def __init__(self):
self.name = os.path.splitext(os.path.basename(__file__))[0]
# 0 何もしない
# 1 購入
# 2 売却
self.Action = [0, 1, 2]
self.enable_actions = np.arange(3)
self.env = DA.DataAccess()
self.reset()
def __init__(self, listOfStocks):
self.listOfStocks= listOfStocks
self.DAY=86400
dataItemsList=list()
dataItemsList.append("alphaValue")
self.alphaData= DataAccess.DataAccess(False, "AAPLonlybollingerBandsAlphaVals.h5", "/alphaData", "alphaData", True, listOfStocks, None, None, None, dataItemsList)
print "Timestamps are: "
for ts in self.alphaData.timestamps:
print ts
def check_user(user, password):
db = DataAccess()
if len(user) == 0 or len(password) == 0:
return jsonify("Bad request"), 400
result = db.check_user(user, password)
if not result:
return jsonify("Authorization failed"), 401
elif result:
resp = make_response(jsonify(result["guid"]), 200)
return resp
def __init__(self, listOfStocks):
self.listOfStocks = listOfStocks
self.DAY = 86400
dataItemsList = list()
dataItemsList.append("alphaValue")
self.alphaData = DataAccess.DataAccess(
False, "curveFittingAlphaVals_Jan_85_to_2010.h5", "/alphaData",
"alphaData", True, listOfStocks, None, None, None, dataItemsList)
print("Timestamps are: ")
for ts in self.alphaData.timestamps:
print(ts)
def InsBTC_VALUE(self):
res = requests.get('https://coincheck.com/api/ticker')
jsonData = res.json()
# last 直近売買
# bid 買値
# ask 売値
# high 24時間での最高取引価格
# low 24時間での最安取引価格
# volume 24時間での取引量
last = jsonData["last"]
bid = jsonData["bid"]
ask = jsonData["ask"]
high = jsonData["high"]
low = jsonData["low"]
volume = jsonData["volume"]
# 取得したデータを保存用プログラムに投げる
env = da.DataAccess()
env.InsertToBTC_VALUE(last, bid, ask, high, low, volume)
#readStocksFromFile done
#Main begins
noOfDaysToUse = 5
daysAhead = 5
folderList = list()
folderList.append("C:\\tempoutput\\")
listOfStocks = getStocks(folderList)
beginTS = 473490000 #2 Jan 1985 0500 hrs GMT
endTS = 1262235600 #31 DEc 2009 0500 hrs GMT
print "list of stocks is: " + str(listOfStocks)
dataAccess = DataAccess.DataAccess(True, folderList, "/StrategyData",
"StrategyData", True, listOfStocks, beginTS,
endTS)
timestamps = list(dataAccess.getTimestampArray())
print "Printing all timestamps: "
for ts in timestamps:
print ts
print "Printing ts done"
#alpha= alphaGenerator.AlphaDataModel.AlphaDataModelClass()
adm.openFile("curveFittingAlphaVals_Jan_85_to_2010.h5")
daysArray = numpy.zeros((noOfDaysToUse + 1), dtype=float)
ctr = 0
while (ctr <= noOfDaysToUse): #because we get back noOfDaysToUse+1 rows
daysArray[ctr] = ctr
def main():
'''
@summary: Calculates Bollinger bands
'''
folderList = list()
folderList.append("C:\\tempoutput\\")
listOfStocks = list()
# listOfStocks.append("ACY")
listOfStocks.append("AAPL")
#listOfStocks= getStocks(folderList)
dataAccess = DataAccess.DataAccess(True, folderList, "/StrategyData",
"StrategyData", True, listOfStocks)
timestamps = list(dataAccess.getTimestampArray())
adm.openFile("AAPLonlybollingerBandsAlphaVals.h5")
period = 10
stdMultiplier = 2
noOfDays = len(timestamps) #400
centerband = numpy.zeros(
noOfDays, dtype=float
) #len(timestamps)- period + 1 #Just to make it the same length as the adj_close to make it easier to plot
upperBand = numpy.zeros(noOfDays, dtype=float)
lowerBand = numpy.zeros(noOfDays, dtype=float)
x = numpy.zeros(noOfDays, dtype=float)
ctr = 0
while (ctr < noOfDays):
x[ctr] = ctr
ctr += 1
#end while
for stock in listOfStocks:
print "Processing: " + str(stock)
#adj_close= dataAccess.getStockDataList(str(stock), 'adj_close')
adj_close = dataAccess.getStockDataList(stock, 'adj_close',
timestamps[0],
timestamps[noOfDays - 1])
adj_close = removeNaNs(adj_close) #nan's removed, unless all are nans
#Now calculating bollinger bands
for ctr in range(period, noOfDays):
try:
centerband[ctr] = numpy.average(adj_close[ctr - period:ctr])
stdDev = numpy.std(adj_close[ctr - period:ctr])
upperBand[ctr] = centerband[ctr] + (stdMultiplier * stdDev)
lowerBand[ctr] = centerband[ctr] - (stdMultiplier * stdDev)
except IndexError:
print "ctr is: " + str(ctr)
#writing alpha values to file
for ctr in range(0, noOfDays):
if (upperBand[ctr] == lowerBand[ctr]) or (adj_close[ctr]
== centerband[ctr]):
adm.addRow(str(stock), "blah", 0.0, timestamps[ctr])
elif (adj_close[ctr] < centerband[ctr]):
alphaValue = lowerBand[ctr] / adj_close[ctr]
adm.addRow(str(stock), "blah", alphaValue, timestamps[ctr])
else:
alphaValue = -adj_close[ctr] / upperBand[ctr]
adm.addRow(str(stock), "blah", alphaValue, timestamps[ctr])
#done writing alpha values of this stock to file
#calculating bollinger bands done!
