def _getFunctionArgs(fname, line, fn_name):
"""分析文件文本获取参数表达式"""
key = key_prefix + agl.MD5(fname + str(line) + fn_name)
val = myredis.createRedisVal(key, [])
if len(val.get()) > 0:
return val.get()
f = open(fname, 'r')
s = f.readlines()
f.close()
line -= 1
while line >= 0:
if s[line].find(fn_name) >= 0:
break
line -= 1
#print line
s = s[line:]
#过滤出表达式
full_s = ''
for s_l in s:
cur_line_s = s_l.strip()
if cur_line_s[0] == '#':
continue
full_s += cur_line_s
if cur_line_s.find(')') >= 0:
break
full_s = full_s.replace(fn_name, '')
full_s = full_s.replace('(', '')
full_s = full_s.replace(')', '')
if full_s[-1] == ',':
full_s = full_s[:-1]
#print full_s
args = full_s.split(',')
#print args
val.set(args)
return args
def test():
pl = publish.Publish()
base_boll_up = get_boll_up_base()
base_boll_lower = get_boll_lower_base()
def get_data():
code = jx.HCGD
df = tdx.getFive(code)
return df
key = myredis.gen_keyname(__file__, Recognize_boll.test)
df = myredis.createRedisVal(key, get_data).get()
df = stock.TDX_BOLL_df(df)
for i in range(60, len(df)):
df_cur = df[:i]
c = Recognize_boll(base_boll_up, df_cur)
c2 = Recognize_boll(base_boll_lower, df_cur)
b = c._calc_beta_up()
b2 = c._calc_beta_lower()
#if c2.is_matched():
#print(b2)
if abs(c.sign()) > 0 or abs(c2.sign()) > 0:
df_cur = df_cur[['c', 'boll_up', 'boll_mid', 'boll_lower']]
df_cur.index = range(len(df_cur))
df_cur.plot()
pl.show()
def get_boll_up_base():
"""获取一个用来作为标准的曲线
return : np.ndarray
"""
code = jx.HCGD
t = '2018-12-3 10:30:00'
t = '2018-11-30 10:00:00'
key = myredis.gen_keyname(__file__, get_boll_up_base)
df = myredis.createRedisVal(
key, lambda: stock.getFiveHisdatDf(code, method='tdx')).get()
upper, middle, lower = stock.TDX_BOLL(df['c'].values)
df['upper'] = upper
df = df[t:]
df = df[df.index[20]:]
df = df[:df.index[29]]
#ui.DrawTs(pl, df['upper'].values)
#pl.show()
return df['upper'].values
def test_recog_history_boll():
from pypublish import publish
pl = publish.Publish(explicit=True)
code = jx.ZCKJ.b
def get_local_codes():
data_path = 'cnn_boll/datasources/'
return np.array([str(f).split('.')[0] for f in os.listdir(data_path)])
from sklearn.utils import shuffle
key = myredis.gen_keyname(__file__, test_recog_history_boll)
codes = myredis.createRedisVal(key, get_local_codes).get()
code = shuffle(codes)[0]
#ui.DrawTs(pl, x)
#x = get_boll_up_base()
x = get_boll_lower_base()
p = 0.75
#report = ['init', pl.get_CurImgFname()]
report = recog_history_boll(pl, x, p, code)
pl.RePublish(report)
def Run(self):
"""
"""
#self._log('Strategy_Boll_Pre')
#以下为交易测试
code = self.data.get_code() #当前策略处理的股票
self.code = code
if not self.is_backtesting and not self.AllowCode(code):
return
df_hisdat = self.data.get_hisdat(code) #日k线
df_five_hisdat = self.data.get_hisdat(code, dtype='5min') #5分钟k线
df_fenshi = self.data.get_fenshi(code) #日分时
if len(df_fenshi) == 0:
self.data.log(code + u"未取到分时数据")
return
account = self._getAccount() #获取交易账户
price = float(df_fenshi.tail(1)['p']) #当前股价
closes = df_hisdat['c']
yestoday_close = closes[-2] #昨日收盘价
zhangfu = stock.ZhangFu(price, yestoday_close)
self._log(
'price=%.2f %s %s' %
(price, str(df_fenshi.index[-1]), str(df_five_hisdat.iloc[-1])))
account_mgr = ac.AccountMgr(account, price, code)
trade_num = ac.ShouShu(account_mgr.init_money() *
self.trade_num_use_money_percent / price)
trade_num = max(100, trade_num)
# 信号计算
four = stock.FOUR(closes)
four = four[-1]
upper, middle, lower = stock.TDX_BOLL(df_five_hisdat['c'])
highs, lows, closes = df_five_hisdat['h'], df_five_hisdat[
'l'], df_five_hisdat['c']
adx = stock.TDX_ADX(highs, lows, closes)
self._log('boll : %.2f,%.2f,%.2f' % (upper[-1], middle[-1], lower[-1]))
boll_w = abs(upper[-1] - lower[-1]) / middle[-1] * 100
#50个周期内最高值
