def __init__(self,
user="******",
password="******",
zuhe="ZH1008383"):
self.user = easytrader.use('xq')
self.user.prepare(user=user, password=password, portfolio_code=zuhe)
self.buylist = []
self.selllist = []
self.limitnum = 20
self.m = MongoDB()
class dbdata():
def __init__(self):
self.m = MongoDB(DB_SERVER, DB_PORT, USER, PWD, AUTHDBNAME)
self.formatlist = [
"date", "volume", "close", "high", "low", "open", "pre_close"
]
return
def dbserver(self):
return DB_SERVER
def _getdata(self,trade_date=dt.datetime.today(),collection="000923.SZ",
db="ml_security_table",\
out=[],isfilt=True,filt={}):
if not out: out = self.formatlist
if isfilt and not filt: filt = \
{"date":{"$gt": trade_date, "$lt": trade_date+dt.timedelta(days=1)}}
query = self.m.read_data(db, collection, filt=filt)
return self.formatquery(query, out)
def formatquery(self, query, out):
'''
query:your source data ,should be a list with dict
out:the fields you want to convert into dataframe
'''
if not out:
query = [i for i in query.sort("policy", 1)]
else:
query = [{k: i[k] for k in out} for i in query.sort("policy", 1)]
return query
def __init__(self, startdate=(2011, 1, 1), enddate=[]):
self.startdate = datetime.datetime(*startdate, 0, 0, 0, 0)
self.enddate = enddate
self.m = MongoDB(DB_SERVER, DB_PORT, USER, PWD, AUTHDBNAME)
self.formatlist = [
"date", "volume", "close", "high", "low", "open", "pre_close"
]
self.savevols = [
"stock", "buy_date", "sell_date", "holddays", "profit", "features"
]
self.looplist = []
self.trading_records = []
self.holding_records = []
self.datalst = []
self.collection = None
self.tempstatus = []
self.lateststatus = []
def connetdb(self):
self.m = MongoDB(DB_SERVER, DB_PORT, USER, PWD, AUTHDBNAME)
return self.m
def __init__(self, stock="600455.SH"):
self.m = MongoDB(DB_SERVER, DB_PORT, USER, PWD, AUTHDBNAME)
class strategy1(object):
'''
trading volume is the lowest in 60 days
'''
def __init__(self, stock="600455.SH"):
self.m = MongoDB(DB_SERVER, DB_PORT, USER, PWD, AUTHDBNAME)
def _getdata(self, db="ml_security_table", collection="600455.SH"):
query = self.m.read_data(
db,
collection,
filt={"date": {
"$gt": datetime.datetime(2013, 1, 1, 0, 0, 0, 0)
}})
return self.m.format2dataframe(query)
# 获取股票列表
def import_stocklist(self, stocklistname):
# df = pd.read_csv(str(stocklistname) + '.csv', parse_dates=['date'])
df = pd.read_csv(str(stocklistname) + '.csv',
parse_dates=['startdate'])
return df
def mean_volume(self, data):
m_vol = sum(data) / len(data)
return m_vol
def buy_condition1(self,
dat,
vol,
vol_data,
close,
last_high,
maxprice,
minprice,
count,
vol_weight=1.2):
if self.condition1(vol, vol_data, vol_weight) and \
self.condition2(close, last_high) and \
self.condition3(close, maxprice) and \
self.condition4(close, minprice):
return True
return False
def buy_condition(self,
dat,
vol,
vol_data,
close,
last_high,
maxprice,
minprice,
count,
parameter,
vol_weight=1.2):
if self.condition6(dat, parameter[1]) and \
self.condition7(close, parameter[0]):
return True
return False
def buy_condition2(self,
dat,
vol,
vol_data,
close,
last_high,
maxprice,
minprice,
count,
parameter,
vol_weight=1.2):
if self.condition8(close):
return True
return False
def sell_conditon(self,
buy_price,
currentday_high,
currentday_low,
hold_days,
gain_grads=0.1,
loss_grads=-0.05,
dayout=10):
if self.stopgain_condition(buy_price, currentday_high, gain_grads):
return True, "stopgain"
elif self.stoploss_condition(buy_price, currentday_low, loss_grads):
return True, "stoploss"
elif self.holdingtime_condition(hold_days, dayout):
return True, "holdtime"
return False, None
def sell_conditon1(self,
buy_price,
currentday_high,
currentday_low,
hold_days,
gain_grads=0.1,
loss_grads=-0.05,
