def buy_byprice(money, price, rate=1):
num = money * rate / price
hb.send_order(num,
symbol='btcusdt',
_type='buy-limit',
price=price,
source='api')
def init_context():
precision_data = houbi.get_symbol_precision(context['base'],
context['quote'])
context['price_precision'] = precision_data['price-precision']
context['amount_precision'] = precision_data['amount-precision']
context['dollars_per_share'] = (1.0 /
(10**context['price_precision'])) * 100
context['symbol'] = context['base'] + context['quote']
# 获取账号的信息
context['base_amount'] = houbi.get_symbol_balance(context['base'])
context['quote_amount'] = houbi.get_symbol_balance(context['quote'])
def handel_data():
if context['sys1']:
size = context['short_in_date']
# 获取最新的市场数据
market_data = houbi.get_kline(context['symbol'], context['period'],
size + 1)['data']
# 获取当前行情数据
price = float(houbi.get_current_price(context['symbol']))
# 计算ATR
atr = cal_atr(market_data)
# 2 判断加仓或止损
if context['hold_flage'] is True and float(
context['base_amount']) > 0: # 先判断是否持仓
temp = add_or_stop(price, float(context['break_price']), atr)
if temp == 1: # 判断加仓
if int(context['add_time']) < int(
context['unit_limit']): # 判断加仓次数是否超过上限
print(f"产生加仓信号,加仓价格是：{price}")
buy(atr, price)
context['add_time'] += 1
context['break_price'] = price
else:
print("加仓次数已经达到上限，不会加仓")
elif temp == -1: # 判断止损
# 卖出止损
print("产生止损信号")
sell(price)
else:
# 根据当前价格判断入场还是出场
in_out_flage = in_out(price, market_data, size)
if in_out_flage == 1: # 入场
if context['hold_flage'] is False and float(
context['quote_amount']) > 0:
# 有买入信号，执行买入
print(f"产生入场信号, 突破价格是{price}")
buy(atr, price)
context['add_time'] = 1
context['hold_flage'] = True
context['break_price'] = price
else:
print("已经入场，不产生入场信号")
elif in_out_flage == -1: # 离场
if context['hold_flage'] is True:
if float(context['base_amount']) > 0:
print("产生离场信号")
sell(price)
else:
print("尚未入场或已经离场，不产生离场信号")
def depth_main(num_str):
q = huobi_py.get_depth(num_str.split(' ')[0], num_str.split(' ')[1])
bids = [i[0] * i[1] for i in q['tick']['bids']]
asks = [i[0] * i[1] for i in q['tick']['asks']]
return (float(np.sum(bids) - np.sum(asks)),
1.0 * (np.sum(bids) - np.sum(asks)) /
np.sum(np.sum(bids) + np.sum(asks)))
def Signal2(coin_symbol,para_min,para_up,para_low,show_len):
resu_temp = hb.get_kline(coin_symbol, para_min,show_len)
up_limit = para_up
low_limit = para_low
cur_kline_ts = resu_temp['data'][0]['id']
date_seq = [time.strftime("%Y-%m-%d-%H-%M", time.localtime(int(x['id']))) for x in resu_temp['data']][::-1]
amount_temp = [x['vol'] for x in resu_temp['data']]
amount = np.array(amount_temp[::-1])
vol_temp = [x['amount'] for x in resu_temp['data']]
vol = np.array(vol_temp[::-1])
# close_temp = [x['close'] for x in resu_temp['data']][1:]
# close = np.array(close_temp[::-1])
price_avg_long = float(amount.sum() / vol.sum())
price_avg_short = float(amount[-1] / vol[-1])
action_flag = 0
delt = price_avg_short - price_avg_long
if price_avg_short - price_avg_long > para_up:
action_flag = -1
elif price_avg_short - price_avg_long < para_low:
action_flag = 1
return action_flag,cur_kline_ts,up_limit,low_limit,delt
def Acct_Info():
resu = hb.get_balance(378)
if resu['status'] == 'error':
print('Acct_Info Errrrrr')
return -1
else:
usdt_trade = []
usdt_all = []
btc_trade = []
btc_all = []
eos_trade = []
eos_all = []
resu_data = resu['data']['list']
for i in range(len(resu_data)):
if resu_data[i]['currency'] == 'usdt':
usdt_all.append(float(resu_data[i]['balance']))
if resu_data[i]['type'] == 'trade':
usdt_trade.append(float(resu_data[i]['balance']))
