def get_predict():
regr = get_model()
select_feat = [
'open', 'high', 'low', 'close', 'vol', 'amount', 'count', 'oc', 'lh'
]
curkline = HuobiService.get_kline('btcusdt', '1min', 1)
curdata = dict()
#print(int(curkline['ts']))
curdata['time'] = (curkline['ts']) / 1000
#print(curdata['ts'])
curdata['close'] = curkline['data'][0]['close']
testdata = pd.DataFrame(curkline['data'])
testdata['oc'] = (testdata['open'] - testdata['close'])
testdata['lh'] = (testdata['high'] - testdata['low'])
testdata = testdata[select_feat]
# train_data = xgb.DMatrix(train_data)
# print(train_data)
y_pred_xgb = regr.predict(testdata)
y_pred = np.exp(y_pred_xgb)
pred_time = get_nextmin(int(curkline['ts']) / 1000)
predict_data = dict()
predict_data['time'] = pred_time
predict_data['close'] = int(y_pred[0])
#print(predict_data)
return jsonify({'curdata': curdata, 'pred_data': predict_data})
def get_model():
kline_data = HuobiService.get_kline('btcusdt', '1min', 2000)
kline_data = kline_data['data']
train_df = pd.DataFrame(kline_data)
train_df['oc'] = (train_df['open'] - train_df['close'])
train_df['lh'] = (train_df['high'] - train_df['low'])
label = train_df[:-1]['close']
train_df = train_df[1:]
train_df = train_df.reset_index(drop=True)
train_df['label'] = label
select_feat = [
'open', 'high', 'low', 'close', 'vol', 'amount', 'count', 'oc', 'lh'
]
label_df = pd.DataFrame(index=train_df.index, columns=["label"])
label_df["label"] = np.log(train_df["label"])
train_df = train_df[select_feat]
def rmse(y_true, y_pred):
return np.sqrt(mean_squared_error(y_true, y_pred))
regr = xgb.XGBRegressor(colsample_bytree=0.5,
gamma=0.0,
learning_rate=0.01,
max_depth=6,
min_child_weight=1.5,
n_estimators=2200,
reg_alpha=0.9,
reg_lambda=0.6,
subsample=0.8,
seed=2018,
silent=1)
regr.fit(train_df, label_df)
return regr
def addSymbol(needAdd):
logUtil.info("needAdd={}".format(needAdd))
for model in needAdd:
logUtil.info("begin add={}".format(model))
kline = huobi.get_kline(model.symbol, model.period, 1000)
datas = kline['data']
datas.reverse()
for data in datas:
commonUtil.addSymbol(data, model, False)
logUtil.info("end add={}".format(model))
def get_data_to_local(self, path, size=120):
res = HuobiService.get_kline(
self._symbol, self.period_type(self._kline_series._period_type),
size)
print res
kline_file = '%s/huobi_kline_%s.json' % (path, date_tools.cur_time())
fout = open(kline_file, 'w')
fout.write(json.dumps(res))
res = HuobiService.get_depth(self._symbol, 'step0')
print res
depth_file = '%s/huobi_deep_%s.json' % (path, date_tools.cur_time())
fout = open(depth_file, 'w')
fout.write(json.dumps(res))
def getKline():
symbol = request.args.get("symbol", default="btcusdt")
period = request.args.get("period", default="1min")
size = request.args.get("size", type=int, default=300)
ret = {}
data_pre = HuobiService.get_kline(symbol, period, size)
kline = []
for item in data_pre["data"]:
temp = []
time = ts_to_time(item['id'])
temp.append(time)
temp.append(item['open'])
temp.append(item['close'])
temp.append(item['low'])
temp.append(item['high'])
kline.append(temp)
ret['data'] = kline
return jsonify(ret)
def doCheck(self, transactionModels):
for transactionModel in transactionModels:
symbol = transactionModel.symbol
sellOrderModels = modelUtil.getSellOrderModels(symbol, self.env)
if len(sellOrderModels) > 0:
for sellOrderModel in sellOrderModels:
orderStatus = self.chechOrder(sellOrderModel.sellOrderId)
logUtil.info("orderId=", sellOrderModel.sellOrderId,
" status=", orderStatus)
if orderStatus:
buyModel = modelUtil.getBuyModelByOrderId(
sellOrderModel.buyOrderId)
modelUtil.delBuyModel(buyModel.id)
modelUtil.delSellOrderById(sellOrderModel.id)
modelUtil.insertBuySellReocrd(
buyModel.symbol, 0, sellOrderModel.buyPrice,
sellOrderModel.sellPrice,
sellOrderModel.buyOrderId,
sellOrderModel.sellOrderId, sellOrderModel.amount,
int(time.time()))
continue