dayout=10):
if self.condition9(close):
return True, "Sold"
return False, None
def stopgain_condition(self, buy_price, current_price, grads=0.1):
if (current_price - buy_price) / buy_price >= grads:
return True
return False
def stoploss_condition(self, buy_price, current_price, grads=-0.05):
if (current_price - buy_price) / buy_price <= grads:
return True
return False
def holdingtime_condition(self, hold_days, dayout=10):
if hold_days >= dayout:
return True
return False
def condition1(self, vol, vol_data, vol_weight=1.2):
if vol >= vol_weight * self.mean_volume(vol_data):
return True
return False
def condition2(self, close, last_high):
if close >= last_high:
return True
return False
def condition3(self, close, high, grads=0.2):
if (high - close) / high >= grads:
return True
return False
def condition4(self, close, low, grads=0.05):
if (close - low) / low <= grads:
return True
return False
def condition5(self, close, low, grads=0.05):
if close <= 100:
return True
return False
def condition6(self, dat, startdate):
newdat = pd.to_datetime(dat)
newdat = newdat.strftime('%Y-%m-%d')
newstartdate = pd.to_datetime(startdate)
newstartdate = newstartdate.strftime('%Y-%m-%d')
# print(newdat)
# print(newstartdate)
if newdat > newstartdate:
return True
return False
def condition7(self, close, cashprice):
if close < cashprice:
return True
return False
def condition8(self, close):
if close < 110:
return True
return False
def condition9(self, close):
if close > 120:
return True
return False
def formatdata(self, stock, source="mongodb"):
if source == "mongodb":
df = self._getdata(collection=stock)
elif source == "tushare":
df = ts.get_hist_data(
stock,
start='2005-01-01',
end='2016-11-18',
)
df.sort_index(inplace=True)
df["date"] = df.index
df["pre_close"] = df["close"] - df["price_change"]
df.to_csv(stock + '.csv')
return df
def histofyreturn(self,
parameter,
db="ml_security_table",
table="",
source="mongodb"):
buy = []
stopgain = 0.1
stoploss = -0.1
vol_day = 10
price_day = 60
count = 60
transaction_record = []
df = self.formatdata(table, source)
# df.to_csv(table+'.csv')
lst = [
l for l in df[[
"date", "volume", "close", "high", "low", "open", "pre_close"
]].fillna(0).values if l[1] != 0
]
for line in lst[count:]:
dat = line[0]
vol = line[1]
if vol == 0: continue
close = line[2]
last_high = lst[count - 1][3]
vol_data = [i[1] for i in lst[count - vol_day:count]]
maxprice = max([i[3]] for i in lst[count - price_day:count])[0]
minprice = min([i[4]] for i in lst[count - price_day:count])[0]
for b in buy[:]:
d = b[0]
c = b[1]
buy_date = d[0]
sell_date = line[0]
hold_days = count - c
buy_price = d[2]
currentday_high = line[3]
currentday_low = line[4]
is_sell, selltype = self.sell_conditon(buy_price, currentday_high, currentday_low, \
hold_days, gain_grads=0.1, \
loss_grads=-0.05, dayout=10)
if is_sell:
#print(buy)
#print(b)
buy.remove(b)
if selltype == "stopgain":
profit = stopgain
elif selltype == "stopgain":
profit = stoploss
else:
profit = (close - buy_price) / buy_price
transaction_record.append(
[table, buy_date, sell_date, hold_days, profit])
# print (profit)
# print(transaction_record)
if self.buy_condition(dat,
vol,
vol_data,
close,
last_high,
maxprice,
minprice,
count,
parameter,
vol_weight=1.2):
buy.append((line, count, table))
count += 1
# df_buy = pd.DataFrame(transaction_record)
# df_buy.to_csv("test_tushare_buy_new.csv")
return transaction_record, buy
def filter_with_all_stocks(self, stocklist, source="mongodb"):
error_list = []
result = []
buyresult = []
for i in stocklist:
print(i[0])
startdate = i[1]
cashprice = i[2]
# try:
r, buyed = self.histofyreturn(startdate,
cashprice,
table=i[0],
source=source)
# if r: result.extend(r)
result.extend(r)