elif resu_data[i]['currency'] == 'btc':
btc_all.append(float(resu_data[i]['balance']))
if resu_data[i]['type'] == 'trade':
btc_trade.append(float(resu_data[i]['balance']))
elif resu_data[i]['currency'] == 'eos':
eos_all.append(float(resu_data[i]['balance']))
if resu_data[i]['type'] == 'trade':
eos_trade.append(float(resu_data[i]['balance']))
ans_usdt = float(np.array(usdt_trade).sum())
ans_usdt_all = float(np.array(usdt_all).sum())
ans_btc = float(math.floor(np.array(btc_all).sum() * 1000) / 1000)
ans_btc_trade = float(math.floor(np.array(btc_trade).sum() * 1000) / 1000)
ans_eos = float(math.floor(np.array(eos_all).sum() * 100) / 100)
ans_eos_trade = float(math.floor(np.array(eos_trade).sum() * 100) / 100)
return ans_usdt, ans_usdt_all,ans_btc,ans_btc_trade, ans_eos,ans_eos_trade
def Signal(coin_symbol,para_min,para_up,para_low):
resu_temp = hb.get_kline(coin_symbol, para_min, 201)
cur_kline_ts = resu_temp['data'][0]['id']
#date_seq = [time.strftime("%Y-%m-%d-%H-%M", time.localtime(int(x['id']))) for x in resu_temp['data']][::-1]
close_temp = [x['close'] for x in resu_temp['data']][1:]
close = np.array(close_temp[::-1])
macd_temp = ta.MACD(close, 12, 26, 9)
up_limit = np.array([x for x in macd_temp[2] if str(x) != 'nan' and x > 0]).max() * para_up
low_limit = np.array([x for x in macd_temp[2] if str(x) != 'nan' and x < 0]).min() * para_low
up_mean = np.array([x for x in macd_temp[2] if str(x) != 'nan' and x > 0]).mean()
low_mean = np.array([x for x in macd_temp[2] if str(x) != 'nan' and x < 0]).mean()
#macd = macd_temp[2][-1]
#print(macd*2)
action_flag = 0
# flag
if (((macd_temp[2][-1] + macd_temp[2][-2] + macd_temp[2][-3]) * 2) < low_limit) and (
macd_temp[2][-2] < macd_temp[2][-1]) and (macd_temp[2][-2] < macd_temp[2][-3]) and (
macd_temp[2][-1] < 0) and (macd_temp[2][-3] < 0) and (
macd_temp[2][-1] - macd_temp[2][-2] < abs(para_low * low_mean)):
action_flag = 1
elif (((macd_temp[2][-1] + macd_temp[2][-2] + macd_temp[2][-3]) * 2) > up_limit) and (
macd_temp[2][-2] > macd_temp[2][-1]) and (macd_temp[2][-2] > macd_temp[2][-3]) and (
macd_temp[2][-1] > 0) and (macd_temp[2][-3] > 0) and (
macd_temp[2][-2] - macd_temp[2][-1] < abs(para_up * up_mean)):
action_flag = -1
return action_flag,cur_kline_ts,up_limit,low_limit
def get_curr_order(self, exCoin, tradetype):
try:
for i in range(3):
try:
rq_ord_curr = HuobiServices.orders_list(
exCoin, 'submitted,partial-filled', None, '2018-07-01',
None, None, 'next', None)
except Exception as ex:
print(Exception, ":", ex)
continue
if rq_ord_curr.find('"status":"ok"') != -1:
break
else:
print("order_curr,order_curr")
time.sleep(5)
if rq_ord_curr.find(tradetype) != -1:
orders = json.loads(rq_ord_curr)['data']
for i in orders:
if str(i).find("'type': '" + tradetype + "'") != -1:
order_id = i['id']
creat_time = i['created-at']
set_price = i['price']
break
return [str(order_id), str(creat_time), str(set_price)]
else:
return []
except Exception as ex:
print(Exception, ":", ex)
return
def get_filled_buy_order(self, exCoin, coin):
try:
buy_time = []
buy_price = []
for i in range(3):
try:
rq_ord_filled = HuobiServices.orders_matchresults(
exCoin, None, '2018-07-01', None, None, 'next', None)
except Exception as ex:
print(Exception, ":", ex)
continue
if rq_ord_filled.find('"status":"ok"') != -1:
orders = json.loads(rq_ord_filled)['data']
break
else:
print("order_fill,order_fill")
print(rq_ord_filled)
time.sleep(5)
for i in orders:
if str(i).find("'type': 'sell-limit'") != -1:
if orders.index(i) == 0 and float(self.account(coin)) > 1:
continue
else:
break
