当初在策略里加上黑名单功能，是为了在实盘时躲过已经暴雷的股票，回测时去掉完全不影响的，拷贝后做2处修改可以运行：
1.必须注释这一行：from bwlist import *
2.去掉blacklist：
#if hold_stocks[i] not in get_blacklist() and free_cash > context.portfolio.total_value / (g.stocknum * 10): # 黑名单里的股票不买
if free_cash > context.portfolio.total_value / (g.stocknum * 10):
3.如果你需要黑名单功能，就不要按1和2改代码，自己添加一个名为bwlist.py的研究，里面加一个函数，以后随时可以自己增删黑名单而不需要动策略。
def get_blacklist():
return []
——————————————————以下为原帖—————————————————
两年多前看到一创聚宽上线，把手里的策略去掉了不少拟合参数放到了模拟盘，后来就跑实盘，再后来实盘策略又不断改动，现在回头一比，发现还是这个模拟了两年多的策略更稳。
策略有择时，有牛熊市不同仓位配置，有多个可复用的函数，还有原创的打分方法。从14年1月1日到现在，年华74%的收益，最大回撤仅仅22.4%，看看自己现在改来改去跑的实盘，有那么一点哭笑不得。
由于是以前的策略，用的是python2，毕竟在一创一直用python2，运行正常，也就没动力去改了。
# 风险及免责提示：该策略由聚宽用户在聚宽社区分享，仅供学习交流使用。
# 原文一般包含策略说明，如有疑问请到原文和作者交流讨论。
# 原文网址：https://www.joinquant.com/post/30481
# 标题：7年40倍，模拟超过两年，年化高回撤低
# 作者：quakecat
# 请选择 python 2 回测
from __future__ import division
# from bwlist import *
import math
def set_param():
# 交易设置
g.stocknum = 4 # 理想持股数量
g.bearpercent = 0.3 # 熊市仓位
g.bearposition = True # 熊市是否持仓
g.sellrank = 10 # 排名多少位之后(不含)卖出
g.buyrank = 9 # 排名多少位之前(含)可以买入
# 初始筛选
g.tradeday = 300 # 上市天数
g.increase1d = 0.087 # 前一日涨幅
g.tradevaluemin = 0.01 # 最小流通市值 单位（亿）
g.tradevaluemax = 1000 # 最大流通市值 单位（亿）
g.pbmin = 0.01 # 最小市净率
g.pbmax = 30 # 最大市净率
# 排名条件及权重，正数代表从小到大，负数表示从大到小
# 各因子权重：总市值，流通市值，最新价格，5日平均成交量，60日涨幅
g.weights = [5,5,8,4,10]
# 配置择时
g.MA = ['000001.XSHG', 10] # 均线择时
g.choose_time_signal = True # 启用择时信号
g.threshold = 0.003 # 牛熊切换阈值
g.buyagain = 5 # 再次买入的间隔时间
# 获取股票n日以来涨幅，根据当前价计算
# n 默认20日
def get_growth_rate(security, n=20):
lc = get_close_price(security, n)
c = get_close_price(security, 1, '1m')
if not isnan(lc) and not isnan(c) and lc != 0:
return (c - lc) / lc
else:
log.error("数据非法, security: %s, %d日收盘价: %f, 当前价: %f" %(security, n, lc, c))
return 0
def get_growth_rate60(security):
price60d = attribute_history(security, 60, '1d', 'close', False)['close'][0]
pricenow = get_close_price(security, 1, '1m')
if not isnan(pricenow) and not isnan(price60d) and price60d != 0:
return pricenow / price60d
else:
return 100
# 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()
# 已存在于持仓的股票即使涨停也不过滤，避免此股票再次可买，但因被过滤而导致选择别的股票
return [stock for stock in stock_list if stock in context.portfolio.positions.keys()
or last_prices[stock][-1] < current_data[stock].high_limit] # 获取前n个单位时间当时的收盘价 def get_close_price(security, n, unit='1d'): return attribute_history(security, n, unit, 'close')['close'][0] # 平仓，卖出指定持仓 def close_position(security): order = order_target_value(security, 0) # 可能会因停牌或跌停失败 if order != None and order.status == OrderStatus.held: g.sold_stock[security] = 0 # 清空卖出所有持仓 def clear_position(context): if context.portfolio.positions: log.info("==> 清仓，卖出所有股票")
for stock in context.portfolio.positions.keys():
close_position(stock)
# 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]
# 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]
# 过滤创业版股票
def filter_gem_stock(context, stock_list):
return [stock for stock in stock_list if stock[0:3] != '300']
# 过滤次新股
def filter_new_stock(context, stock_list):
return [stock for stock in stock_list if (context.previous_date - datetime.timedelta(days=g.tradeday)) > get_security_info(stock).start_date]
# 过滤昨日涨幅过高的股票
def filter_increase1d(stock_list):
