聚宽源码10

原文策略源码如下：
#追三板策略
from operator import itemgetter, attrgetter
import pandas as pd
import numpy as np
import talib as tl

初始化函数，设定要操作的股票、基准等等

def initialize(context):
# 设定沪深300作为基准
set_benchmark(‘000016.XSHG’)
# True为开启动态复权模式，使用真实价格交易
set_option(‘use_real_price’, True)
# 设定成交量比例
set_option(‘order_volume_ratio’, 1)
# 股票类交易手续费是：买入时佣金万分之三，卖出时佣金万分之三加千之一印花税, 每笔交易佣金最低扣5块钱
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’)
g.buylist=[]
# 持仓数量
g.stocknum =10
# 持仓天数
g.holdDays=[]
for days in range(0,7):
g.holdDays.append([])

# 股票买入金额 
g.cash_per_stock = context.portfolio.portfolio_value / g.stocknum
# 运行函数
# run_daily(trade,time="open")
# 止盈百分比
g.cut_gain_percentage = 0.2
# 止损百分比
g.cut_loss_percentage = 0.5

形态和成交额选股

def check_stocks(context):

设定查询条件

q = query(
        valuation.code,
        valuation.circulating_market_cap,
        valuation.circulating_cap
    ).filter(
        valuation.circulating_market_cap.between(8,50)
    ).order_by(
        valuation.circulating_market_cap.asc()
    )
# 选股
df = get_fundamentals(q)
resultdict=dict()
buylist=[]
for index,row in df.iterrows():
    hData = attribute_history(row['code'], 10, unit='1d'
        , fields=['open', 'close', 'high', 'low', 'volume', 'money','high_limit']
        , skip_paused=False
        , df=False)
    closes = hData['close']
    amount = hData['money']
    vl=hData['volume']
    hlm=hData['high_limit']
    lows=hData['low']
    highs=hData['high']
    opens=hData['open']
    if np.isnan(closes[0]):
        continue
    
    if amount[-1]>2200000000:
        continue
    
    if vl[-1]!=max(vl[-3:]):
        continue
    
    if amount[-3]/amount[-4]<2:
        continue
    
    if closes[-1]!=hlm[-1] or closes[-2]!=hlm[-2] or closes[-3]!=hlm[-3] or closes[-4]==hlm[-4]:
        continue
    
    resultdict[row['code']]=closes[-1]
    log.info(row['code'],get_security_info(row['code']).display_name,amount[-1],context.current_dt)

sorteddict=sorted(resultdict.iteritems(), key=itemgetter(1),reverse=False)
for v in sorteddict:
    buylist.append(v[0])
g.buylist = filter_paused_stock(buylist)

def before_trading_start(context):
check_stocks(context)

交易函数

def handle_data(context, data):
if context.current_dt.hour != 9 or context.current_dt.minute != 30:
return
todaylist=[]
# 如果有持仓，则卖出
if len(list(context.portfolio.positions.keys())) > 0 :
to_sell = sell_signal(context)
for security in to_sell[1]:
order_target(security, 0)
for security in to_sell[0]:
order_target(security,0)
if len(g.holdDays[-1])>0:
for security in g.holdDays[-1]:
order_target(security, 0)
g.holdDays.insert(0,[])
g.holdDays=g.holdDays[:-1]
## 选股
if len(g.buylist)==0:
return
buynum = g.stocknum - len(context.portfolio.positions.keys())
if buynum<=0:
return
stock_list = g.buylist[:buynum]
cash = context.portfolio.portfolio_value /(len(stock_list)+1)
for stock in stock_list:
if stock in list(context.portfolio.positions.keys()):
continue
if len(context.portfolio.positions.keys()) < g.stocknum:
close = attribute_history(stock, 1, ‘1d’, [‘close’])[‘close’][0]
curOpen = get_current_data()[stock].day_open
if curOpen>=close*0.94:
order_value(stock, cash)
todaylist.append(stock)

g.holdDays[0].extend(todaylist)

过滤停牌股票

def filter_paused_stock(stock_list):
cur_data = get_current_data()
# 非停牌、非ST
stock_list = [stock for stock in stock_list if
(not cur_data[stock].paused) and
(not cur_data[stock].is_st) and
(‘ST’ not in cur_data[stock].name) and
(’*’ not in cur_data[stock].name) and
(‘退’ not in cur_data[stock].name)]

return stock_list

def sell_signal(context):
# 建立需要卖出的股票list
to_sell_gain = []
to_sell_lost = []
# 对于仓内所有股票
for security in context.portfolio.positions:
# 取现价
current_price = attribute_history(security, 1, ‘1d’, [‘close’])[‘close’][0]
# 获取买入平均价格
avg_cost = context.portfolio.positions[security].avg_cost
# 计算止盈线
high = avg_cost * (1+ g.cut_gain_percentage)
# 计算止损线
low = avg_cost*(1-g.cut_loss_percentage)
# 如果价格突破了止损或止盈线
if current_price >= high:
to_sell_gain.append(security)
if current_price <= low:
to_sell_lost.append(security)
return(to_sell_gain,to_sell_lost)

来源：CSDN

作者：程序化小学生

链接：https://blog.csdn.net/weixin_45131220/article/details/103487957