elif str(i).find("'type': 'buy-limit'") != -1:
creat_time = i['created-at']
buy_time.append(str(creat_time))
set_price = i['price']
buy_price.append(str(set_price))
return [list(reversed(buy_time)), list(reversed(buy_price))]
except Exception as ex:
print(Exception, ":", ex)
return
def get_huobi_orderboook (self, currency ="btc"):
huobiDepth = Huobi.get_depth(currency.lower() + "usdt", "step0")
self.huobi = {}
self.huobi.update(asks=huobiDepth['tick']['asks'][:self.amount])
self.huobi.update(bids=huobiDepth['tick']['bids'][:self.amount])
tick2 = time.time()
def get_huobi_depth(self,currency = ""):
if currency == "":
currency = self.currency
huobiDepth = huobi.get_depth(currency.lower()+"usdt","step0")
self.marketDepth['asks'] = huobiDepth['tick']['asks']
self.marketDepth['bids'] = huobiDepth['tick']['bids']
def Signal(coin_symbol,para_min,para_up,para_low,show_len):
resu_temp = hb.get_kline(coin_symbol, para_min, 200+show_len)
cur_kline_ts = resu_temp['data'][0]['id']
date_seq = [time.strftime("%Y-%m-%d-%H-%M", time.localtime(int(x['id']))) for x in resu_temp['data']][::-1]
amount_temp = [x['vol'] for x in resu_temp['data']]
amount = np.array(amount_temp[::-1])
vol_temp = [x['amount'] for x in resu_temp['data']]
vol = np.array(vol_temp[::-1])
close_temp = [x['close'] for x in resu_temp['data']]
close = np.array(close_temp[::-1])
price_avg_long0 = []
price_avg_long = []
price_avg_short = []
price_close = []
delt_list = []
for i in range(201,len(amount)):
price_avg_long.append(float(amount[i-200:i+1].mean()/vol[i-200:i+1].mean()))
price_avg_long0.append(float(amount[:i + 1].mean() / vol[:i + 1].mean()))
if vol[i] == 0:
temp_short = price_avg_short[-1]
price_avg_short.append(temp_short)
else:
price_avg_short.append(float(amount[i]/vol[i]))
price_close.append(close[i])
delt_list.append(float(amount[i-200:i+1].mean()/vol[i-200:i+1].mean()) / float(amount[i-1-200:i].mean()/vol[i-1-200:i].mean()))
resu = []
color = []
macd_temp = ta.MACD(close, 12, 26, 9)
# up_limit = np.array([x for x in macd_temp[2] if str(x) != 'nan' and x > 0]).max() * para_up
# low_limit = np.array([x for x in macd_temp[2] if str(x) != 'nan' and x < 0]).min() * para_low
# up_mean = np.array([x for x in macd_temp[2] if str(x) != 'nan' and x > 0]).mean()
# low_mean = np.array([x for x in macd_temp[2] if str(x) != 'nan' and x < 0]).mean()
macd_list = macd_temp[2][len(macd_temp[2])-len(price_close):len(macd_temp[2])]
#macd_list = [x for x in macd_temp[2] if str(x) != 'nan']
deal_list = []
for j in range(len(macd_list)):
macd = macd_list[j]
action_flag = 0
if j > 1:
if macd_list[j-2] > macd_list[j-1] and macd_list[j] > macd_list[j-1] and macd_list[j] < 0 and macd_list[j-2] < 0 and (macd_list[j-2]+macd_list[j-1]+macd_list[j])<-0.1:
action_flag = 1
elif macd_list[j-2] < macd_list[j-1] and macd_list[j] < macd_list[j-1] and macd_list[j] > 0 and macd_list[j-2] > 0 and (macd_list[j-2]+macd_list[j-1]+macd_list[j])>0.1:
action_flag = -1
if action_flag != 0:
deal_list.append(j)
if macd >= 0:
color.append('g')
else:
color.append('r')
resu.append(macd)
return price_avg_long0,price_avg_long,price_avg_short,price_close,delt_list,macd_list,color,deal_list
def get_huobi_ticker(self, currency = ""):
if currency == "":
currency = self.currency
huobiTicker = huobi.get_ticker(currency.lower()+"usdt")['tick']
huobiTicker['askPrice'] = huobiTicker['ask'][0]
huobiTicker['bidPrice'] = huobiTicker['bid'][0]
self.btcQuote['global']['ticker'] = huobiTicker
def get_symbols():