is_high = abs(price - max(df_five_hisdat[-1000:]['h'])) / price < 0.005
boll_poss = [
upper[-1],
(upper[-1] - middle[-1]) / 2 + middle[-1],
middle[-1],
(middle[-1] - lower[-1]) / 2 + lower[-1],
lower[-1],
]
self._log('boll_poss: %.2f, %.2f boll_w=%.2f adx=%.2f' %
(boll_poss[0], boll_poss[1], boll_w, adx[-1]))
#上一个成交的价位
pre_price = account_mgr.last_chengjiao_price()
pre_pre_price = account_mgr.last_chengjiao_price(index=-2)
sell_count = account_mgr.queryTradeCount(1)
buy_count = account_mgr.queryTradeCount(0)
#买入均价
adx = adx[-1]
boll_up_ratio = 0.02
#信号判断
num = 0
if so.assemble(
price > boll_poss[1],
price > pre_price * (1 + self.trade_ratio),
#price > boll_poss[2],
#price > self.max_buy_price*(1+self.trade_ratio),
#boll_w > 3.5,
#adx > 60,
#sell_count < 2,
#pr.horizontal(df_five_hisdat),
0,
):
num = -trade_num
self.trade_status = self.enum.boll_up_mid
#if self.order(1, code, price, num):
#self._log(agl.utf8_to_ascii('一档卖出%s, %.2f, %d'%(code, price, num)))
if so.assemble(
price > boll_poss[0],
price > pre_price * (1 + self.trade_ratio),
#price > self.max_buy_price*(1+self.trade_ratio),
#boll_w > 3,
adx > 60,
is_high,
#four > self.trade_four[1],
#sell_count < 2,
#self.trade_status == self.enum.nothing,
#0,
):
num = -trade_num * 3
self.trade_status = self.enum.boll_up
#if self.order(1, code, price, num):
#self._log(agl.utf8_to_ascii('二档卖出%s, %.2f, %d'%(code, price, num)))
if so.assemble(
price < boll_poss[-2] * (1 + boll_up_ratio),
price < pre_price * (1 - self.trade_ratio),
#price < boll_poss[2],
#price < self.min_sell_price*(1-0.03),
#boll_w > 3.5,
#adx>60,
#buy_count < 2,
#pr.horizontal(df_five_hisdat),
0,
):
num = trade_num
self.trade_status = self.enum.boll_down_mid
#if boll_w > 6:
#num *= self.trade_num_ratio
#if self.order(0, code, price, num):
#self._log(agl.utf8_to_ascii('一档买入%s, %.2f, %d'%(code, price, num)))
if so.assemble(
price < boll_poss[-1],
price < pre_price * (1 - self.trade_ratio),
#price < self.min_sell_price*(1-0.03),
#boll_w > 3,
#buy_count < 2,
#self.trade_status == self.enum.nothing,
#adx>70,
#four < self.trade_four[0],
#0,
):
num = trade_num * 3
#num = account_mgr.last_chengjiao_num()
self.trade_status = self.enum.boll_down
#if self.order(0, code, price, num):
#self._log(agl.utf8_to_ascii('二档买入%s, %.2f, %d'%(code, price, num)))
#成本区间
if so.assemble(
price < pre_price * (1 - 0.05),
four < -0.1,
self.trade_status == self.enum.boll_up,
0,
):
num = trade_num * self.trade_num_ratio
self.trade_status = self.enum.nothing
if so.assemble(
price > pre_price * (1 + 0.05),
four > 0.1,
self.trade_status == self.enum.boll_down,
0,
):
num = -trade_num * self.trade_num_ratio
self.trade_status = self.enum.nothing
#zz顶抛出后回补
if so.assemble(
price < pre_price * (1 - 0.02),
#sell_count >= 2,
self.trade_status == self.enum.zz_up,
0,
):
#上次zz卖出的数量
num = account_mgr.last_chengjiao_num()
self.trade_status = self.enum.zz_hui_bu
if so.assemble(
price > pre_price * (1 + 0.02),
#sell_count >= 2,
self.trade_status == self.enum.zz_down,
0,
):
#上次zz卖出的数量
num = account_mgr.last_chengjiao_num()
self.trade_status = self.enum.zz_hui_bu
#计算分时zz
zz_sign = 0
closes = df_five_hisdat['c'][-200:].values
zz = stock.ZigZag(closes)
if len(zz) > 2:
zz_result = stock.analyzeZZ(zz)
zz_line_ratio = zz_result[1] / zz_result[0] #线段比率
#扑捉大涨回头的信号
if abs(zz_result[0]) > 0.05 and abs(zz_line_ratio) > 0.05 and abs(
zz_line_ratio) < 0.2 and abs(zz_result[0]) > 0.04:
zz_sign = agl.where(zz_result[1] > 0, 1, -1)
if num != 0:
bSell = agl.where(num > 0, 0, 1)
num = abs(num)
#if bSell:
#num = self._compensate(num, bSell, code)
#基本上每天的振幅都在1个点以上
if abs(stock.ZhangFu(price, yestoday_close)) > 0.01:
self.order(bSell, code, price, num)
zz_pre_price = myredis.createRedisVal('policy_basesign_zz_pre_price',
price)