print(result)
if buyed: buyresult.extend(buyed)
# except:
# error_list.append(i)
return result, buyresult, error_list
def filter_with_all_stocks_new(self, df, source="mongodb"):
error_list = []
result = []
buyresult = []
par = []
count = 0
df_len = len(df.index)
column_num = len(df.count())
while (count < df_len):
columncount = 1
# print(df.iat[0,1])
par = []
while (columncount < column_num):
par.append(df.iat[count, columncount])
columncount = columncount + 1
print(par)
# try:
print(count)
print(df.iat[count, 0])
stock_name = str(df.iat[count, 0])
print(stock_name)
if len(stock_name) == 1:
stock_name = '00000' + stock_name
elif len(stock_name) == 2:
stock_name = '0000' + stock_name
elif len(stock_name) == 3:
stock_name = '000' + stock_name
elif len(stock_name) == 4:
stock_name = '00' + stock_name
elif len(stock_name) == 5:
stock_name = '0' + stock_name
print(stock_name)
r, buyed = self.histofyreturn(par, table=stock_name, source=source)
# if r: result.extend(r)
result.extend(r)
# if buyed: buyresult.extend(buyed)
buyresult.extend(buyed)
# except:
error_list.append(stock_name)
count = count + 1
return result, buyresult, error_list
class trade():
def __init__(self,
user="******",
password="******",
zuhe="ZH1008383"):
self.user = easytrader.use('xq')
self.user.prepare(user=user, password=password, portfolio_code=zuhe)
self.buylist = []
self.selllist = []
self.limitnum = 20
self.m = MongoDB()
def getcurrentdata(self):
'''code：代码, name:名称 ,changepercent:涨跌幅 , trade:现价 ,open:开盘价 ,high:最高价, low:最低价, settlement:昨日收盘价 ,
volume:成交量 ,turnoverratio:换手率 ,amount:成交量 ,per:市盈率 ,pb:市净率, mktcap:总市值 ,nmc:流通市值
'''
out = ["code", "trade", "open", "high", "low", "settlement"]
rst = ts.get_today_all()
return rst[out]
def getbuylist(self, filename="procedure_records.csv"):
s = macd()
self.buylist = s.getprocedure(isdb=True)
return self.buylist
def justsellit(self, db="macd", collection="operatequeue", stoploss=False):
currentdata = {i[0]: i for i in self.getcurrentdata().values}
rst = self.m.read_data(db, collection, filt={"status": "HOLD"})
position = self.getposition()
for i in rst:
stock = i["stock"]
for xq in position:
if stock == xq["stock_code"][2:]:
rate = xq["market_value"] / i["buymoney"]
print("check stock:{} rate:{}".format(stock, rate))
print(i)
print(xq)
print("********************")
bias = 1
if stoploss:
bias = currentdata[stock][4] / currentdata[stock][1]
self.trysell(stock, rate, stoploss, bias=bias)
break
def trysell(self, stock, rate, stoploss=False, bias=1):
if stoploss and rate / bias <= 0.9:
self.sell(stock=stock, number=100, price=0.55)
self.updateholdlst(stock)
elif rate >= 1.1:
self.sell(stock=stock, number=100, price=0.55)
self.updateholdlst(stock)
def updateholdlst(self, stock, db="macd", collection="operatequeue"):
data = {'$set': {'status': 'SELL'}}
self.m.update_data(data, db, collection, filt={"stock": stock})
def getholdlst(self, holddate, db="etfgrid", collection="operatequeue"):
out = ["stock", "buymoney", "date", "startprice", "status"]
#df = self.m._getdata(collection, db, out=out, filt={"date": holddate})
#df = self.m.read_data(db, collection, filt={"date": holddate})
#df = self.m.read_data(db, collection)
df = self.m.read_data(db, collection, filt={"status": "HOLD"})
#if isfilt and not filt: filt = {"date": {"$gt": self.startdate}}
#query = self.m.read_data(db, collection, filt=filt)
print('hold list:')
print(df)
#holdlist = [l for l in df[['date', 'startprice', "buytimes", 'close']].values]
return
#data = {'$set': {'status': 'SELL'}}
#self.m.update_data(data, db, collection, filt={"stock": stock})
def buyitnow(self):
localbackup = []
holdlist = []
tradedate = datetime.today().strftime('%Y-%m-%d')