# symbols = ['btcusdt', 'bchusdt', 'ethusdt', 'etcusdt', 'ltcusdt', 'eosusdt', 'adausdt', 'xrpusdt', 'omgusdt', 'iotausdt', 'steemusdt', 'dashusdt', 'zecusdt', 'hb10usdt']
# symbols += ['bchbtc', 'ethbtc', 'ltcbtc', 'etcbtc', 'eosbtc', 'adabtc', 'xrpbtc', 'iotabtc', 'omgbtc', 'dashbtc', 'steembtc', 'zecbtc', 'xmrbtc']
# symbols += ['eoseth', 'adaeth', 'omgeth', 'xmreth', 'steemeth', 'iotaeth']
raw_data = hs.get_symbols()
symbols = []
for x in raw_data['data']:
symbols.append(x['symbol'])
return symbols
def getlast5close():
history_close = []
datas = hb.get_kline('btcusdt', '1min', size=5)
if datas is not None:
if 'data' in datas:
last5info = datas['data']
for info in last5info:
history_close.append(info['close'])
return history_close
def get_cur_price_by_kline():
res = hb.get_kline('btcusdt', '5min')
if res:
res = res.get('data')
cur_price = res[0].get('close')
# print(res)
#print('price:{}'.format(cur_price))
return cur_price
else:
return None
def get_last_order(coin_symbol,type):
# 获取上一次市价买单的成交价(即成本价)
last_order_temp = hb.orders_list(symbol=coin_symbol, states='filled', direct='next',types=type)
last_order_dict = last_order_temp['data']
last_price = 0.00
for i in range(len(last_order_dict)):
if str(last_order_dict[i]['type'])[:4] == str(type)[:4]:
last_order_id = last_order_dict[i]['id']
last_price = float(last_order_dict[i]['field-cash-amount']) / float(last_order_dict[i]['field-amount'])
break
return last_price
def Acct_Info(curr_type):
resu = hb.get_balance(xxxxxxxx)
if resu['status'] == 'error':
print('Acct_Info Errrrrr')
return -1
else:
resu_data = resu['data']['list']
for i in range(len(resu_data)):
if resu_data[i]['currency'] == curr_type and resu_data[i]['type'] == 'trade':
return float(resu_data[i]['balance'])
return 0
def get_portfolio(coin_list,para_min):
#获取 k 线数据，检查时间戳，不一致的话返回[]
init_ts = ''
list_return = []
for i in range(len(coin_list)):
resu_temp = hb.get_kline(str(coin_list[i]) + 'usdt', para_min, 201)
cur_kline_ts = resu_temp['data'][0]['id']
if i == 0:
init_ts = cur_kline_ts
elif init_ts != cur_kline_ts:
return []
#date_seq = [time.strftime("%Y-%m-%d-%H-%M", time.localtime(int(x['id']))) for x in resu_temp['data']][::-1]
close_temp = [x['close'] for x in resu_temp['data']][1:]
close = np.array(close_temp[::-1])
if len(close) != 200:
return []
ri = []
ri = close/close[0]
ri = ri - 1.0
ri = ri * 100
list_return.append(ri)
#list_return.append(np.array([float(0.00)] * 200))
# 求协方差矩阵
cov = np.cov(np.array(list_return))
# 求特征值和其对应的特征向量
ans = np.linalg.eig(cov)
# 排序，特征向量中负数置0，非负数归一
ans_index = copy.copy(ans[0])
ans_index.sort()
resu = []
for k in range(len(ans_index)):
con_temp = []
con_temp.append(ans_index[k])
content_temp1 = ans[1][np.argwhere(ans[0] == ans_index[k])[0][0]]
content_temp2 = []
#content_sum = np.array([x for x in content_temp1 if x >= 0.00]).sum()
content_sum = np.array([abs(x) for x in content_temp1]).sum()
for m in range(len(content_temp1)):
content_temp2.append(abs(content_temp1[m]) / content_sum)
# if content_temp1[m] >= 0 and content_sum > 0:
# content_temp2.append(content_temp1[m]/content_sum)
# else:
# content_temp2.append(0.00)
con_temp.append(content_temp2)
resu.append(con_temp)
return resu
def getcoin():
if request.method == 'POST' and request.is_json:
coin_name = request.get_json()['coin_name']
period = request.get_json()['period']
size1 = request.get_json()['size']
size = int(size1)
result = api.get_kline(coin_name, period, size)
#print(coin_name,period,size)
#print(result)
json.dump(result)
return result
def get_cur_acount(itype):
acct_id = hb.get_accounts()
#print(acct_id)
idLists = acct_id['data']
masterid = idLists[0]['id']
