_ds=m12.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,
m_lazy_run=False
)
m9 = M.instruments.v2(
start_date=T.live_run_param('trading_date', '2017-01-01'),
end_date=T.live_run_param('trading_date', '2017-3-31'),
market='CN_STOCK_A',
instrument_list='',
max_count=0
)
m10 = M.general_feature_extractor.v6(
instruments=m9.data,
features=m14.data,
start_date='',
end_date='',
before_start_days=0
)
m11 = M.derived_feature_extractor.v2(
input_data=m10.data,
features=m14.data,
date_col='date',
instrument_col='instrument'
)
m16 = M.filter.v3(
input_data=m11.data,
expr='st_status_0==0',
output_left_data=False
)
m13 = M.dropnan.v1(
input_data=m16.data
)
m8 = M.stock_ranker_predict.v5(
model=m6.model,
data=m13.data,
m_lazy_run=False
)
# 回测引擎：每日数据处理函数，每天执行一次
def m17_handle_data_bigquant_run(context, data):
#大盘风控模块，以上证指数5日涨幅为例，如果大盘下跌较多，全部卖出并结束当日操作
today_date = data.current_dt.strftime('%Y-%m-%d')
benckmarch_prices = data.history(context.symbols('000001.SHA'), ['close'], 5, '1d')['close']
benckmarch_control = benckmarch_prices[context.symbol('000001.SHA')][-1] / benckmarch_prices[context.symbol('000001.SHA')][0]
if benckmarch_control < 0.998:
position_all = context.portfolio.positions.keys()
for i in position_all:
context.order_target(i, 0)
print('日期',today_date,'大盘风控止损触发')
return
#周期控制模块
if context.trading_day_index%3!=0:#以3天换一次仓为例
return
# 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()}
#---------------------------START:止赢止损模块(含建仓期)--------------------
today_date = data.current_dt.strftime('%Y-%m-%d')
positions_stop={e.symbol:p.cost_basis
for e,p in context.portfolio.positions.items()}
# 新建当日止赢止损股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断
current_stopwin_stock=[]
current_stoploss_stock = []
if len(positions_stop)>0:
for i in positions_stop.keys():
stock_cost=positions_stop[i]
stock_market_price=data.current(context.symbol(i),'price')
# 赚3元且可以交易and not context.has_unfinished_sell_order(equities[i])
if stock_market_price-stock_cost>=0.5 and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(i):
context.order_target_percent(context.symbol(i),0)
current_stopwin_stock.append(i)
#print('日期：',today_date,'股票：',i,'出现止盈状况')
print(today_date,'止盈股票列表',current_stopwin_stock)
# 亏1元就止损and not context.has_unfinished_sell_order(equities[i])
if stock_market_price - stock_cost <= -1 and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(i):
context.order_target_percent(context.symbol(i),0)
current_stoploss_stock.append(i)
#print('日期：',today_date,'股票：',i,'出现止损状况')
print(today_date,'止损股票列表',current_stoploss_stock)
#--------------------------END: 止赢止损模块-----------------------------
#--------------------------START：持有固定天数卖出(不含建仓期)---------------
current_stopdays_stock = []
today = data.current_dt
today_date = data.current_dt.strftime('%Y-%m-%d')
# 不是建仓期（在前hold_days属于建仓期）
if not is_staging:
equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
if len(equities)>0:
for i in equities:
sid = equities[i].sid # 交易标的
# 今天和上次交易的时间相隔hold_days就全部卖出 datetime.timedelta(context.options['hold_days'])也可以换成自己需要的天数，比如datetime.timedelta(5)
if today-equities[i].last_sale_date>=datetime.timedelta(context.options['hold_days']) and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(equities[i]):
context.order_target_percent(sid, 0)
current_stopdays_stock.append(i)
#print('日期：',today_date,'持有固定天数卖出股票',str(i))
print(today_date,'固定天数卖出列表',current_stopdays_stock)
#-------------------------------END:持有固定天数卖出--------------------------
# 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()}
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:
if instrument in current_stopwin_stock:
continue
if instrument in current_stoploss_stock:
continue
if instrument in current_stopdays_stock:
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 m17_prepare_bigquant_run(context):
context.instruments = context.instruments + ['000300.SHA','000905.SHA','000001.SHA']
# 回测引擎：初始化函数，只执行一次
def m17_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 = 2
# 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 = 1
context.options['hold_days'] = 1
# 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 m17_before_trading_start_bigquant_run(context, data):
pass
m17 = M.trade.v3(
instruments=m9.data,
options_data=m8.predictions,
start_date='',
end_date='',
handle_data=m17_handle_data_bigquant_run,
prepare=m17_prepare_bigquant_run,
initialize=m17_initialize_bigquant_run,
before_trading_start=m17_before_trading_start_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',
price_type='后复权',
plot_charts=True,
backtest_only=False,
amount_integer=False
)
为什么系统不认‘000001.SHA’呢？