holdlist = self.getholdlst(tradedate)
print(holdlist)
balance = self.getbalance()
lst = self.getposition()
holdnum = len(lst)
leftmony = balance[0]["current_balance"]
couldbynum = self.limitnum - holdnum
holdlst = [i["stock_code"][2:] for i in lst]
couldbuylst = [i for i in self.buylist if i not in holdlst]
triggerbuynum = len(couldbuylst)
print("could buy list:{}".format(self.buylist))
if couldbynum > 0:
permoney = leftmony / couldbynum
if triggerbuynum > couldbynum:
buylst = random.sample(couldbuylst, couldbynum)
else:
buylst = couldbuylst
else:
buylst = []
for stock in buylst:
try:
t.buy(stock=stock, number=permoney, price=1)
localbackup.append({
"stock":
stock,
"buymoney":
permoney,
"date":
datetime.today().strftime('%Y-%m-%d'),
"status":
"HOLD"
})
except:
print("buy {} failed!!!!".format(stock))
return localbackup
def connetdb(self):
self.m = MongoDB(DB_SERVER, DB_PORT, USER, PWD, AUTHDBNAME)
return self.m
def savebackup2db(self, data, db="etfgrid", collection="operatequeue"):
if not data: return
db = eval('self.m.client.{}'.format(db))
db[collection].insert_many(data)
return
def getbalance(self):
'''output:[{ 'asset_balance': '资产总值','current_balance': '当前余额','enable_balance': '可用金额','market_value': '证券市值',
'money_type': '币种','pre_interest': '预计利息' }]
'''
return self.user.balance
def getposition(self):
'''output:[{'cost_price': '摊薄成本价','current_amount': '当前数量','enable_amount': '可卖数量','income_balance': '摊薄浮动盈亏','keep_cost_price': '保本价',
'last_price': '最新价','market_value': '证券市值','position_str': '定位串','stock_code': '证券代码','stock_name': '证券名称'}]
'''
return self.user.position
def getentrust(self):
'''获取今日委托单
output：[{'business_amount': '成交数量', 'business_price': '成交价格','entrust_amount': '委托数量','entrust_bs': '买卖方向','entrust_no': '委托编号','entrust_price': '委托价格',
'entrust_status': '委托状态', 废单 / 已报 'report_time': '申报时间', 'stock_code': '证券代码', 'stock_name': '证券名称'}]
'''
return self.user.entrust
def buy(self, stock="600461", number=10000, price=0):
self.user.buy(stock, price=price, amount=number)
def cancel(self, entrust):
'''entrust:委托单号
'''
self.user.cancel_entrust(entrust)
def cancelall(self):
entrust = [
i for i in self.getentrust()
if i["entrust_status"] == "已报" and i["entrust_bs"] == "买入"
]
for i in entrust:
flash_entrust = [
i for i in self.getentrust()
if i["entrust_status"] == "已报" and i["entrust_bs"] == "买入"
]
entrust_no = flash_entrust[0]["entrust_no"]
self.cancel(entrust_no)
return
def sell(self, stock="600461", number=100, price=0.55):
self.user.sell(stock, price=price, amount=number)
def adjust_weight(self, stock='000001', weight=10):
self.user.adjust_weight(stock, weight)
def updatebuyoppor(self, buyopporlist, lst, tradetype):
#buyopporlist = []
if not lst: return buyopporlist
for line in lst:
buyopporlist.append({"stock": line, "tradetype": tradetype})
return buyopporlist
class macd(object):
'''
���������������������
'''
def __init__(self):
self.db = MongoDB(DB_SERVER, DB_PORT, USER, PWD, AUTHDBNAME)
self.looplist = []
def setlooplist(self, lst=[]):
excludelst = [
"399001.SZ", "399006.SZ", "000001.SH", "000300.SH", "399005.SZ"
]
if not lst:
self.looplist = [
i for i in self.db.getallcollections("stockdatas")
if i not in excludelst
]
else:
self.looplist = lst
return self.looplist
def buycondition(self, data):
'''out = ["macd","cross_dist"]'''
if data["macd"] > 0 and data["cross_dist"] == 0 \
and data["diff12_26"] < 0:
return True
return False
def enhancebuycondition(self, data):
if data["macd"] < 0 and data["diff12_26"] < 0:
return True
return False
def sellcondition(self, data):
pass
def getlastbuylst(self, ):
dbname = "stockdatas"
num = 1
filt = [{"$sort": {"date": -1}}, {"$limit": num}]
buylst = []
enhancebuylst = []
for i in self.looplist:
collection = i
query = self.db.read_data(dbname,
collection,
filt,
is_aggregate=True)
data = [i for i in query]
if self.buycondition(data[0]):
buylst.append(i)
# print ("normal condition buy stock:{}".format(i))
query = self.db.read_data(dbname,
collection, [{
"$sort": {
"date": -1
}
}, {
"$match": {
"cross_dist": 0,
"macd": {
"$ne": 0
}
}
}, {
"$limit": 2
}],
is_aggregate=True)
newdata = [i for i in query]
if self.enhancebuycondition(newdata[1]):
enhancebuylst.append(i)
print("enhance condition buy stock:{}".format(i))
return buylst, enhancebuylst
def gethistorybuylst(self, starttime="2011-01-01"):
dbname = "stockdatas"
filt = [{
"$sort": {
"date": 1
}
}, {
"$match": {
"date": {
"$gt": starttime
}
}
}]
buylst = []
enhancebuylst = []
for i in self.looplist:
collection = i
query = self.db.read_data(dbname,
collection,
filt,
is_aggregate=True)
data = [i for i in query]
for record in data:
if self.buycondition(record):
buylst.append([i, record["date"], record["close"]])
# print ([i,record["date"],record["close"]])
idx = data.index(record)
precross = idx - data[idx - 1]["cross_dist"] - 1
if precross >= 0 and self.enhancebuycondition(
data[precross]):
enhancebuylst.append(i)
print([i, record["date"], record["close"]])
return buylst, enhancebuylst
def gettraderecord(self, starttime="2011-01-01"):
pass
def getcurrentdata(self):
'''code：代码, name:名称 ,changepercent:涨跌幅 , trade:现价 ,open:开盘价 ,high:最高价, low:最低价, settlement:昨日收盘价 ,
volume:成交量 ,turnoverratio:换手率 ,amount:成交量 ,per:市盈率 ,pb:市净率, mktcap:总市值 ,nmc:流通市值
'''
out = ["code", "trade", "open", "high", "low", "settlement"]
rst = ts.get_today_all()
return rst[out].set_index('code')
def gettradebuylst(self):
dbname = "stockdatas"
num = 1
emaslow = 26
emafast = 12
demday = 9
filt = [{"$sort": {"date": -1}}, {"$limit": num}]
buylst = []
currentdata = self.getcurrentdata()
for i in self.looplist:
collection = i
query = self.db.read_data(dbname,
collection,
filt,
is_aggregate=True)
data = [i for i in query]
s_ema = data[0]["ema26"]
f_ema = data[0]["ema12"]
dem = data[0]["dem9"]
macd = data[0]["macd"]
try:
c_close = currentdata["trade"].ix[collection.split(".")[0]]
n_s_ema = (s_ema * (emaslow - 1) + 2 * c_close) / (emaslow + 1)
n_f_ema = (f_ema * (emafast - 1) + 2 * c_close) / (emafast + 1)
n_diff = n_f_ema - n_s_ema
n_dem = (dem * (demday - 1) + 2 * n_diff) / (demday + 1)
n_macd = 2 * (n_diff - n_dem)
if macd * n_macd < 0 and n_macd > 0:
buylst.append(collection)
except:
print("error stock:{}".format(collection))
[print("buylist:{}".format(collection)) for collection in buylst]
return buylst
def save2db(self):
pass
def save2csv(self):
pass
def __init__(self):
self.db = MongoDB(DB_SERVER, DB_PORT, USER, PWD, AUTHDBNAME)
self.looplist = []
def __init__(self):
self.m = MongoDB(DB_SERVER, DB_PORT, USER, PWD, AUTHDBNAME)
self.formatlist = [
"date", "volume", "close", "high", "low", "open", "pre_close"
]
return
def __init__(self, stock="600455.SH"):
self.m = MongoDB()
class strategy1(object):
'''
trading volume is the lowest in 60 days
'''
def __init__(self, stock="600455.SH"):
self.m = MongoDB()
def _getdata(self, db="ml_security_table", collection="600455.SH"):
query = self.m.read_data(
db,
collection,
filt={"date": {
"$gt": datetime.datetime(2013, 1, 1, 0, 0, 0, 0)
}})
return self.m.format2dataframe(query)
def histofyreturn(self, db="ml_security_table", table=""):
holding = []
w = 40
# stocklist = self.m.getallcollections(db)
df = self._getdata(collection=table)
# df.to_csv('600455.csv')
count = 1
origindata = []
lst = [
l for l in df[["date", "volume", "close", "high"]].fillna(0).values
if l[1] != 0
]
for line in lst:
if line[1] == 0: continue
origindata.append(line[1])
if count == w:
sorteddata = sorted(origindata)