# 测试环境不会走到这里，因为测试环境能直接卖成功
kline = huobi.get_kline(symbol, transactionModel.period, 1)
if kline['status'] == 'ok' and kline['data'] and len(
kline['data']) >= 1:
kline['data'].reverse()
price = float(kline['data'][0]['close'])
buyPrice = float(sellOrderModel.buyPrice)
if buyPrice > price: # 当前价格比买入的时候低才需要判断要不要撤单
gap = (buyPrice - price) / buyPrice
if gap > 0.05: # 比买入的时候还要低5%就撤单
#{'status': 'ok', 'data': '82495000363'}
result = huobi.cancel_order(
sellOrderModel.sellOrderId)
if result['status'] == 'ok':
modelUtil.delSellOrderById(
sellOrderModel.id)
buyModel = modelUtil.getBuyModelByOrderId(
sellOrderModel.buyOrderId)
buyModel.status = 0
modelUtil.modBuyModel(buyModel)
symbols = huobi.get_symbols()
datas = symbols['data']
symbolsList = []
for data in datas:
quote_currency = data['quote-currency']
if quote_currency == 'usdt':
symbolsList.append(data['symbol'])
amplitudeModelList = []
lineLen = 2000
for symbol in symbolsList:
lastClose = 0
kLine = huobi.get_kline(symbol, '15min', lineLen)
if kLine is None:
continue
print(symbol)
if kLine['status'] == 'ok' and kLine['data'] and len(
kLine['data']) >= lineLen:
kLine['data'].reverse()
else:
continue
amplitudeList = []
for data in kLine['data']:
pairList.append(pairs)
tranPairsList = []
count = 0
for symbolPair in pairList:
try:
first = symbolPair[0]
second = symbolPair[1]
# if count == 5:
# break
# count = count + 1
#1min, 5min, 15min, 30min, 60min, 1day, 1mon, 1week, 1year
firstLine = huobi.get_kline(first, '15min', 2000)
secondLine = huobi.get_kline(second, '15min', 2000)
# print(firstLine,first)
# print(secondLine,second)
if firstLine is None or secondLine is None:
continue
if firstLine['status'] == 'ok' and secondLine[
'status'] == 'ok' and firstLine['data'] and secondLine[
'data']:
firstLine['data'].reverse()
secondLine['data'].reverse()
import HuobiService as huobi
if __name__ == '__main__':
# A EOS B HT C USDT
firstLine = huobi.get_kline('htusdt', '1min', 2000)
secondLine = huobi.get_kline('eosusdt', '1min', 2000)
threeLine = huobi.get_kline('eosht', '1min', 2000)
firstData = firstLine['data']
secondData = secondLine['data']
threeData = threeLine['data']
firstData.reverse()
secondData.reverse()
threeData.reverse()
usdt = 0
ht = 5
eos = 5
balance = 0
buyAmount = 50
fei = 0
for i in range(0, 2000):
p1 = float(firstData[i]['close'])
p2 = float(secondData[i]['close'])
p3 = float(threeData[i]['close'])
if (p2 / p1 - p3) > (p3 * 0.003):
eos += buyAmount # 买入 EOS
global lastBuy
global buynum
isSend = False
if buy== 0:
if (D<K and lastK<lastD or J>100 ) :
isSend = True
if J < lastJ and J>50 and J>K:
isSend = True
return isSend
if __name__ == '__main__':
test = huobi.get_kline('eosusdt','1min',1000)
test['data'].reverse()
for i in test['data']:
get_KDJ(i,test['data'].index(i),'init')
lastId = 0
count = 0
lastDate = huobi.get_kline('eosusdt','1min',1)
while True:
try:
test = huobi.get_kline('eosusdt','1min',1)
test['data'].reverse()
logging.info(test['data'])
cash = pd.Series(cash, index=position.index)
shareY = pd.Series(shareY, index=position.index)
shareX = pd.Series(shareX, index=position.index)
asset = cash + shareY * priceY + shareX * priceX
account = pd.DataFrame({
'Position': position,
'ShareY': shareY,
'ShareX': shareX,
'Cash': cash,
'Asset': asset
})
return (account)
if __name__ == '__main__':
firstLine = huobi.get_kline('xlmusdt', '1min', 100)
secondLine = huobi.get_kline('omgusdt', '1min', 100)
firstLine['data'].reverse()
secondLine['data'].reverse()
firstData = []
secondData = []
firstCount = 0
secondCount = 0
balance = 0
score = []
firstClose = 0
secondClose = 0
ratioList = []
b = 0
lastK.append(K)
lastJ.append(J)
lastD.append(D)
if len(lastK)>10:
lastK = lastK[1:]
lastD = lastD[1:]
lastJ = lastJ[1:]
last100.append(data['close'])
last100.sort()
if len(last100) >101:
last100 = last100[1:]
return isBuy
if __name__ == '__main__':
test = huobi.get_kline('eosusdt','1min',100)
