策略回测
策略源码
# 标题：敢于直接实盘的—八仙过海V2
from jqdata import *
import pandas as pd
import talib
def initialize(context):
set_params()
#
set_option("avoid_future_data",True)
set_option('use_real_price', True) # 用真实价格交易
set_benchmark('000300.XSHG')
log.set_level('order', 'error')
#
# 将滑点设置为0
set_slippage(FixedSlippage(0.00))
# 手续费: 采用系统默认设置
set_order_cost(OrderCost(close_tax=0.00, open_commission=0.0001, close_commission=0.0001, min_commission=5),
type='stock')
# 开盘前运行L
run_daily(before_market_open, time='before_open', reference_security='000300.XSHG')
#run_daily(get_signal, time='11:00')
run_daily(get_signal, time='14:30')
# 1 设置参数
def set_params():
g.use_dynamic_target_market = True # 是否动态改变大盘热度参考指标
# g.target_market = '000300.XSHG'
g.target_market = '000300.XSHG'
g.empty_keep_stock = '511880.XSHG' # 闲时买入的标的
# g.empty_keep_stock = '601318.XSHG'#闲时买入的标的
g.signal = 'BUY' # 交易信号初始化
g.lag = 60 #获取前多少天的数据
#
g.buy = [] # 购买股票列表
g.ETFList = []
g.cang = {}
g.allCang = 0
def get_before_after_trade_days(date, count, is_before=True):
"""
来自： https://www.joinquant.com/view/community/detail/c9827c6126003147912f1b47967052d9?type=1
date :查询日期
count : 前后追朔的数量
is_before : True , 前count个交易日 ; False ,后count个交易日
返回 : 基于date的日期, 向前或者向后count个交易日的日期 ,一个datetime.date 对象
"""
all_date = pd.Series(get_all_trade_days())
if isinstance(date, str):
date = datetime.datetime.strptime(date, '%Y-%m-%d').date()
if isinstance(date, datetime.datetime):
date = date.date()
if is_before:
return all_date[all_date <= date].tail(count).values[0] else: return all_date[all_date >= date].head(count).values[-1]
def before_market_open(context):
# 确保交易标的已经上市g.lag1个交易日以上
ETF_targets = [
#'159902.XSHE',#中小板指
#'512880.XSHG',#券商B
#'159901.XSHE',#深100etf
#'510050.XSHG',#上证50
# '510180.XSHG',#上证180
#'510880.XSHG',#红利ETF
#'159905.XSHE',#深红利
# '159915.XSHE',#创业板
# '510300.XSHG',#沪深300
#'510500.XSHG',#中证500
#'159949.XSHE',#创业板50
# '515700.XSHG',# 新能车etf
# '512660.XSHG',# 军工etf
#'512010.XSHG',# 医药etf
# '512290.XSHG',# 生物医药etf
#'518800.XSHG',# 黄金基金
# '512690.XSHG',#白酒
#'159928.XSHE',#消费
'162605.XSHE',#景顺鼎益
'161903.XSHE',#万家
'161005.XSHE',#富国
'163417.XSHE',#兴全合一
'163402.XSHE',#兴全
'163415.XSHE',#兴全模式
'501054.XSHG',#东征睿泽
'162703.XSHE',#广发小盘
# '512760.XSHG',#半导体50
# '159996.XSHE',#家用电器
# '159995.XSHE',#芯片
# '161226.XSHE',#白银
# '515000.XSHG',#科技
# '512800.XSHG',#银行
# '512720.XSHG',#计算机
# '512400.XSHG',#有色
# '515050.XSHG',#通信
#'588000.XSHG',#科创50
# '510900.XSHG',#H股ETF
# '159920.XSHE',#恒生
# '513500.XSHG',#标普500
#'515220.XSHG',#煤炭
# '159966.XSHE',#创蓝筹
#'513100.XSHG',#纳指
# '512200.XSHG',#房地产
# '512170.XSHG',#医疗
# '515900.XSHG',#央创
# '512960.XSHG',#央调
# '512980.XSHG',#传媒
#'159967.XSHE',#创成长
# '159997.XSHE',#电子
# '159959.XSHE',#央企
# '159819.XSHE',#人工智能
# '515210.XSHG',#钢铁
# '159807.XSHE',#科技
]
yesterday = context.previous_date