elif count > w:
index = self.getnewindex(sorteddata, line[1], w)
for h in holding:
if (line[3] - h[1]) / h[1] >= 0.1:
# print (count-h[0])
print(0.1)
holding.remove(h)
HOLDS.append(count - h[0])
elif (line[3] - h[1]) / h[1] <= -0.10 or count - h[0] > 20:
if count - h[0] > 20:
print("{},{}".format((line[3] - h[1]) / h[1],
line[0]))
else:
print("{},{}".format(-0.1, line[0]))
RESULTS.append((line[3] - h[1]) / h[1])
HOLDS.append(count - h[0])
holding.remove(h)
if index == 0:
holding.append((count, line[2], line[0]))
DATES.append(line[0])
# print ("{} {} {}".format(line[0],index,(lst[count+2][2]-lst[count][2])/lst[count][2]))
sorteddata.insert(index, line[1])
sorteddata.remove(origindata[0])
del origindata[0]
count += 1
print(table)
# print (holding)
def getnewindex(self, datas, newdata, l, base=0):
'''datas:should be sorted from min to max,
newdats: the data need to check the position in the sorted datas
l:len of data
base:first position of the datas in origin datas
'''
index = int(l / 2)
if datas[index] > newdata:
if l == 1: return base
d = datas[:index]
nl = index
return self.getnewindex(d, newdata, nl, base)
elif datas[index] < newdata:
if l <= 2: return base + l
d = datas[index + 1:]
nl = int((l - 1) / 2)
return self.getnewindex(d, newdata, nl, base + index + 1)
elif datas[index] == newdata:
return base + index
c_preclose = line[6]
if c_preclose != close:
reh = [[i[0], i[1] * c_preclose / close] for i in reh]
reh.append([c, c_preclose / close])
close = line[2]
c += 1
result = []
sc = 0
ec = 0
for idx in range(len(reh)):
weight = reh[idx][1]
ec = reh[idx][0]
piece = self.recount(lst, sc, ec, weight)
result.extend(piece)
sc = ec
result.extend(lst[sc:])
return result
def recount(self, lst, sc, ec, weight):
rst = []
for line in lst[sc:ec]:
rst.append([line[0], line[1], *[i * weight for i in line[2:]]])
return rst
if __name__ == '__main__':
db = MongoDB(DB_SERVER, DB_PORT, USER, PWD, AUTHDBNAME)
s = StockIndicator(mdb=db)
s.setlooplist()
s.updateallstocks2db()
class strategy1(object):
'''
trading volume is the lowest in 60 days
'''
def __init__(self, stock="600455.SH"):
self.m = MongoDB(DB_SERVER, DB_PORT, USER, PWD, AUTHDBNAME)
def _getdata(self, db="ml_security_table", collection="600455.SH"):
query = self.m.read_data(
db,
collection,
filt={"date": {
"$gt": datetime.datetime(2011, 1, 1, 0, 0, 0, 0)
}})
return self.m.format2dataframe(query)
# 获取股票列表
def import_stocklist(self, stocklistname):
#df = pd.read_csv(str(stocklistname) + '.csv', parse_dates=['date'])
df = pd.read_csv(str(stocklistname) + '.csv', parse_dates=['code'])
return df
def mean_volume(self, data):
m_vol = sum(data) / len(data)
return m_vol
def buy_condition(self,
vol,
vol_data,
close,
last_high,
maxprice,
minprice,
count,
highindex,
vol_weight=1.2):
if self.condition1(vol,vol_data,vol_weight) and \
self.condition2(close,last_high) and \
self.condition3(close,maxprice) and \
self.condition4(close,minprice) and \
self.condition5(count,highindex):
return True
return False
def sell_conditon(self,
buy_price,
currentday_high,
currentday_low,
hold_days,
gain_grads=0.1,
loss_grads=-0.05,
dayout=10):
if self.stopgain_condition(buy_price, currentday_high, gain_grads):
return True, "stopgain"
elif self.stoploss_condition(buy_price, currentday_low, loss_grads):
return True, "stoploss"
elif self.holdingtime_condition(hold_days, dayout):
return True, "holdtime"
return False, None
def stopgain_condition(self, buy_price, current_price, grads=0.1):
if (current_price - buy_price) / buy_price >= grads:
return True
return False
def stoploss_condition(self, buy_price, current_price, grads=-0.05):
if (current_price - buy_price) / buy_price <= grads:
return True
return False