test['data'].reverse()
for i in test['data']:
judgeBuy(i,test['data'].index(i))
train_df = train_df[1:]
train_df = train_df.reset_index(drop=True)
train_df['label'] = label
select_feat = [
'open', 'high', 'low', 'close', 'vol', 'amount', 'count', 'oc', 'lh'
]
label_df = pd.DataFrame(index=train_df.index, columns=["label"])
label_df["label"] = np.log(train_df["label"])
train_df = train_df[select_feat]
def rmse(y_true, y_pred):
return np.sqrt(mean_squared_error(y_true, y_pred))
regr = xgb.XGBRegressor(colsample_bytree=0.5,
gamma=0.0,
learning_rate=0.01,
max_depth=6,
min_child_weight=1.5,
n_estimators=2200,
reg_alpha=0.9,
reg_lambda=0.6,
subsample=0.8,
seed=2018,
silent=1)
regr.fit(train_df, label_df)
return regr
if __name__ == '__main__':
print(HuobiService.get_kline('btcusdt', '1min', 1))
import HuobiService as huobi
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from arch.unitroot import ADF
def zscore(series):
return (series - series.mean()) / np.std(series)
if __name__ == '__main__':
firstLine = huobi.get_kline('ctxcusdt', '15min', 2000)
secondLine = huobi.get_kline('hcusdt', '15min', 2000)
firstLine['data'].reverse()
secondLine['data'].reverse()
firstData = []
secondData = []
firstCount = 0
secondCount = 0
balance = 0
score = []
firstClose = 0
secondClose = 0
ratioList = []
b = 0
c = 0
mu = 0
transactionModels = refresh.getAllPairAndRefresh()
if env != "pro":
checkOrderStatusThread.doCheck(transactionModels)
lastDataDict = commonUtil.lastDataDict
lastIdDict = commonUtil.lastIdDict
try:
for transactionModel in transactionModels:
lastData = lastDataDict.get(transactionModel.symbol, [])
lastId = lastIdDict.get(transactionModel.symbol, 0)
if len(lastData) == 0:
lastData = huobi.get_kline(transactionModel.symbol,
transactionModel.period, 1)
#{'ch': 'market.xrpusdt.kline.1min', 'status': 'ok', 'ts': 1587722033132,
# 'data': [{'open': 0.19629, 'id': 1587721980, 'count': 29, 'amount': 36697.23, 'close': 0.1963, 'vol': 7200.5755178, 'high': 0.1963, 'low': 0.19613}]}
thisData = huobi.get_kline(transactionModel.symbol,
transactionModel.period, 1)
if thisData['status'] == 'ok' and thisData['data'] and len(
thisData['data']) >= 1:
thisData['data'].reverse()
else:
continue
if env == "pro":
logUtil.info(thisData['data'], transactionModel.symbol)
logUtil.kLineData(transactionModel.symbol + "-->" +
import MAUtil as mAUtil
import commonUtil as commonUtil
import BiTradeUtil as biTradeUtil
import fileOperUtil as fileOperUtil
import BollUtil as bollUtil
import AmountUtil as amountUtil
import logUtil
if __name__ == '__main__':
transactionModels = modelUtil.getAllPair()
env = "dev"
lastIndex = 0
for transactionModel in transactionModels:
logUtil.info(transactionModel)
kline = huobi.get_kline(transactionModel.symbol, transactionModel.period, 2000)
datas = kline['data']
datas.reverse()
for data in datas:
commonUtil.addSymbol(data ,transactionModel)
kdjFlag = kDJUtil.judgeBuy(transactionModel.symbol)
rSIFlag = rSIUtil.rsiJudgeBuy(transactionModel.symbol,12)
maFlag = mAUtil.maJudgeBuy(data,transactionModel.symbol)
avgFlag = commonUtil.juideAllGap(data['close'],transactionModel.symbol,transactionModel.tradeGap)
highFlag = commonUtil.juideHighest(data['close'],transactionModel.symbol)
bollFlag = bollUtil.judgeBoll(data['close'],transactionModel.symbol)
lowFlag = commonUtil.juideLowest(data['close'],transactionModel.symbol)
riskFlag = amountUtil.judgeRisk(transactionModel.symbol)
import TradeUtil as TradeUtil
import time
import logging
# 1.初始化日志默认配置
logging.basicConfig(
filename='./my.log', # 日志输出文件
level=logging.INFO, # 日志写入级别
datefmt='%Y-%m-%d %H:%M:%S', # 时间格式
format='%(asctime)s Line:%(lineno)s==>%(message)s') # 日志写入格式
if __name__ == '__main__':
symbols = 'eosusdt'
bi = 'eos'
env = "dev"
test = huobi.get_kline(symbols, '1min', 2000)
test['data'].reverse()
# a = test['data'][:1000]
# b = test['data'][1000:]
for data in test['data']:
