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oandaData.py
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oandaData.py
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from pyoanda import Client, TRADE
from oandapy import oandapy
import pandas,time,datetime,math
import sqlite3,os,threadpool
import indicator,numpy
import json
folder='ini'
instruments='Instrument.txt'
granularity=['M15','H1','H4','D','W','M']
savePath=open('ini/OandaSavePath.txt').read()
Clients=json.load(open('ini/OandaClient.json'))
opClient=None
defautClient=None
def createOpClient(opClient=opClient):
if opClient is not None:
return opClient
opClient=oandapy.API(**Clients['oandapy'])
return opClient
def creatDefaultClient(client=defautClient):
if client is not None:
return client
client=Client(**Clients['pyoanda'])
return client
def getInstrumentHistory(instrument,candle_format="bidask",granularity='D', count=None,
daily_alignment=0, alignment_timezone='Etc/UTC',
weekly_alignment="Monday", start=None,end=None,client=defautClient,recursion=False,con=None):
'''
:param instrument:
:param candle_format:
:param granularity:
:param count:
:param daily_alignment:
:param alignment_timezone:
:param weekly_alignment:
:param start:
:param end:
:param client:
See more:
http://developer.oanda.com/rest-live/rates/#retrieveInstrumentHistory
:param recursion: ignore this
:return: DataFrame()
'''
client=creatDefaultClient(client)
pdata=None
print(start)
if recursion:
data=client.get_instrument_history(
instrument=instrument,
candle_format=candle_format,
granularity=granularity,
count=count,
daily_alignment=daily_alignment,
alignment_timezone=alignment_timezone,
weekly_alignment=weekly_alignment,
start=start,
end=end
)
pdata=pandas.DataFrame(data['candles'])
else:
try:
data=client.get_instrument_history(
instrument=instrument,
candle_format=candle_format,
granularity=granularity,
count=count,
daily_alignment=daily_alignment,
alignment_timezone=alignment_timezone,
weekly_alignment=weekly_alignment,
start=start,
end=end
)
pdata=pandas.DataFrame(data['candles'])
except Exception as e:
print('Error:93',e)
# 如果所需candle数 > 5000 则从start开始每5000根获取一次并合并数据,recursion=True
if '5000' in str(e):
data=getInstrumentHistory(instrument,candle_format=candle_format,granularity=granularity, count=5000,
daily_alignment=daily_alignment, alignment_timezone=alignment_timezone,
weekly_alignment=weekly_alignment, start=start,end=None,client=client,recursion=True)
if con is not None:
save_sql(data,granularity,con=con)
return 0
return(data)
else : return 0
timeSting=[]
# 修改时间格式
for i in pdata.index:
t=pdata.get_value(i,'time')
tSting=t.split('.')[0]
timeSting.append(tSting)
ti=time.strptime(tSting,'%Y-%m-%dT%H:%M:%S')
pdata.set_value(i,'time',time.mktime(ti))
pdata.set_index('time',inplace=True)
pdata.drop('complete',1,inplace=True)
column=pdata.columns.tolist()
for i in pdata.index:
for c in column:
v=pdata.get_value(i,c)
try:
pdata.set_value(i,c,float(v))
except Exception as e:
print(e)
pdata.insert(0,'Date',timeSting)
if recursion:
# 如果递归且当前数据=5000组 说明没有读取到结束位置的数据 则继续递归 由当前数据的最后一个作为start输入
if len(pdata.index)==5000:
oldendtime=pdata.index.tolist()[-1]
startTime=datetime.datetime.fromtimestamp(oldendtime).strftime("%Y-%m-%dT%H:%M:%S.%f%z")
new = getInstrumentHistory(instrument,candle_format=candle_format,granularity=granularity, count=5000,
daily_alignment=daily_alignment, alignment_timezone=alignment_timezone,
weekly_alignment=weekly_alignment, start=startTime,end=end,client=client,recursion=True)
return pdata.append(new)
if con is not None:
save_sql(pdata,granularity,con=con)
return 0
return (pdata)
def update(*granularity,dbpath=None,instrument=None,con=None):
'''
update candle chart
:param dbpath: address to save
:param granularity: 'M15','H1','H4','D','W','M'
:param instrument: instrument to get
If dbpath is named by instrument itself, instrument can be None
:return: None
'''
error=[]
close=False
if dbpath is None:
dbpath='%s/%s.db' % (savePath,instrument)
