def input_data(tt):
trade_data = pd.DataFrame(columns=[
'time', 'ok0330', 'ok_thisweek', 'bfx_bids_wall', 'bfx_asks_wall',
'bfx_total_bids', 'bfx_total_asks', 'bfx_buy_volumn',
'bfx_sell_volumn', 'bfx_last_price', 'exchange_rate', 'huobi_USDT'
])
for i in range(0, tt):
t = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
ok0330 = float(
mykey.future_ticker('btc_usd', 'quarter')['ticker']['last'])
ok_thisweek = float(
mykey.future_ticker('btc_usd', 'this_week')['ticker']['last'])
bfx_bids_wall = bfx.bfx_books()['bids_wall']
bfx_asks_wall = bfx.bfx_books()['asks_wall']
bfx_total_bids = bfx.bfx_books()['total_bids']
bfx_total_asks = bfx.bfx_books()['total_asks']
bfx_buy_volumn = bfx.bfx_volumn()[0]
bfx_sell_volumn = bfx.bfx_volumn()[1]
bfx_last_price = bfx.bfx_ticker()
exchange_rate = float(mykey.exchange_rate()['rate'])
huobiUSDT = float(huobi_USDT.get_usdt_price())
trade_data.loc[i] = [
t, ok0330, ok_thisweek, bfx_bids_wall, bfx_asks_wall,
bfx_total_bids, bfx_total_asks, bfx_buy_volumn, bfx_sell_volumn,
bfx_last_price, exchange_rate, huobiUSDT
]
print(trade_data)
time.sleep(30)
trade_data.to_csv('btc.csv')
def test_data(tt):
pca=joblib.load('pca.m')
next5=joblib.load('next5.m')
next10=joblib.load('next10.m')
next15=joblib.load('next15.m')
i=0
while i<=tt:
try:
DateTime=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
ok0330=float(mykey.future_ticker('btc_usd','quarter')['ticker']['last'])
ok_thisweek=float(mykey.future_ticker('btc_usd','this_week')['ticker']['last'])
bfx_bids_wall=float(bfx.bfx_books()['bids_wall'])
bfx_asks_wall=float(bfx.bfx_books()['asks_wall'])
bfx_total_bids=float(bfx.bfx_books()['total_bids'])
bfx_total_asks=float(bfx.bfx_books()['total_asks'])
bfx_buy_volumn=float(bfx.bfx_volumn()[0])
bfx_sell_volumn=float(bfx.bfx_volumn()[1])
bfx_last_price=float(bfx.bfx_ticker())
exchange_rate=float(mykey.exchange_rate()['rate'])
huobiUSDT=float(huobi_USDT.get_usdt_price())
#huobi USDT OTC price
news_emotion=float(wallstreet_news.wallstr_news())
#News emotion from wallstreet cn
if i==0:
raw=pd.DataFrame([[DateTime,ok0330,ok_thisweek,bfx_bids_wall,bfx_asks_wall,bfx_total_bids,bfx_total_asks,bfx_buy_volumn,bfx_sell_volumn,bfx_last_price,exchange_rate,huobiUSDT,news_emotion]],columns=['DateTime','ok0330','ok_thisweek','bfx_bids_wall','bfx_asks_wall','bfx_total_bids','bfx_total_asks','bfx_buy_volumn','bfx_sell_volumn','bfx_last_price','exchange_rate','huobi_USDT','news_emotion'])
if (i>0)&(i<=15):
raw2=pd.DataFrame([[DateTime,ok0330,ok_thisweek,bfx_bids_wall,bfx_asks_wall,bfx_total_bids,bfx_total_asks,bfx_buy_volumn,bfx_sell_volumn,bfx_last_price,exchange_rate,huobiUSDT,news_emotion]],index=[i],columns=['DateTime','ok0330','ok_thisweek','bfx_bids_wall','bfx_asks_wall','bfx_total_bids','bfx_total_asks','bfx_buy_volumn','bfx_sell_volumn','bfx_last_price','exchange_rate','huobi_USDT','news_emotion'])
raw=raw.append(raw2)
if i>15:
raw3=pd.DataFrame([[DateTime,ok0330,ok_thisweek,bfx_bids_wall,bfx_asks_wall,bfx_total_bids,bfx_total_asks,bfx_buy_volumn,bfx_sell_volumn,bfx_last_price,exchange_rate,huobiUSDT,news_emotion]],index=[i],columns=['DateTime','ok0330','ok_thisweek','bfx_bids_wall','bfx_asks_wall','bfx_total_bids','bfx_total_asks','bfx_buy_volumn','bfx_sell_volumn','bfx_last_price','exchange_rate','huobi_USDT','news_emotion'])
raw=raw.append(raw3)
