策略为动态大小盘轮动策略，核心逻辑如下：
- 市场趋势判断体系：
- 双维度监控：同时跟踪沪深 300 成分股（大盘）和创业板综指（小盘）走势
- 动量对比：计算 20 只头部大盘股与 20 只尾部小盘股 10 日收益率均值
- 趋势跟随：当大盘股收益率均值 > 小盘股时配置大盘，反之配置小盘
- 极端行情切换：任一指数 10 日涨幅超 10% 时强制跟随强势方向
- 财务质量筛选机制：
- 大盘股筛选：
- ROIC_TTM>8% 且负债率 < 50%
- 连续三年净利润增长 > 30%，毛利率 > 30%
- 市值 > 300 亿，PE_TTM<50，未分配利润为正
- 小盘股筛选：
- ROE>15%，ROA>10%
- 营收增长 > 20%，流通市值 < 100 亿
- 每股收益 > 0.3 元，市销率 < 8
- 动态仓位管理：
- 月度调仓：每月首个交易日执行仓位调整
- 分散持仓：等权重配置 3-5 只股票，单只占比 20%-33%
- 强制轮动：清除非趋势方向的持仓标的
- 境外对冲：趋势不明时配置黄金 / 纳斯达克 ETF 对冲风险
- 多层风控体系：
- 个股硬止损：持仓成本回撤 8% 立即平仓
- 涨停板管理：昨日涨停股次日破板即卖出
- 流动性保护：排除 ST / 次新 / 科创 / 北交所标的
- 极端波动规避：自动跳过涨跌停价附近的交易
策略代码
from jqdata import *
from jqfactor import *
import numpy as np
import pandas as pd
import pickle
import talib
import warnings
warnings.filterwarnings("ignore")
# 初始化函数
def initialize(context):
# 设定基准
set_benchmark('000300.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=0.0003, close_commission=0.0003,
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 = 3
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.foreign_ETF = [
'518880.XSHG',
'513030.XSHG',
'513100.XSHG',
'164824.XSHE',
'159866.XSHE',
]
# 设置交易运行时间
run_daily(prepare_stock_list, '9:05')
run_monthly(monthly_adjustment, 1, '9:30')
run_daily(stop_loss, '14:00')
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 = []
def stop_loss(context):
num = 0
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)
num = num+1
else:
log.info("[%s]涨停，继续持有" % (stock))
SS=[]
S=[]
for stock in g.hold_list:
if stock in list(context.portfolio.positions.keys()):
if context.portfolio.positions[stock].price < context.portfolio.positions[stock].avg_cost * 0.92: order_target_value(stock, 0) log.debug("止损 Selling out %s" % (stock)) num = num+1 else: S.append(stock) NOW = (context.portfolio.positions[stock].price - context.portfolio.positions[stock].avg_cost)/context.portfolio.positions[stock].avg_cost SS.append(np.array(NOW)) else: if num >=1:
if len(SS) > 0:
num=3
min_values = sorted(SS)[:num]
min_indices = [SS.index(value) for value in min_values]
min_strings = [S[index] for index in min_indices]
cash = context.portfolio.cash/num
for ss in min_strings:
order_value(ss, cash)
log.debug("补跌最多的N支 Order %s" % (ss))
def filter_roic(context,stock_list):
yesterday = context.previous_date
list=[]
for stock in stock_list:
roic=get_factor_values(stock, 'roic_ttm', end_date=yesterday,count=1)['roic_ttm'].iloc[0,0]
if roic>0.08:
list.append(stock)
return list
def filter_highprice_stock(context,stock_list):
last_prices = history(1, unit='1m', field='close', security_list=stock_list)
return [stock for stock in stock_list if stock in context.portfolio.positions.keys()
or last_prices[stock][-1] < 10]
def filter_highprice_stock2(context,stock_list):
last_prices = history(1, unit='1m', field='close', security_list=stock_list)
return [stock for stock in stock_list if stock in context.portfolio.positions.keys()
or last_prices[stock][-1] < 300] def get_recent_limit_up_stock(context, stock_list, recent_days): stat_date = context.previous_date new_list = [] for stock in stock_list: df = get_price(stock, end_date=stat_date, frequency='daily', fields=['close','high_limit'], count=recent_days, panel=False, fill_paused=False) df = df[df['close'] == df['high_limit']] if len(df) > 0:
new_list.append(stock)
return new_list
def get_recent_down_up_stock(context, stock_list, recent_days):
stat_date = context.previous_date
new_list = []
for stock in stock_list:
df = get_price(stock, end_date=stat_date, frequency='daily', fields=['close','low_limit'], count=recent_days, panel=False, fill_paused=False)
df = df[df['close'] == df['low_limit']]
if len(df) > 0:
new_list.append(stock)
return new_list
def SMALL(context,choice):
df = get_fundamentals(query(
valuation.code,
indicator.roe,
indicator.roa,
).filter(
valuation.code.in_(choice),
indicator.roe > 0.15,
indicator.roa > 0.10,
)).set_index('code').index.tolist()
q = query(
valuation.code
).filter(
valuation.code.in_(df)
).order_by(
valuation.market_cap.asc())
final_list = list(get_fundamentals(q).code)
return final_list
def BIG(context,choice):
BIG_stock_list = get_fundamentals(query(
valuation.code,
).filter(
valuation.code.in_(choice),
valuation.pe_ratio_lyr.between(0,30),#市盈率
valuation.ps_ratio.between(0,8),#市销率TTM
valuation.pcf_ratio<10,#市现率TTM indicator.eps>0.3,#每股收益
indicator.roe>0.1,#净资产收益率
indicator.net_profit_margin>0.1,#销售净利率
indicator.gross_profit_margin>0.3,#销售毛利率