if con is None:
con=sqlite3.connect(dbpath)
close=True
if instrument is None:
instrument=Split(dbpath,'/','.')[-2]
if len(granularity)==0:
# granularity=con.execute('''select name from sqlite_master where type='table' ''').fetchall()
granularity=['M15','H1','H4','D','W','M']
for g in granularity:
print (g)
try:
lastRecord=con.execute('''SELECT * FROM "%s" ORDER BY rowid DESC ''' % g).fetchone()
start=datetime.datetime.fromtimestamp(lastRecord[0])
startTime=start.strftime("%Y-%m-%dT%H:%M:%S.%f%z")
endTime=datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f%z")
new=getInstrumentHistory(instrument,granularity=g,start=startTime,end=endTime)
new[new.index>lastRecord[0]].to_sql(g,con,if_exists='append')
except Exception as e:
print('error_181:',e)
error.append(g)
if close:
con.close()
return error
def Split(word,*seps,outType='str'):
if len(seps)>1:
w=word.split(seps[0])
s=[]
for i in w:
s.extend(Split(i,*seps[1:]))
if outType is 'int':
for i in range(0,len(s)):
s[i]=int(s[i])
return(s)
elif len(seps)==1:
s=word.split(seps[0])
while '' in s:
s.remove('')
return(s)
# else: return []
def read_sql(table,code=None,dbpath=None,con=None):
'''
only 1 is in need
:param table:
:param code:
:param dbpath:
:param con:
:return:
How to use:
data=read_sql('D',code='EUR_USD')
data=read_sql('D',dbpath='.../.../EUR_USD/.db')
data=read_sql('D',con=sqlite3.connect('.../.../EUR_USD/.db'))
'''
close=False
if dbpath is None:
dbpath='%s/%s.db' % (savePath,code)
if con is None:
con=sqlite3.connect(dbpath)
close=True
data=pandas.read_sql('''select * from %s''' % table,con)
if close:
con.close()
return data
def save_sql(data,table,dbpath=None,con=None,if_exists='replace'):
'''
:param data:
:param table:
:param dbpath:
:param con:
:param if_exists:
:return:
How to use:
data=getInstrumentHistory(.....)
save_sql(data,'D',code='EUR_USD')
save_sql(data,'D',dbpath='.../.../EUR_USD.db')
save_sql(data,'D',con=sqlite3.connect('.../.../EUR_USD.db'))
'''
close=False
if con is None:
con=sqlite3.connect(dbpath)
close=True
data.to_sql(table,con,if_exists=if_exists)
print('%s saved' % table)
if close:
print(dbpath)
con.close()
def readInsts():
'''
:return: list of instruments : ['EUR_USD','GBP_USD',...]
'''
path='%s/%s' % (folder,instruments)
file=open(path)
lines=file.readlines()
out=[]
for l in lines:
out.extend(Split(l,',','\n'))
file.close()
return out
def getCommitmentsOfTraders(instrument,client=opClient,start=None,con=None):
'''
:param instrument:
Required Name of the instrument to retrieve Commitments of Traders data for.
Supported instruments: AUD_USD, GBP_USD, USD_CAD, EUR_USD, USD_JPY,
USD_MXN, NZD_USD, USD_CHF, XAU_USD, XAG_USD.
:param client:
:return: Dataframe
'''
client=createOpClient(client)
insts=['AUD_USD', 'GBP_USD', 'USD_CAD', 'EUR_USD', 'USD_JPY',
'USD_MXN', 'NZD_USD', 'USD_CHF', 'XAU_USD', 'XAG_USD']
if instrument not in insts:
print('%s : not supported for Commitments of Traders' % instrument)
return 0
response=client.get_commitments_of_traders(instrument=instrument)
data=pandas.DataFrame(response[instrument])
timelist=[]
longshort=[]
shortlong=[]
publish=[]
diff=[0]
for i in data.index:
value=data.get_value(i,'date')
timelist.append(value)
publish.append(value+4*24*60*60)
ls=float(data.get_value(i,'ncl'))-float(data.get_value(i,'ncs'))
longshort.append(ls)
shortlong.append(-ls)
try:
diff.append(-ls-shortlong[-2])
except :
pass
date=datetime.datetime.fromtimestamp(value)
data.set_value(i,'date',date)
data.insert(0,'time',timelist)
data.insert(0,'publish',publish)
data.insert(5,'l-s',longshort)
data.insert(6,'s-l',shortlong)
data.insert(7,'s-l_diff',diff)
if start is not None:
return data[data.time>=start].set_index('time')
data.set_index('time',inplace=True)
if con is not None:
save_sql(data,'COT',con=con)
return 0
return data
def getHistoricalPositionRatios(instrument,period=31536000,client=opClient,start=None,con=None):
'''
:param instrument:
Required Name of the instrument to retrieve historical position ratios for.