def holdingtime_condition(self, hold_days, dayout=10):
if hold_days >= dayout:
return True
return False
def condition1(self, vol, vol_data, vol_weight=1.2):
if vol >= vol_weight * self.mean_volume(vol_data):
return True
return False
def condition2(self, close, last_high):
if close >= last_high:
return True
return False
def condition3(self, close, high, grads=0.2):
if (high - close) / high >= grads:
return True
return False
def condition4(self, close, low, grads=0.05):
if (close - low) / low <= grads:
return True
return False
def condition5(self, currentday, highday, grads=60):
if currentday - highday >= grads:
return True
return False
def formatdata(self, stock, source="mongodb"):
if source == "mongodb":
df = self._getdata(collection=stock)
elif source == "tushare":
df = ts.get_hist_data(
stock,
start='2016-08-01',
end='2016-11-18',
)
df.sort_index(inplace=True)
df["date"] = df.index
df["pre_close"] = df["close"] - df["price_change"]
return df
def is_ex_right(self, currentprice, lastprice):
if currentprice != lastprice:
return True, currentprice / lastprice
else:
return False, 1.0
def histofyreturn(self,
db="ml_security_table",
table="",
source="mongodb"):
buy = []
stopgain = 0.1
stoploss = -0.5
vol_day = 30
price_day = 90
count = 90
transaction_record = []
df = self.formatdata(table, source)
lst = [
l for l in df[[
"date", "volume", "close", "high", "low", "open", "pre_close"
]].fillna(0).values if l[1] != 0
]
for line in lst[count:]:
vol = line[1]
if vol == 0: continue
close = line[2]
last_high = lst[count - 1][3]
vol_data = [i[1] for i in lst[count - vol_day:count]]
maxprice = max([i[3]] for i in lst[count - price_day:count])[0]
maxindex = [
i for i in range(count - price_day, count)
if lst[i][3] == maxprice
][0]
minprice = min([i[4]] for i in lst[count - price_day:count])[0]
ex_right, ex_right_rate = self.is_ex_right(line[6],
lst[count - 1][2])
for b in buy[:]:
d = b[0]
c = b[1]
buy_date = d[0]
sell_date = line[0]
hold_days = count - c
buy_price = d[2]
currentday_high = line[3]
currentday_low = line[4]
is_sell,selltype = self.sell_conditon(buy_price,currentday_high,currentday_low,\
hold_days,gain_grads=0.1,\
loss_grads=-0.05,dayout=10)
if is_sell:
buy.remove(b)
if selltype == "stopgain":
profit = stopgain
elif selltype == "stoploss":
# profit = stoploss
profit = (close - buy_price) / buy_price
else:
profit = (close - buy_price) / buy_price
transaction_record.append(
[table, buy_date, sell_date, hold_days, profit])
print(profit)
if self.buy_condition(vol,
vol_data,
close,
last_high,
maxprice,
minprice,
count,
maxindex,
vol_weight=1.2):
buy.append((line, count, table))
print(line)
count += 1
return transaction_record, buy
def filter_with_all_stocks(self, stocklist, source="mongodb"):
error_list = []
result = []
buyresult = []
for i in stocklist:
print(i)
try:
r, buyed = self.histofyreturn(table=i, source=source)
if r: result.extend(r)
if buyed: buyresult.extend(buyed)
except:
error_list.append(i)
return result, buyresult, error_list
def import_data(self,stock, start, end ):
query = MongoDB()
df = query.format2dataframe(query.read_data("ml_fund_table",stock))
df['change'] = (df['close'] - df['close'].shift(1))/df['close'].shift(1)
df['code'] = stock
return df
class basestrategy(object):
'''
trading volume is the lowest in 60 days
'''
def __init__(self, startdate=(2011, 1, 1), enddate=[]):
self.startdate = datetime.datetime(*startdate, 0, 0, 0, 0)
self.enddate = enddate
self.m = MongoDB(DB_SERVER, DB_PORT, USER, PWD, AUTHDBNAME)
self.formatlist = [
"date", "volume", "close", "high", "low", "open", "pre_close"
]
self.savevols = [
"stock", "buy_date", "sell_date", "holddays", "profit", "features"
]
self.looplist = []
self.trading_records = []