list_date = get_before_after_trade_days(yesterday, g.lag) # 今天的前g.lag1个交易日的日期
g.ETFList = []
all_funds = get_all_securities(types='fund', date=yesterday) # 上个交易日之前上市的所有基金
for symbol in ETF_targets:
if symbol in all_funds.index:
if all_funds.loc[symbol].start_date <= list_date: # 对应的基金也已经在要求的日期前上市 g.ETFList.append(symbol) # 则列入可交易对象中 return # 每日交易时 def ETFtrade(context): record(cangWei = g.allCang) if g.signal == 'CLEAR': for stock in context.portfolio.positions: if stock == g.empty_keep_stock: continue log.info("清仓: %s" % stock) order_target(stock, 0) elif g.signal == 'BUY': if g.empty_keep_stock in context.portfolio.positions: order_target(g.empty_keep_stock, 0) # holdings = set(context.portfolio.positions.keys()) # 现在持仓的 targets = set(g.buy) # 想买的目标 # # 1. 卖出不在targets中的 sells = holdings - targets for code in sells: log.info("卖出: %s" % code) order_target(code, 0) # ratio = len(targets) if ratio>0:
cash = context.portfolio.total_value
# 2. 交集部分调仓
adjusts = holdings & targets
for code in adjusts:
# 手续费最低5元，只有交易5000元以上时，交易成本才会低于千分之一，才去调仓
log.info('调仓: %s' % code)
order_target_value(code, cash*g.cang[code])
# 3. 新的，买入
purchases = targets - holdings
for code in purchases:
log.info('买入: %s' % code)
order_target_value(code,cash*g.cang[code])
#
current_returns = 100 * context.portfolio.returns
log.info("当前收益：%.2f%%，当前持仓: %s", current_returns, list(context.portfolio.positions.keys()))
# if len(context.portfolio.positions) == 0:
order_target_value(g.empty_keep_stock, context.portfolio.available_cash)
# 获取信号
def get_signal(context):
# # 创建保持计算结果的DataFrame
current_data = get_current_data()
fen = 1.0/len(g.ETFList)
g.cang = 0
df_etf = pd.DataFrame(columns=['基金代码','周期涨幅'])
mZhangfu = 0
g.buy = []
g.cang = {}
g.allCang = 0
for mkt_idx in g.ETFList:
security = mkt_idx # 指数对应的基金
# 获取股票的收盘价
close_data = attribute_history(security, g.lag, '1d', ['close'], df=False)
# 获取股票现价
zhangfu = 0
current_price = current_data[security].last_price
# 取得平均价格
ma_n5 = close_data['close'][-5:].mean()
ma_n10 = close_data['close'][-10:].mean()
ma_n20 = close_data['close'][-20:].mean()
ma_n30 = close_data['close'][-30:].mean()
ma_n60 = close_data['close'][-60:].mean()
#总的仓位计算
index = 0
if current_price>ma_n5:
index+=0.3
if current_price>ma_n10:
index+=0.25
if current_price>ma_n20:
index+=0.2
if current_price>ma_n30:
index+=0.15
if current_price>ma_n60:
index+=0.1
cang = 0
if index==1:
cang = fen
elif index>0:
cang = index*0.3*fen
if index>0:
g.buy.append(security)
g.cang[security] = cang
g.allCang += cang
df_etf = df_etf.append({'基金代码':security, '周期涨幅':zhangfu },ignore_index=True)
mZhangfu+=zhangfu
if len(g.buy)==0:
g.signal = 'CLEAR'
log.info("仓位"+str(g.allCang)+"\n清仓")
ETFtrade(context)
return
log.info("仓位"+str(g.allCang)+"持仓数量"+str(g.cang)+"\n交易信号:持有 %s" % g.buy)
g.signal = 'BUY'
ETFtrade(context)
return
