class macd(object):
'''
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'''
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}}, {"$limit": num}]
buylst = []
currentdata = self.getcurrentdata()
for i in self.looplist:
collection = i
query = self.db.read_data(dbname,
collection,
filt,
is_aggregate=True)
data = [i for i in query]
s_ema = data[0]["ema26"]
f_ema = data[0]["ema12"]
dem = data[0]["dem9"]
macd = data[0]["macd"]
try:
c_close = currentdata["trade"].ix[collection.split(".")[0]]
n_s_ema = (s_ema * (emaslow - 1) + 2 * c_close) / (emaslow + 1)
n_f_ema = (f_ema * (emafast - 1) + 2 * c_close) / (emafast + 1)
n_diff = n_f_ema - n_s_ema
n_dem = (dem * (demday - 1) + 2 * n_diff) / (demday + 1)
n_macd = 2 * (n_diff - n_dem)
if macd * n_macd < 0 and n_macd > 0:
buylst.append(collection)
except:
print("error stock:{}".format(collection))
[print("buylist:{}".format(collection)) for collection in buylst]
return buylst
def save2db(self):
pass
def save2csv(self):
pass
class basestrategy(object):
'''
trading volume is the lowest in 60 days
'''
def __init__(self, startdate=(2011, 1, 1), enddate=[]):
self.startdate = datetime.datetime(*startdate, 0, 0, 0, 0)
self.enddate = enddate
self.m = MongoDB(DB_SERVER, DB_PORT, USER, PWD, AUTHDBNAME)
self.formatlist = [
"date", "volume", "close", "high", "low", "open", "pre_close"
]
self.savevols = [
"stock", "buy_date", "sell_date", "holddays", "profit", "features"
]
self.looplist = []
self.trading_records = []
self.holding_records = []
self.datalst = []
self.collection = None
self.tempstatus = []
self.lateststatus = []
def setlooplist(self, lst=[]):
if not lst:
self.looplist = self.m.getallcollections("ml_security_table")
else:
self.looplist = lst
return self.looplist
def _getdata(self,
collection="600455.SH",
db="ml_security_table",
out=[],
isfilt=True,
filt={}):
if not out: out = self.formatlist
if isfilt and not filt: filt = {"date": {"$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