raw=raw.drop([i-16])
feature=d_pro(raw)
#Data Preprocessing
PCA_ed_feature=pca.transform(feature)
#PCA Features
next_5=next5.predict(PCA_ed_feature)[15]
next_10=next10.predict(PCA_ed_feature)[15]
next_15=next15.predict(PCA_ed_feature)[15]
if(next_5)+(next_10)+(next_15)>2:
print('buy buy buy')
mykey.future_trade('btc_usd','quarter',amount=10,tradeType=1,matchPrice=1,leverRate=20)
longtime.append(i)
#record the time we long bitcoin
print([next_5,next_10,next_15])
i=i+1
for time in longtime:
if i>time+15:
mykey.future_trade('btc_usd','quarter',amount=10,tradeType=3,matchPrice=1,leverRate=20)
longtime.remove(time)
#sell the future long ticket
print(raw)
time.sleep(60)
except:
print('connect error')
return raw
def test_data(tt):
pca=joblib.load('pca.m')
next5=joblib.load('next5.m')
next10=joblib.load('next10.m')
next15=joblib.load('next15.m')
i=0
while i<=tt:
try:
DateTime=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
ok0330=float(mykey.future_ticker('btc_usd','quarter')['ticker']['last'])
ok_thisweek=float(mykey.future_ticker('btc_usd','this_week')['ticker']['last'])
bfx_bids_wall=float(bfx.bfx_books()['bids_wall'])
bfx_asks_wall=float(bfx.bfx_books()['asks_wall'])
bfx_total_bids=float(bfx.bfx_books()['total_bids'])
bfx_total_asks=float(bfx.bfx_books()['total_asks'])
bfx_buy_volumn=float(bfx.bfx_volumn()[0])
bfx_sell_volumn=float(bfx.bfx_volumn()[1])
bfx_last_price=float(bfx.bfx_ticker())
exchange_rate=float(mykey.exchange_rate()['rate'])
huobiUSDT=float(huobi_USDT.get_usdt_price())
#huobi USDT OTC price
news_emotion=float(wallstreet_news.wallstr_news())
#News emotion from wallstreet cn
if i==0:
raw=pd.DataFrame([[DateTime,ok0330,ok_thisweek,bfx_bids_wall,bfx_asks_wall,bfx_total_bids,bfx_total_asks,bfx_buy_volumn,bfx_sell_volumn,bfx_last_price,exchange_rate,huobiUSDT,news_emotion]],columns=['DateTime','ok0330','ok_thisweek','bfx_bids_wall','bfx_asks_wall','bfx_total_bids','bfx_total_asks','bfx_buy_volumn','bfx_sell_volumn','bfx_last_price','exchange_rate','huobi_USDT','news_emotion'])
if (i>0)&(i<=15):
raw2=pd.DataFrame([[DateTime,ok0330,ok_thisweek,bfx_bids_wall,bfx_asks_wall,bfx_total_bids,bfx_total_asks,bfx_buy_volumn,bfx_sell_volumn,bfx_last_price,exchange_rate,huobiUSDT,news_emotion]],index=[i],columns=['DateTime','ok0330','ok_thisweek','bfx_bids_wall','bfx_asks_wall','bfx_total_bids','bfx_total_asks','bfx_buy_volumn','bfx_sell_volumn','bfx_last_price','exchange_rate','huobi_USDT','news_emotion'])
raw=raw.append(raw2)
if i>15:
raw3=pd.DataFrame([[DateTime,ok0330,ok_thisweek,bfx_bids_wall,bfx_asks_wall,bfx_total_bids,bfx_total_asks,bfx_buy_volumn,bfx_sell_volumn,bfx_last_price,exchange_rate,huobiUSDT,news_emotion]],index=[i],columns=['DateTime','ok0330','ok_thisweek','bfx_bids_wall','bfx_asks_wall','bfx_total_bids','bfx_total_asks','bfx_buy_volumn','bfx_sell_volumn','bfx_last_price','exchange_rate','huobi_USDT','news_emotion'])
raw=raw.append(raw3)
raw=raw.drop([i-16])
feature=d_pro(raw)
#Data Preprocessing
PCA_ed_feature=pca.transform(feature)
#PCA Features
next_5=next5.predict(PCA_ed_feature)[15]
next_10=next10.predict(PCA_ed_feature)[15]
next_15=next15.predict(PCA_ed_feature)[15]
if next_5+next_10+next_15>2:
#input auto-trading code to long
if next_5+next_10+next_15=0:
#input auto-trading code to short
#predict the future bitcoin price(5min/10min/15min)
