#初始化函数
def initialize(context):
# 设定基准
set_benchmark('000905.XSHG')
# 用真实价格交易
set_option('use_real_price', True)
# 打开防未来函数
set_option("avoid_future_data", True)
# 将滑点设置为0
set_slippage(FixedSlippage(0))
# 设置交易成本万分之三，不同滑点影响可在归因分析中查看
set_order_cost(OrderCost(open_tax=0, close_tax=0.001, open_commission=0.0001, close_commission=0.0001, close_today_commission=0, min_commission=5),type='stock')
# 过滤order中低于error级别的日志
log.set_level('order', 'error')
#初始化全局变量
g.no_trading_today_signal = False
g.stock_num = 5
g.hold_list = [] #当前持仓的全部股票
g.yesterday_HL_list = [] #记录持仓中昨日涨停的股票
g.factor_list = [
(#ARBR-SGAI-NPtTORttm-RPps
[
'ARBR', #情绪类因子 ARBR
'SGAI', #质量类因子 销售管理费用指数
'net_profit_to_total_operate_revenue_ttm', #质量类因子 净利润与营业总收入之比
'retained_profit_per_share' #每股指标因子 每股未分配利润
],
[
-2.3425,
-694.7936,
-170.0463,
-1362.5762
]
),
(#P1Y-TPtCR-VOL120
[
'Price1Y', #动量类因子 当前股价除以过去一年股价均值再减1
'total_profit_to_cost_ratio', #质量类因子 成本费用利润率
'VOL120' #情绪类因子 120日平均换手率
],
[
-0.0647128120839873,
-0.006385116279168804,
-0.0029867925845833217
]
),
(#PNF-TPtCR-ITR
[
'price_no_fq', #技术指标因子 不复权价格因子
'total_profit_to_cost_ratio', #质量类因子 成本费用利润率
'inventory_turnover_rate' #质量类因子 存货周转率
],
[
-6.123355346008858e-05,
-0.002579342458393642,
-2.194257357346814e-06
]
),
(#DtA-OCtORR-DAVOL20-PNF-SG
[
'debt_to_assets', #风格因子 资产负债率
'operating_cost_to_operating_revenue_ratio', #质量类因子 销售成本率
'DAVOL20', #情绪类因子 20日平均换手率与120日平均换手率之比
'price_no_fq', #技术指标因子 不复权价格因子
'sales_growth' #风格因子 5年营业收入增长率
],
[
0.04477354820057883,
0.021636407482421707,
-0.01864268317469762,
-0.0004678118383947827,
0.02884867440332058
]
),
(#TVSTD6-CFpsttm-SR120-NONPttm
[
'TVSTD6', #情绪类因子 6日成交金额的标准差
'cashflow_per_share_ttm', #每股指标因子 每股现金流量净额
'sharpe_ratio_120', #风险类因子 120日夏普率
'non_operating_net_profit_ttm' #基础科目及衍生类因子 营业外收支净额TTM
],
[
-5.394060941494863e-12,
4.6306072704138405e-05,
-0.0030567075906980912,
1.4227113275455325e-12
]
)
]
# 设置交易运行时间
run_daily(prepare_stock_list, '9:05')
run_weekly(weekly_adjustment, 1, '9:30')
run_daily(check_limit_up, '14:00') #检查持仓中的涨停股是否需要卖出
run_daily(close_account, '14:30')
run_daily(print_position_info, '15:10')
#1-1 准备股票池
def prepare_stock_list(context):
#获取已持有列表
g.hold_list= []
for position in list(context.portfolio.positions.values()):
stock = position.security
g.hold_list.append(stock)
#获取昨日涨停列表
if g.hold_list != []:
df = get_price(g.hold_list, end_date=context.previous_date, frequency='daily', fields=['close','high_limit'], count=1, panel=False, fill_paused=False)
df = df[df['close'] == df['high_limit']]
g.yesterday_HL_list = list(df.code)
else:
g.yesterday_HL_list = []
#判断今天是否为账户资金再平衡的日期
g.no_trading_today_signal = today_is_between(context, '04-05', '04-30')
#1-2 选股模块
def get_stock_list(context):
#指定日期防止未来数据
yesterday = context.previous_date
today = context.current_dt
#获取初始列表
initial_list = get_all_securities('stock', today).index.tolist()
initial_list = filter_new_stock(context, initial_list)
initial_list = filter_kcbj_stock(initial_list)
initial_list = filter_st_stock(initial_list)
final_list = []
#MS
for factor_list,coef_list in g.factor_list:
factor_values = get_factor_values(initial_list,factor_list, end_date=yesterday, count=1)
df = pd.DataFrame(index=initial_list, columns=factor_values.keys())
for i in range(len(factor_list)):
df[factor_list[i]] = list(factor_values[factor_list[i]].T.iloc[:,0])
df = df.dropna()
df['total_score'] = 0
for i in range(len(factor_list)):
df['total_score'] += coef_list[i]*df[factor_list[i]]
df = df.sort_values(by=['total_score'], ascending=False) #分数越高即预测未来收益越高，排序默认降序
complex_factor_list = list(df.index)[:int(0.1*len(list(df.index)))]
q = query(valuation.code,valuation.circulating_market_cap,indicator.eps).filter(valuation.code.in_(complex_factor_list)).order_by(valuation.circulating_market_cap.asc())
df = get_fundamentals(q)
df = df[df['eps']>0]
lst = list(df.code)
lst = filter_paused_stock(lst)
lst = filter_limitup_stock(context, lst)
lst = filter_limitdown_stock(context, lst)