if so.assemble(
zz_sign != 0,
0,
):
#print self.price, getZZPrePrice(self.price),abs(self.price-getZZPrePrice(self.price))/self.price
num = trade_num * 12
bSell = agl.where(zz_sign > 0, 0, 1)
num = self._compensate(num, bSell, code)
bCanOrder = False
if so.assemble(
bSell,
price > zz_pre_price.get() * (1 + 0.03),
#price > pre_price*(1+self.trade_ratio),
):
bCanOrder = True
self.trade_status = self.enum.zz_up
if so.assemble(
(not bSell),
price < zz_pre_price.get() * (1 - 0.03),
#price < pre_price*(1-self.trade_ratio)
):
bCanOrder = True
self.trade_status = self.enum.zz_down
if bCanOrder:
self._getAccount().Order(bSell, code, price, num)
zz_pre_price.set(price)
#信号发生时语音播报, 并通知界面回显
if not self.is_backtesting and (price > boll_poss[1]
or price < boll_poss[-2]):
codename = stock.GetCodeName(code)
s = '%s, %.2f' % (codename, price)
self.data.show(codename) #通知界面显示
self.data.speak2(s) #语音播报
#tick report
if self.is_backtesting and self.is_tick_report:
self._getAccount().TickReport(df_five_hisdat, 'win')
return
def Run(self): account = self._getAccount() code = self.data.get_code() hisdat = self.data.get_hisdat(code) closes = hisdat['c'].dropna() if len(closes)<60: return #计算技术指标 four = stock.FOUR(closes) boll_up, boll_mid, boll_low ,boll_w= stock.TDX_BOLL2(closes) adx = stock.TDX_ADX(hisdat['h'], hisdat['l'], closes) #收集使用过的技术指标，为了报告使用 self.tech = closes, four, boll_up, boll_mid, boll_low ,boll_w, adx four = four[-1] price = hisdat.iloc[-1]['c'] #print self.getCurTime(), four, price, boll_low[-1], boll_w[-1] #基于价值回归及成本控制，加上技术指标择时 #缺点， 资金利用率低， 选股必须选好， 否则碰到一直下跌的情况就会造成亏损 #优点, 成本在相对安全区域 #当成本亏base_ratio%时加倍买入 df_stock_list = account.StockList() df_stock_list = df_stock_list[df_stock_list['证券代码'] == code] #第一次买 if so.assemble(four<self.base_four[0] ,len(df_stock_list) == 0 , adx[-1] > 60, #price<boll_low[-1] , #boll_w[-1]>2 ): if hasattr(self, 'base_num_ratio'): total_money = ac.AccountMgr(self._getAccount(), price, code).init_money() self.base_num = ac.ShouShu(total_money*self.base_num_ratio/price) self._getAccount().Order(0, code, price, self.base_num) if len(df_stock_list) > 0: chengben = float(df_stock_list['参考盈亏成本价'].loc[0]) changwei = int(df_stock_list['库存数量'].loc[0]) yinkui = calcYinKui(price, chengben) #agl.Print( '盈亏:', chengben, yinkui, changwei, '频繁') if price < chengben*(1-self.base_ratio): #如果仓位大于总资金的70%， 那么仓位为剩余资金的一半 df_zhijing = account.ZhiJing() canuse_money = float(df_zhijing['可用'][-1]) total_money = canuse_money + changwei*price if canuse_money/total_money<self.base_rhombus_mid_ratio: num = ac.ShouShu(canuse_money/2/price) #这里开始就按上次交易的价格来作为判断, 最后一个买入 df_chengjiao = account.ChengJiao() df_chengjiao = df_chengjiao[df_chengjiao['买0卖1'] == '0'] last_buy_price = float(df_chengjiao['成交价格'][-1]) if price > last_buy_price*(1-self.base_ratio): num = 0 else: num = changwei*self.base_pramid_ratio if num>0: account.Order(0, code, price, num) #当赚base_ratio%时加倍卖出 chengben = float(df_stock_list['买入均价'].loc[0]) sell_avg_price = myredis.createRedisVal(self.key_sell_avg_price, chengben) sell_num = myredis.createRedisVal(self.key_sell_num, 0) if price>chengben*(1+self.base_ratio) and four>self.base_four[1]: #第一次卖 if sell_num.get() == 0: account.Order(1, code, price, self.base_num) sell_avg_price.set(price) sell_num.set(self.base_num) #print '--',chengben, sell_avg_price.get()*(1+self.base_ratio) if price > sell_avg_price.get()*(1+self.base_ratio): num = int(df_stock_list['可卖数量'].loc[0]) num = min(sell_num.get() * self.base_pramid_ratio, num) num = ac.ShouShu(num) if num > 0: account.Order(1, code, price, num) #更新卖出均价 new_sell_avg_price = (sell_avg_price.get() * sell_num.get()+price*num)/(sell_num.get()+num) sell_avg_price.set(new_sell_avg_price) sell_num.set(sell_num.get()+num)