print([next_5,next_10,next_15])
i=i+1
print(raw)
time.sleep(60)
except:
print('connect error')
return raw
def input_data(tt):
#input the number of rows you want to input
j = 0
for i in range(0, tt):
#count number of rows
try:
ok0330 = float(
mykey.future_ticker('btc_usd', 'quarter')['ticker']['last'])
ok_thisweek = float(
mykey.future_ticker('btc_usd', 'this_week')['ticker']['last'])
bfx_bids_wall = bfx.bfx_books()['bids_wall']
bfx_asks_wall = bfx.bfx_books()['asks_wall']
bfx_total_bids = bfx.bfx_books()['total_bids']
bfx_total_asks = bfx.bfx_books()['total_asks']
bfx_buy_volumn = bfx.bfx_volumn()[0]
bfx_sell_volumn = bfx.bfx_volumn()[1]
bfx_last_price = bfx.bfx_ticker()
exchange_rate = float(mykey.exchange_rate()['rate'])
huobiUSDT = float(huobi_USDT.get_usdt_price())
#get USDT price from huobi.pro
news_emotion = float(wallstreet_news.wallstr_news())
print('test')
print(news_emotion)
#get news emotion from wallstreet news blockchain channel
cursor = con.cursor()
print(cursor)
print('test')
# db_name = 'PythonTestDB'
# create_db_sql = 'CREATE DATABASE ' + db_name
# cursor.execute(create_db_sql)
# if i==0:
# create_table_sql = 'DROP TABLE BitcoinTradeHistory'
# cursor.execute(create_table_sql)
# string = 'CREATE TABLE BitcoinTradeHistory(date DATE, last_price FLOAT ,ok_thisweek FLOAT ,bfx_bids_wall FLOAT ,bfx_asks_wall FLOAT ,bfx_total_bids FLOAT ,bfx_total_asks FLOAT ,bfx_buy_volumn FLOAT ,bfx_sell_volumn FLOAT ,bfx_last_price FLOAT ,exchange_rate FLOAT ,huobiUSDT FLOAT ,news_emotion FLOAT)'
# cursor.execute(string)
cursor.execute(
"insert into BitcoinTradeHistory values(getdate(),{0},{1},{2},{3},{4},{5},{6},{7},{8},{9},{10},{11})"
.format(ok0330, ok_thisweek, bfx_bids_wall, bfx_asks_wall,
bfx_total_bids, bfx_total_asks, bfx_buy_volumn,
bfx_sell_volumn, bfx_last_price, exchange_rate,
huobiUSDT, news_emotion))
print('test')
#insert into SQL Server Database
con.commit()
j = j + 1
print('collected {0} rows'.format(j))
time.sleep(60)
except:
print('connect error')
traceback.print_exc(file=sys.stdout)
print('done')
#connect to SQL Server Database
def input_data(tt):
#input the number of rows you want to input
j=0
for i in range(0,tt):
#count number of rows
try:
ok0330=float(mykey.future_ticker('btc_usd','quarter')['ticker']['last'])
ok_thisweek=float(mykey.future_ticker('btc_usd','this_week')['ticker']['last'])
bfx_bids_wall=bfx.bfx_books()['bids_wall']
bfx_asks_wall=bfx.bfx_books()['asks_wall']
bfx_total_bids=bfx.bfx_books()['total_bids']
bfx_total_asks=bfx.bfx_books()['total_asks']
bfx_buy_volumn=bfx.bfx_volumn()[0]
bfx_sell_volumn=bfx.bfx_volumn()[1]
bfx_last_price=bfx.bfx_ticker()
exchange_rate=float(mykey.exchange_rate()['rate'])
huobiUSDT=float(huobi_USDT.get_usdt_price())
#get USDT price from huobi.pro
news_emotion=float(wallstreet_news.wallstr_news())
#get news emotion from wallstreet news blockchain channel
cursor=con.cursor()
cursor.execute("insert into dbo.BitcoinTradeHistory values(getdate(),{0},{1},{2},{3},{4},{5},{6},{7},{8},{9},{10},{11})".format(ok0330,ok_thisweek,bfx_bids_wall,bfx_asks_wall,bfx_total_bids,bfx_total_asks,bfx_buy_volumn,bfx_sell_volumn,bfx_last_price,exchange_rate,huobiUSDT,news_emotion))
#insert into SQL Server Database
con.commit()
j=j+1
print('collected {0} rows'.format(j))
time.sleep(60)
except:
print('connect error')
print('done')