self.holding_records = []
self.datalst = []
self.collection = None
self.tempstatus = []
self.lateststatus = []
def setlooplist(self, lst=[]):
if not lst:
self.looplist = self.m.getallcollections("ml_security_table")
else:
self.looplist = lst
return self.looplist
def _getdata(self,
collection="600455.SH",
db="ml_security_table",
out=[],
isfilt=True,
filt={}):
if not out: out = self.formatlist
if isfilt and not filt: filt = {"date": {"$gt": self.startdate}}
query = self.m.read_data(db, collection, filt=filt)
return self.formatquery(query, out)
def formatquery(self, query, out):
'''
query:your source data ,should be a list with dict
out:the fields you want to convert into dataframe
'''
if not out:
query = [i for i in query.sort("date", 1)]
else:
query = [{k: i[k] for k in out} for i in query.sort("date", 1)]
return pd.DataFrame(query)
def buy(self, line, count):
return False
def sell(self, line, count, holding_record):
traderecord = []
return False, traderecord
def setenv(self, collection):
self.collection = collection
data = self._getdata(collection)
self.datalst = [
l for l in data[self.formatlist].fillna(0).values if l[1] != 0
]
self.datalst = self.rehabilitation(self.datalst)
return
def rehabilitation(self, lst):
close = lst[0][2]
c = 1
reh = []
for line in lst[1:]:
c_preclose = line[6]
if c_preclose != close:
reh = [[i[0], i[1] * c_preclose / close] for i in reh]
reh.append([c, c_preclose / close])
close = line[2]
c += 1
result = []
sc = 0
for idx in range(len(reh)):
weight = reh[idx][1]
ec = reh[idx][0]
piece = self.recount(lst, sc, ec, weight)
result.extend(piece)
sc = ec
result.extend(self.recount(lst, ec, -1, 1))
return result
def recount(self, lst, sc, ec, weight):
rst = []
for line in lst[sc:ec]:
rst.append([line[0], line[1], *[i * weight for i in line[2:]]])
return rst
def getfeatures(self, count):
return []
def historyreturn(self, collection):
self.setenv(collection)
trading_record = []
holding_record = []
count = 0
lst = self.datalst
for line in lst[:]:
isbuy = self.buy(lst, count)
for b in holding_record[:]:
issell, traderecord = self.sell(lst, count, b)
if issell:
holding_record.remove(b)
trading_record.append(traderecord)
print(traderecord)
if isbuy:
feature = self.getfeatures(lst, count)
holding_record.append(
([i for i in line], count, collection, feature))
count += 1
# "date","volume","close","high","low","open","pre_close"
holdresult = [[
collection,
self.timestamp2date(i[0][0]), i[0][2], i[3]
] for i in holding_record]
return trading_record, holdresult
def looplist_historyreturn(self):
for collection in self.looplist:
try:
tr, hr = self.historyreturn(collection)
self.lateststatus.append(self.tempstatus)
self.trading_records.extend(tr)
self.holding_records.extend(hr)
except:
print("error: {}".format(collection))
return self.trading_records, self.holding_records
def timestamp2date(self, timestamp):
try:
return str(timestamp).split(" ")[0]
except:
return timestamp
def savetrading2csv(self, filename="trading_records.csv"):
df = pd.DataFrame(self.trading_records, columns=self.savevols)
df.to_csv(filename)
return
def savetrading2db(self,
db="regressiontraderesult",
strategyname="strategytradersult"):
db = eval("self.m.client.{}".format(db))
bulk = db[strategyname].initialize_ordered_bulk_op()
for line in self.trading_records:
bulk.find({'buy_date': line[1]}).upsert().update( \
{'$set': {'stock': line[0], \
'buy_date': line[1], \
'sell_date': line[2], \
'holddays': line[3], \
'profit': line[4], \
'features': line[5], \
}})
bulk.execute()
return
def saveholding2csv(self, filename="holding_records.csv"):
df = pd.DataFrame(self.holding_records,
columns=["stock", "date", "buy_data", "features"])
df.to_csv(filename)
return