lst = lst[:min(g.stock_num, len(lst))]
for stock in lst:
if stock not in final_list:
final_list.append(stock)
return final_list
#1-3 整体调整持仓
def weekly_adjustment(context):
if g.no_trading_today_signal == False:
#获取应买入列表
target_list = get_stock_list(context)
#调仓卖出
for stock in g.hold_list:
if (stock not in target_list) and (stock not in g.yesterday_HL_list):
log.info("卖出[%s]" % (stock))
position = context.portfolio.positions[stock]
close_position(position)
else:
log.info("已持有[%s]" % (stock))
#调仓买入
position_count = len(context.portfolio.positions)
target_num = len(target_list)
if target_num > position_count:
value = context.portfolio.cash / (target_num - position_count)
for stock in target_list:
if context.portfolio.positions[stock].total_amount == 0:
if open_position(stock, value):
if len(context.portfolio.positions) == target_num:
break
#1-4 调整昨日涨停股票
def check_limit_up(context):
now_time = context.current_dt
if g.yesterday_HL_list != []:
#对昨日涨停股票观察到尾盘如不涨停则提前卖出，如果涨停即使不在应买入列表仍暂时持有
for stock in g.yesterday_HL_list:
current_data = get_price(stock, end_date=now_time, frequency='1m', fields=['close','high_limit'], skip_paused=False, fq='pre', count=1, panel=False, fill_paused=True)
if current_data.iloc[0,0] < current_data.iloc[0,1]:
log.info("[%s]涨停打开，卖出" % (stock))
position = context.portfolio.positions[stock]
close_position(position)
else:
log.info("[%s]涨停，继续持有" % (stock))
#2-1 过滤停牌股票
def filter_paused_stock(stock_list):
current_data = get_current_data()
return [stock for stock in stock_list if not current_data[stock].paused]
#2-2 过滤ST及其他具有退市标签的股票
def filter_st_stock(stock_list):
current_data = get_current_data()
return [stock for stock in stock_list
if not current_data[stock].is_st
and 'ST' not in current_data[stock].name
and '*' not in current_data[stock].name
and '退' not in current_data[stock].name]
#2-3 过滤科创北交股票
def filter_kcbj_stock(stock_list):
for stock in stock_list[:]:
if stock[0] == '4' or stock[0] == '8' or stock[:2] == '68':
stock_list.remove(stock)
return stock_list
#2-4 过滤涨停的股票
def filter_limitup_stock(context, stock_list):
last_prices = history(1, unit='1m', field='close', security_list=stock_list)
current_data = get_current_data()
return [stock for stock in stock_list if stock in context.portfolio.positions.keys()
or last_prices[stock][-1] < current_data[stock].high_limit]
#2-5 过滤跌停的股票
def filter_limitdown_stock(context, stock_list):
last_prices = history(1, unit='1m', field='close', security_list=stock_list)
current_data = get_current_data()
return [stock for stock in stock_list if stock in context.portfolio.positions.keys()
or last_prices[stock][-1] > current_data[stock].low_limit]
#2-6 过滤次新股
def filter_new_stock(context,stock_list):
yesterday = context.previous_date
return [stock for stock in stock_list if not yesterday - get_security_info(stock).start_date < datetime.timedelta(days=375)]
#3-1 交易模块-自定义下单
def order_target_value_(security, value):
if value == 0:
log.debug("Selling out %s" % (security))
else:
log.debug("Order %s to value %f" % (security, value))
return order_target_value(security, value)
#3-2 交易模块-开仓
def open_position(security, value):
order = order_target_value_(security, value)
if order != None and order.filled > 0:
return True
return False
#3-3 交易模块-平仓
def close_position(position):
security = position.security
order = order_target_value_(security, 0) # 可能会因停牌失败
if order != None:
if order.status == OrderStatus.held and order.filled == order.amount:
return True
return False
#4-1 判断今天是否为账户资金再平衡的日期
def today_is_between(context, start_date, end_date):
today = context.current_dt.strftime('%m-%d')
if (start_date < = today) and (today < = end_date):
return True
else:
return False
#4-2 清仓后次日资金可转
def close_account(context):
if g.no_trading_today_signal == True:
if len(g.hold_list) != 0:
for stock in g.hold_list:
position = context.portfolio.positions[stock]
close_position(position)
log.info("卖出[%s]" % (stock))
2023-08-08
雪球