# fig = Figure()
# canvas = FigureCanvas(fig)
# ax = fig.add_subplot(111)
# ax.plot(centerband)
# ax.plot (lowerBand)
# ax.plot (upperBand)
# ax.plot (adj_close)
#
# ax.set_title(str(stock)+' Bollinger bands')
# ax.grid(True)
# ax.set_xlabel('time')
# ax.set_ylabel('')
# canvas.print_figure(str(listOfStocks.index(stock)))
#for stock in listOfStocks: done
adm.closeFile()
def ckeck_token():
token = request.headers.get("Authorization")
db = DataAccess()
sessions = db.get_all_sessions()
g.isAdmin = token in sessions
def add_location_type():
ensure_admin()
location_type = json.loads(request.stream.read())
loc_DB = DataAccess()
loc_handler = LocationHandler(loc_DB)
return jsonify(loc_handler.add_location_type(location_type))
def delete_location_type():
ensure_admin()
data = json.loads(request.stream.read())
db = DataAccess()
loc_info_handler = LocationHandler(db)
return jsonify(loc_info_handler.delete_location_type(data))
def get_location_types():
db = DataAccess()
loc_info_handler = LocationHandler(db)
return jsonify(loc_info_handler.get_all_locations_types())
def get_all_event_types():
db = DataAccess()
return jsonify(db.get_all_event_types())
def get_all_image_types():
db = DataAccess()
return jsonify(db.get_all_image_types())
logger.addHandler(sh)
formatter = logging.Formatter(
'%(asctime)s:%(lineno)d:%(levelname)s:%(message)s')
fh.setFormatter(formatter)
sh.setFormatter(formatter)
if __name__ == "__main__":
# トレーニング回数
n_epochs = 10000
# 受け渡しデータ数
DataNum = 14
# トレーニングデータ数（現在未使用）
# TestDataNum = 200
pgenv = pg.PlayGround()
daenv = da.DataAccess()
data = daenv.SelectBTC_VALUE()
agent = DQNAgent(pgenv.enable_actions, pgenv.name, DataNum)
averagelist = [10000]
# 決められた回数実行する
for e in range(n_epochs):
# トレーニングデータの作成
daenv.TrainDataSet()
# 初期化
pgenv.reset()
i = 0
count = 0
length = len(data)
# 初期金額を保存
def getRandPort( lNum, dtStart=None, dtEnd=None, lsStocks=None,\
dmPrice=None, dmVolume=None, bFilter=True, fNonNan=0.95,\
fPriceVolume=100*1000, lSeed=None ):
"""
@summary Returns a random portfolio based on certain criteria.
@param lNum: Number of stocks to be included
@param dtStart: Start date for portfolio
@param dtEnd: End date for portfolio
@param lsStocks: Optional list of ticker symbols, if not provided all symbols will be used
@param bFilter: If False, stocks are not filtered by price or volume data, simply return random Portfolio.
@param dmPrice: Optional price data, if not provided, data access will be queried
@param dmVolume: Optional volume data, if not provided, data access will be queried
@param fNonNan: Optional non-nan percent for filter, default is .95
@param fPriceVolume: Optional price*volume for filter, default is 100,000
@warning: Does not work for all sets of optional inputs, e.g. if you don't include dtStart, dtEnd, you need
to include dmPrice/dmVolume
@return list of stocks which meet the criteria
"""
if (lsStocks is None):
if (dmPrice is None and dmVolume is None):
norObj = da.DataAccess('Norgate')
lsStocks = norObj.get_all_symbols()
elif (not dmPrice is None):
lsStocks = list(dmPrice.columns)
else:
lsStocks = list(dmVolume.columns)
if (dmPrice is None and dmVolume is None and bFilter == True):
norObj = da.DataAccess('Norgate')
ldtTimestamps = du.getNYSEdays(dtStart, dtEnd, dt.timedelta(hours=16))
# if dmPrice and dmVol are provided then we don't query it every time """
bPullPrice = False
bPullVol = False
if (dmPrice is None):
bPullPrice = True
if (dmVolume is None):
bPullVol = True
# Default seed (none) uses system clock """
rand.seed(lSeed)
lsRetStocks = []
# Loop until we have enough randomly selected stocks """
llRemainingIndexes = list(range(0, len(lsStocks)))
lsValid = None
while (len(lsRetStocks) != lNum):
lsCheckStocks = []
for i in range(lNum - len(lsRetStocks)):
lRemaining = len(llRemainingIndexes)
if (lRemaining == 0):
print('Error in getRandPort: ran out of stocks')
return lsRetStocks
# Pick a stock and remove it from the list of remaining stocks """
lPicked = rand.randint(0, lRemaining - 1)
lsCheckStocks.append(lsStocks[llRemainingIndexes.pop(lPicked)])
# If bFilter is false"""
# simply return our first list of stocks, don't check prive/vol """
if (not bFilter):
return sorted(lsCheckStocks)
# Get data if needed """
if (bPullPrice):
dmPrice = norObj.get_data(ldtTimestamps, lsCheckStocks, 'close')
# Get data if needed """
if (bPullVol):
dmVolume = norObj.get_data(ldtTimestamps, lsCheckStocks, 'volume')
# Only query this once if data is provided"""
# else query every time with new data """
if (lsValid is None or bPullVol or bPullPrice):
lsValid = stockFilter(dmPrice, dmVolume, fNonNan, fPriceVolume)
for sAdd in lsValid:
if sAdd in lsCheckStocks:
lsRetStocks.append(sAdd)
return sorted(lsRetStocks)