indicator.inc_revenue_year_on_year>0.25,#营业收入同比增长率
).order_by(
valuation.market_cap.desc()).limit(g.stock_num)).set_index('code').index.tolist()
return BIG_stock_list
def ROIC_BIG(context,choice):
df = get_fundamentals(query(
valuation.code,
).filter(
valuation.code.in_(choice),
valuation.market_cap>300,#总市值（亿元）
valuation.pe_ratio.between(0,50),#市盈率TTM
indicator.eps>0.12,#每股收益
indicator.roa>0.15, #总资产收益
(balance.total_liability/balance.total_sheet_owner_equities)<0.5, indicator.inc_total_revenue_year_on_year>0.3,#营业总收入同比增长率
indicator.inc_revenue_year_on_year>0.2,#营业收入同比增长率
balance.retained_profit>0,#未分配利润
)).set_index('code').index.tolist()
df=filter_roic(context,df)
q = query(
valuation.code
).filter(
valuation.code.in_(df)
).order_by(
balance.retained_profit.desc())
final_list = list(get_fundamentals(q).code)[:g.stock_num]
return final_list
def BM(context,choice):
BM_list = get_fundamentals(query(
valuation.code,
).filter(
valuation.code.in_(choice),
valuation.market_cap.between(100,900),#总市值（亿元）
valuation.pb_ratio.between(0,10),#市净率
valuation.pcf_ratio<4,#市现率TTM indicator.eps>0.3,#每股收益
indicator.roe>0.2,#净资产收益率
indicator.net_profit_margin>0.1,#销售净利率
indicator.inc_revenue_year_on_year>0.2,#营业收入同比增长率
indicator.inc_operation_profit_year_on_year>0.1,#营业利润同比增长率
).order_by(
valuation.market_cap.asc()).limit(g.stock_num)).set_index('code').index.tolist()
return BM_list
# 1-3 整体调整持仓
def monthly_adjustment(context):
today = context.current_dt
dt_last = context.previous_date
N=10
B_stocks = get_index_stocks('000300.XSHG', dt_last)
B_stocks = filter_kcbj_stock(B_stocks)
B_stocks = filter_st_stock(B_stocks)
B_stocks = filter_new_stock(context, B_stocks)
S_stocks = get_index_stocks('399101.XSHE', dt_last)
S_stocks = filter_kcbj_stock(S_stocks)
S_stocks = filter_st_stock(S_stocks)
S_stocks = filter_new_stock(context, S_stocks)
q = query(
valuation.code, valuation.circulating_market_cap
).filter(
valuation.code.in_(B_stocks)
).order_by(
valuation.circulating_market_cap.desc()
)
df = get_fundamentals(q, date=dt_last)
Blst = list(df.code)[:20]
q = query(
valuation.code, valuation.circulating_market_cap
).filter(
valuation.code.in_(S_stocks)
).order_by(
valuation.circulating_market_cap.asc()
)
df = get_fundamentals(q, date=dt_last)
Slst = list(df.code)[:20]
#
B_ratio = get_price(Blst, end_date=dt_last, frequency='1d', fields=['close'], count=N, panel=False
).pivot(index='time', columns='code', values='close')
change_BIG = (B_ratio.iloc[-1] / B_ratio.iloc[0] - 1) * 100
A1 = np.array(change_BIG)
A1 = np.nan_to_num(A1)
B_mean = np.mean(A1)
S_ratio = get_price(Slst, end_date=dt_last, frequency='1d', fields=['close'], count=N, panel=False
).pivot(index='time', columns='code', values='close')
change_SMALL = (S_ratio.iloc[-1] / S_ratio.iloc[0] - 1) * 100
A1 = np.array(change_SMALL)
A1 = np.nan_to_num(A1)
S_mean = np.mean(A1)
if B_mean > 10 or S_mean > 10:
print('无敌好行情')
if B_mean > S_mean:
print('开大')
choice = B_stocks
target_list1 = ROIC_BIG(context,choice)
target_list2 = BIG(context,choice)
target_list3 = BM(context,choice)
target_list = target_list3+target_list1+target_list2
target_list = list(set(target_list))
else:
print('开小')
choice = S_stocks
target_list = SMALL(context,choice)[:g.stock_num*3]
elif B_mean>S_mean and B_mean>0:
print('开大')
choice = B_stocks
target_list2 = ROIC_BIG(context,choice)
target_list1 = BIG(context,choice)
target_list3 = BM(context,choice)
target_list = target_list1+target_list2+target_list3
target_list = list(set(target_list))
elif B_mean < S_mean and S_mean > 0:
print('开小')
choice = S_stocks
target_list = SMALL(context,choice)[:g.stock_num*3]
else:
print('开外盘')
target_list = g.foreign_ETF
target_list = filter_limitup_stock(context,target_list)
target_list = filter_limitdown_stock(context,target_list)
target_list = filter_paused_stock(target_list)
for stock in g.hold_list:
if (stock not in target_list) and (stock not in g.yesterday_HL_list):
position = context.portfolio.positions[stock]
close_position(position)
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 stock not in list(context.portfolio.positions.keys()):
if open_position(stock, value):
if len(context.portfolio.positions) == target_num:
break
# 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
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' or stock[0] == '3':
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)]
2025-02-24