index = test['data'].index(data)
close = data['close']
priceUtil.add(data, index)
KDJ.calKDJ(data, index)
# for data in a:
# index = a.index(data)
# close = data['close']
import matplotlib.pyplot as plt
import HuobiService as huobi
import pandas as pd
import numpy as np
from arch.unitroot import ADF
def zscore(series):
return (series - series.mean()) / np.std(series)
if __name__ == '__main__':
firstLine = huobi.get_kline('ontusdt', '15min', 2000)
secondLine = huobi.get_kline('btsusdt', '15min', 2000)
firstLine['data'].reverse()
secondLine['data'].reverse()
firstData = []
secondData = []
for index in range(
min(len(firstLine['data']) - 1000,
len(secondLine['data']) - 1000)):
firstData.append(firstLine['data'][index]['close'])
secondData.append(secondLine['data'][index]['close'])
X = pd.Series(firstData, name='X')
Y = pd.Series(secondData, name='Y')
pd.concat([Y], axis=1).plot(figsize=(10, 7))
pd.concat([X], axis=1).plot(figsize=(10, 7))
# (X / Y).plot(figsize=(10, 7))
# -*- coding: utf-8 -*-
import HuobiService as huobi
import spot_api as spot
if __name__ == '__main__':
api_key = 'fe31ff24-fcf3-4160-81e9-69f1d0509dc3'
spotAPI = spot.SpotAPI(api_key, seceret_key, passphrase, True)
test = spotAPI.get_kline('eos-USDT', '', '', 60)
data = test
data.reverse()
print(data)
test2 = huobi.get_kline('eosusdt', '1min', 200)
data2 = test2['data']
data2.reverse()
print(data2)
count = 0
amount = 1
for i in range(0, 200):
close1 = float(data[i][4])
close2 = float(data2[i]['close'])
if close1 > close2:
fei = close1 * 0.0015 + close2 * 0.002
gap = close1 - close2
if gap > fei:
def checkGetKline():
symbol = request.args.get("symbol")
period = request.args.get("period")
size = request.args.get("size", type=int, default=150)
#print symbol,period,size
infos = HuobiService.get_kline(symbol, period, size)
#print infos
klines = []
times = []
buyLimit = []
sellLimit = []
curTime = datetime.datetime.now()
#print curTime
for item in infos["data"]:
tmp = []
tmp.append(item["open"])
tmp.append(item["close"])
tmp.append(item["low"])
tmp.append(item["high"])
buyLimit.append(0)
sellLimit.append(0)
klines.append(tmp)
#times.append(time.strftime("%d %M"),curTime)
times.append(curTime.strftime("%m-%d %H"))
if period == "30min":
curTime -= datetime.timedelta(minutes=1)
elif period == "60min":
curTime -= datetime.timedelta(hours=1)
elif period == "1day":
curTime -= datetime.timedelta(days=1)
#print times
klines.reverse()
times.reverse()
matchresults = getMatchresults()
matchRecords = []
for item in matchresults["data"]:
#print item["created-at"]
timeTmp = time.strftime("%m-%d %H",
time.localtime(item["created-at"] / 1000))
index = times.index(timeTmp)
#print index
if item["type"] == "buy-limit":
buyLimit[index] = item["filled-amount"]
elif item["type"] == "sell-limit":
sellLimit[index] = item["filled-amount"]
tmpItem = []
tmpItem.append(item["type"])
tmpItem.append(
time.strftime("%Y-%m-%d %H:%M:%S",
time.localtime(item["created-at"] / 1000)))
tmpItem.append(item["filled-amount"])
tmpItem.append(item["filled-fees"])
tmpItem.append(item["price"])
matchRecords.append(tmpItem)
return jsonify({
"klines": klines,
"times": times,
"buy-limit": buyLimit,
"sell-limit": sellLimit,
"matchRecords": matchRecords
})
isSend = False
if buy== 0:
if (D<K and lastK<lastD or J>100 ) :
isSend = True
if J < lastJ and J>50 and J>K:
isSend = True
return isSend
if __name__ == '__main__':
fig = plt.figure()
symbols = 'eosusdt'
test = huobi.get_kline(symbols,'1min',2000)
# test = aa.test0
test['data'].reverse()
# test = client.getKline(1200,"eos_usdt")
xmajorLocator = MultipleLocator(100);
klineXY = get_kline_xy(test['data'])
klinex = klineXY[0]
kliney = klineXY[1]
MA60XY = get_MA(test['data'],60)
MA30XY = get_MA(test['data'],30)
MA10XY = get_MA(test['data'],5)
def exposed_get_kline(self, symbol, period, size):
return hb.get_kline(symbol, period, size)