Supported instruments: AUD_JPY, AUD_USD, EUR_AUD, EUR_CHF, EUR_GBP, EUR_JPY,
EUR_USD, GBP_CHF, GBP_JPY, GBP_USD, NZD_USD, USD_CAD,
USD_CHF, USD_JPY, XAU_USD, XAG_USD.
:param period:
Period of time in seconds to retrieve calendar data for.
Values not in the following list will be automatically adjusted to the nearest valid value.
86400 - 1 day - 20 minute snapshots
172800 - 2 day - 20 minute snapshots
604800 - 1 week - 1 hour snapshots
2592000 - 1 month - 3 hour snapshots
7776000 - 3 months - 3 hour snapshots
15552000 - 6 months - 3 hour snapshots
31536000 - 1 year - daily snapshots
:param client:
:return: DataFrame()
'''
client=createOpClient(client)
insList=['AUD_JPY', 'AUD_USD', 'EUR_AUD', 'EUR_CHF', 'EUR_GBP', 'EUR_JPY','USD_CHF', 'USD_JPY',
'EUR_USD', 'GBP_CHF', 'GBP_JPY', 'GBP_USD', 'NZD_USD', 'USD_CAD', 'XAU_USD', 'XAG_USD']
if instrument not in insList:
print('%s : not supported for Historical Position Ratio' % instrument)
return 0
response = client.get_historical_position_ratios(instrument=instrument,period=period)
data=pandas.DataFrame(response['data'][instrument]['data'],columns=['time','long_position_ratio','exchange_rate'])
if start is not None:
data=data[data.time>start]
timeList=[]
for t in data['time']:
d=datetime.datetime.fromtimestamp(t)
timeList.append(datetime.datetime.fromtimestamp(t))
spr=[]
position=[]
posdiff=[0]
for l in data['long_position_ratio']:
spr.append(100-l)
position.append(spr[-1]-l)
try:
posdiff.append(position[-2]-position[-1])
except:
pass
data.insert(1,'datetime',timeList)
data.insert(3,'short_position_ratio',spr)
data.insert(4,'position',position)
data.insert(5,'position_diff',posdiff)
if con is not None:
save_sql(data.set_index('time'),'HPR',con=con)
return 0
return data.set_index('time')
def getCalendar(instrument,period=31536000,client=opClient):
client=createOpClient(client)
response = client.get_eco_calendar(instrument=instrument,period=period)
calendar=pandas.DataFrame(response)
columns=calendar.columns.tolist()
columns[columns.index('timestamp')]='time'
calendar.columns=columns
dateList=[]
for i in calendar.index:
value=calendar.get_value(i,'time')
dateList.append(datetime.datetime.fromtimestamp(value))
calendar.insert(0,'datetime',dateList)
return calendar.set_index('time')
def createFactorsTable(instrument=None,dbpath=None,con=None,**factors):
'''
Only have to input on of these 3:
:param instrument:
:param dbpath:
:param con:
:param factors: No use temporarily
:return:
How to use:
createFactorsTable(instrument='EUR_USD')
-------------------------------------------------------------
createFactorsTable(dbpath='E:/FinanceData/Oanda/EUR_USD.db')
-------------------------------------------------------------
con=sqlite3.connect('E:/FinanceData/Oanda/EUR_USD.db')
createFactorsTable(con=con)
con.close()
'''
close=False
if len(factors)==0:
factors={
'D':['time','closeBid','highBid','lowBid'],
'COT':['publish','s-l','s-l_diff'],
'HPR':['time','position','position_diff']
}
if dbpath is None:
dbpath='%s/%s.db' % (savePath,instrument)
print(dbpath)
if con is None:
con=sqlite3.connect(dbpath)
close=True
data={}
start=0
for k in factors.keys():
f=factors[k]
data[k]=read_sql(k,con=con).get(f)
startTime=data[k].get_value(0,f[0])
if start<startTime:
start=startTime
data['COT'].columns=['time','s-l','s-l_diff']
if close:
con.close()
price=data['D']
momentum=indicator.momentum(price['time'],price['closeBid'],period=60)