return [stock for stock in stock_list if get_close_price(stock, 1) / get_close_price(stock, 2) < (1 + g.increase1d)] # 过滤卖出不足buyagain日的股票 def filter_buyagain(stock_list): return [stock for stock in stock_list if stock not in g.sold_stock.keys()] def get_stock_list(context): df = get_fundamentals(query(valuation.code).filter(valuation.pb_ratio.between(g.pbmin, g.pbmax) ).order_by(valuation.circulating_market_cap.asc()).limit(1000)).dropna() stock_list = list(df['code']) # 过滤创业板、ST、停牌、当日涨停、次新股、昨日涨幅过高 stock_list = filter_gem_stock(context, stock_list) stock_list = filter_st_stock(stock_list) stock_list = filter_paused_stock(stock_list) stock_list = filter_limitup_stock(context, stock_list) stock_list = filter_new_stock(context, stock_list) stock_list = filter_increase1d(stock_list) stock_list = filter_buyagain(stock_list) return stock_list def get_stock_rank_m_m(stock_list): rank_stock_list = get_fundamentals(query( valuation.code, valuation.market_cap, valuation.circulating_market_cap ).filter(valuation.code.in_(stock_list) ).order_by(valuation.circulating_market_cap.asc()).limit(100)) volume5d = [attribute_history(stock, 1200, '1m', 'volume', df=False)['volume'].sum() for stock in rank_stock_list['code']] increase60d = [get_growth_rate60(stock) for stock in rank_stock_list['code']] current_price = [get_close_price(stock, 1, '1m') for stock in rank_stock_list['code']] min_price = min(current_price) min_increase60d = min(increase60d) min_circulating_market_cap = min(rank_stock_list['circulating_market_cap']) min_market_cap = min(rank_stock_list['market_cap']) min_volume = min(volume5d) totalcount = [[i, math.log(min_volume / volume5d[i]) * g.weights[3] + math.log(min_price / current_price[i]) * g.weights[2] + math.log(min_circulating_market_cap / rank_stock_list['circulating_market_cap'][i]) * g.weights[1] + math.log(min_market_cap / rank_stock_list['market_cap'][i]) * g.weights[0] + math.log(min_increase60d / increase60d[i]) * g.weights[4]] for i in rank_stock_list.index] totalcount.sort(key=lambda x:x[1]) return [rank_stock_list['code'][totalcount[-1-i][0]] for i in range(min(g.sellrank, len(rank_stock_list)))] # 调仓策略：控制在设置的仓位比例附近，如果过多或过少则调整 # 熊市时按设置的总仓位比例控制 def my_adjust_position(context, hold_stocks): if g.choose_time_signal and (not g.isbull): free_value = context.portfolio.total_value * g.bearpercent maxpercent = 1.3 / g.stocknum * g.bearpercent else: free_value = context.portfolio.total_value maxpercent = 1.3 / g.stocknum buycash = free_value / g.stocknum for stock in context.portfolio.positions.keys(): current_data = get_current_data() price1d = get_close_price(stock, 1) nosell_1 = context.portfolio.positions[stock].price >= current_data[stock].high_limit
sell_2 = stock not in hold_stocks
if sell_2 and not nosell_1:
close_position(stock)
else:
current_percent = context.portfolio.positions[stock].value / context.portfolio.total_value
if current_percent > maxpercent:order_target_value(stock, buycash)
def mybuy(context):
if not g.nohold:
# 避免卖出的股票马上买入
hold_stocks = filter_buyagain(g.chosen_stock_list)
log.info("待买股票列表：%s" %(hold_stocks))
if g.choose_time_signal and (not g.isbull):
free_value = context.portfolio.total_value * g.bearpercent
minpercent = 0.7 / g.stocknum * g.bearpercent
else:
free_value = context.portfolio.total_value
minpercent = 0.7 / g.stocknum
buycash = free_value / g.stocknum
for i in range(min(g.buyrank, len(hold_stocks))):
free_cash = free_value - context.portfolio.positions_value
if free_cash > context.portfolio.total_value / (g.stocknum * 10): # 黑名单里的股票不买
if hold_stocks[i] in context.portfolio.positions.keys():
log.info("已经持有股票：[%s]" %(hold_stocks[i]))
current_percent = context.portfolio.positions[hold_stocks[i]].value / context.portfolio.total_value
if current_percent >= minpercent:continue
tobuy = min(free_cash, buycash - context.portfolio.positions[hold_stocks[i]].value)
else:
tobuy = min(buycash, free_cash)
order_value(hold_stocks[i], tobuy)
后续代码