data['momentumfast']=momentum
momentum=indicator.momentum(price['time'],price['closeBid'],period=130)
data['momentumslow']=momentum
data['atr']=indicator.ATR(price['time'],price['highBid'],price['lowBid'],price['closeBid'],period=10)
data['mafast']=indicator.MA(price['time'],price['closeBid'],period=60,compare=True)
data['maslow']=indicator.MA(price['time'],price['closeBid'],period=130,compare=True)
adx=indicator.ADX(price['time'],price['highBid'],price['lowBid'],price['closeBid'],period=10)
data['ADX']=adx
data['RSI']=indicator.RSI(price['time'],price['closeBid'],period=10)
data['MACD']=indicator.MACD(price['time'],price['closeBid'],out=['hist'])
histdiff=[0]
for h in data['MACD'].index.tolist()[1:]:
histdiff.append(data['MACD'].get_value(h,'hist')-data['MACD'].get_value(h-1,'hist'))
data['MACD'].insert(2,'hist_diff',histdiff)
data['ADX-mom']=indicator.momentum(adx['time'],adx['ADX%s' % 10],period=5)
data['ADX-mom'].columns=['time','ADX-mom']
data.pop('D')
out=None
for k in sorted(data.keys()):
# if k == 'D':
# continue
v=data[k]
select=v[v.time>=start]
for i in select.index:
t=select.get_value(i,'time')
select.set_value(i,'time',datetime.date.fromtimestamp(t))
# print(select)
if out is None:
out=select.drop_duplicates('time').set_index('time')
else:
out=out.join(select.drop_duplicates('time').set_index('time'),how='outer')
for c in out.columns:
former=out.get_value(out.index.tolist()[0],c)
for t in out.index.tolist()[1:]:
v=out.get_value(t,c)
# print(t,c,v,type(v))
if math.isnan(v):
out.set_value(t,c,former)
former=out.get_value(t,c)
return out.dropna()
def changeData(table,instrument=None,dbpath=None):
'''
Do not run this function!!!
It's only for changing static data temporarily
:param table:
:param instrument:
:param dbpath:
:return:
'''
if dbpath is None:
dbpath='%s/%s.db' % (savePath,instrument)
con=sqlite3.connect(dbpath)
try:
data=read_sql(table,con=con)
data.drop_duplicates('time').set_index('time').to_sql(table,con,if_exists='replace')
except Exception as e:
print(e)
con.close()
return 0
# save_sql(data.set_index('time'),table,con=con)
con.close()
def factorToScore(insts):
'''
:param insts: list of instruments:['EUR_USD','USD_CAD',....]
:return: print(pandas.DataFrame(scoretable of all instruments in insts))
'''
score=[]
for i in insts:
data=read_sql('Factors',i)
index=data.index.tolist()[-1]
out=data[data.index==index]
sldiff=0
if out.get_value(index,'s-l_diff')<-1000:
sldiff=-1
elif out.get_value(index,'s-l_diff')>1000:
sldiff=1
posdiff=0
if out.get_value(index,'position_diff')<-0.5:
posdiff=-1
elif out.get_value(index,'position_diff')>0.5:
posdiff=1
hist=0
if out.get_value(index,'hist')>0 and out.get_value(index,'hist_diff')<0:
hist=-1
elif out.get_value(index,'hist')<0 and out.get_value(index,'hist_diff')>0:
hist=1
MA=0
if out.get_value(index,'C-MA60')>out.get_value(index,'C-MA130'):
if out.get_value(index,'C-MA60')<0 and out.get_value(index,'C-MA130')<0:
MA=-1
elif out.get_value(index,'C-MA60')<out.get_value(index,'C-MA130'):
if out.get_value(index,'C-MA60')>0 and out.get_value(index,'C-MA130')>0:
MA=1
mom=0
sm=out.get_value(index,'momentum60')
lm=out.get_value(index,'momentum130')
if lm<0 and sm>0:
mom=-1
if lm>0 and sm<0 and (-sm)>lm:
mom=-1
if lm>0 and sm<0:
mom=1
if lm<0 and sm>0 and sm>(-lm):
mom=1
rsi='Central area'
if out.get_value(index,'RSI10')<40:
rsi='Oversold'
elif out.get_value(index,'RSI10')>60:
rsi='Overbougut'
adx='Consolidate'
ADX=out.get_value(index,'ADX10')
ADXm=out.get_value(index,'ADX-mom')
if ADX>25 :
if ADXm>100:
adx='Trend'
else:
adx='Counter Trend'
atr=out.get_value(index,'ATR10')
score.append([i,sldiff,posdiff,hist,MA,mom,rsi,adx,atr*0.35,sldiff+posdiff+hist+MA++mom])
# print(score)
data=pandas.DataFrame(score,columns=[
'Symbol','S-L_diff','Position_diff','hist & hist_diff','C-MA60 && C-MA130',
'Momentum 60 & Momentum 130','RSI','ADX & ADX_Momentum','ATR:ATR*0.35','score'])
print(data)
def updateCOT(instrument,dbpath=None,con=None):
close=False
if dbpath is None:
dbpath='%s/%s.db' % (savePath,instrument)
if con is None:
con=sqlite3.connect(dbpath)
close=True
try:
last=read_sql('COT',con=con)
new=getCommitmentsOfTraders(instrument,start=last['publish'].tolist()[-1])
new=last.append(new)
save_sql(new,'COT',con=con)
except:
data=getCommitmentsOfTraders(instrument)
save_sql(data,'COT',con=con)
if close:
con.close()
def updateHPR(instrument,dbpath=None,con=None):
insList=['AUD_JPY', 'AUD_USD', 'EUR_AUD', 'EUR_CHF', 'EUR_GBP', 'EUR_JPY','USD_CHF', 'USD_JPY',
'EUR_USD', 'GBP_CHF', 'GBP_JPY', 'GBP_USD', 'NZD_USD', 'USD_CAD', 'XAU_USD', 'XAG_USD']
if instrument not in insList:
return 0
close=False
if dbpath is None:
dbpath='%s/%s.db' % (savePath,instrument)
if con is None:
con=sqlite3.connect(dbpath)
close=True
try:
last=read_sql('HPR',con=con)
new=getHistoricalPositionRatios(instrument,start=last['time'].tolist()[-1])
new=last.drop(last.index.tolist()[-1]).set_index('time').append(new)
save_sql(new,'HPR',con=con)
except:
data=getHistoricalPositionRatios(instrument)
save_sql(data,'HPR',con=con)
if close:
con.close()
def importNewInstrument(instrument,*granularity,path=savePath,con=None):
'''
:param instrument: 'EUR_USD', 'AUD_USD' ...
:param granularity: 'M15','H1','H4','D','W','M'
If import nothing: granularity=['M15','H1','H4','D','W','M']
:param path: default to savepath oanda
:param con: None
Will be created by path and instrument
:return:
Automatically save history data and COT and HPR data of a new instrument
witch does not have any data in path 'oanda'
'''
if len(granularity) ==0:
granularity=['M15','H1','H4','D','W','M']
dbpath='%s/%s.db' % (path,instrument)
print(dbpath)
if os.path.exists(dbpath):
print(instrument,' data already exists \nPlease try update(), updateCOT() or updateHPR()')
return
try:
con=sqlite3.connect(dbpath)
except :
os.makedirs(path,exist_ok=True)
time.sleep(1)
con=sqlite3.connect(dbpath)
start=datetime.date(1971,1,1).strftime("%Y-%m-%dT%H:%M:%S.%f%z")
end=datetime.date.today().strftime("%Y-%m-%dT%H:%M:%S.%f%z")
pool=threadpool.ThreadPool(6)
def save(req,out):
kwds=req.kwds
if kwds is not None:
save_sql(out,kwds['granularity'],con=con)
for g in granularity:
wrequest=threadpool.WorkRequest(getInstrumentHistory,
kwds={'instrument':instrument,'granularity':g,
'start':start,'end':end},
callback=save)
pool.putRequest(wrequest)
def saveCOT(req,out):
save_sql(out,'COT',con=con)
pool.putRequest(
threadpool.WorkRequest(getCommitmentsOfTraders,[instrument],callback=saveCOT)
)
def saveHPR(req,out):
save_sql(out,'HPR',con=con)
pool.putRequest(
threadpool.WorkRequest(getHistoricalPositionRatios,[instrument],callback=saveHPR)
)
pool.wait()
factors=createFactorsTable(instrument,con=con)
save_sql(factors,'Factors',con=con)
con.close()
if __name__ == '__main__':
print(getInstrumentHistory('GBP_USD'))
pass