master_balance = hb.get_balance(masterid)
balancelists = master_balance['data']['list']
res = []
for listnum in balancelists:
if (itype == 'usdt'):
if (listnum['currency'] == 'usdt'):
return float(listnum['balance'])
elif (itype == 'btc'):
if (listnum['currency'] == 'btc'):
return float(listnum['balance'])
else:
if (listnum['currency'] == 'usdt' and listnum['type'] == 'trade'):
res.append(listnum['balance'])
if (listnum['currency'] == 'btc' and listnum['type'] == 'trade'):
res.append(listnum['balance'])
return res
def Signal(coin_symbol, para_min, para_up, para_low):
resu_temp = hb.get_kline(coin_symbol, para_min, 200)
cur_kline_ts = resu_temp['data'][0]['id']
date_seq = [
time.strftime("%Y-%m-%d-%H-%M", time.localtime(int(x['id'])))
for x in resu_temp['data']
][::-1]
close_temp = [x['close'] for x in resu_temp['data']][1:]
close = np.array(close_temp[::-1])
resu = []
color = []
macd_temp = ta.MACD(close, 12, 26, 9)
up_limit = np.array([x for x in macd_temp[2] if str(x) != 'nan' and x > 0
]).max() * para_up
low_limit = np.array(
[x
for x in macd_temp[2] if str(x) != 'nan' and x < 0]).min() * para_low
up_mean = np.array([x for x in macd_temp[2]
if str(x) != 'nan' and x > 0]).mean()
low_mean = np.array([x for x in macd_temp[2]
if str(x) != 'nan' and x < 0]).mean()
macd_list = [x for x in macd_temp[2] if str(x) != 'nan']
for i in range(len(macd_list)):
macd = macd_list[i]
if macd >= 0:
color.append('g')
else:
color.append('r')
resu.append(macd)
action_flag = 0
# flag
if (((macd_temp[2][-1] + macd_temp[2][-2] + macd_temp[2][-3]) *
2) < low_limit) and (macd_temp[2][-2] < macd_temp[2][-1]) and (
macd_temp[2][-2] < macd_temp[2][-3]
) and (macd_temp[2][-1] < 0) and (macd_temp[2][-3] < 0) and (
macd_temp[2][-1] - macd_temp[2][-2] < abs(para_low * low_mean)):
action_flag = 1
elif (((macd_temp[2][-1] + macd_temp[2][-2] + macd_temp[2][-3]) * 2) >
up_limit) and (macd_temp[2][-2] > macd_temp[2][-1]) and (
macd_temp[2][-2] > macd_temp[2][-3]
) and (macd_temp[2][-1] > 0) and (macd_temp[2][-3] > 0) and (
macd_temp[2][-2] - macd_temp[2][-1] < abs(para_up * up_mean)):
action_flag = -1
return action_flag, resu, color, up_limit, low_limit
def cancel_order(self, exCoin, tradetype):
try:
for i in range(3):
try:
rq_ord_cancel = HuobiServices.cancel_order(
self.get_curr_order(exCoin, tradetype)[0])
except Exception as ex:
print(Exception, ":", ex)
continue
if rq_ord_cancel.find('"status":"ok"') != -1:
break
else:
print("cancel_order,cancel_order")
time.sleep(5)
except Exception as ex:
print(Exception, ":", ex)
return
def account(self, coin):
try:
for i in range(3):
try:
rq_acc = HuobiServices.get_balance('443597')
except:
continue
if rq_acc.find('"status":"ok"') != -1:
l = re.search(
'"currency":"' + coin +
'","type":"trade","balance":"(.+?)"', rq_acc).group(1)
break
else:
print("accout,accout")
time.sleep(5)
return l
except Exception as ex:
print(Exception, ":", ex)
return
def trade(self, coin, exCoin, tradeType, tradePrice):
try:
for i in range(3):
try:
rq_tra = HuobiServices.send_order(
self.get_coin_amount(coin, exCoin), 'spot-app', exCoin,
tradeType, tradePrice)
except Exception as ex:
print(Exception, ":", ex)
continue
if rq_tra.find('"status":"ok"') != -1:
print(rq_tra)
break
else:
print("trade,trade 11")
time.sleep(5)
except Exception as ex:
print(Exception, ":", ex)
return
def get_avg_order(coin_symbol,cur_price):
# 获取上一次市价买单的成交价(即成本价)
resu_orders_temp = hb.orders_matchresults(symbol=coin_symbol, direct='next')
resu_orders_dict = resu_orders_temp['data']
his_amount = []
his_vol = []
last_buy_ts = 0
for i in range(len(resu_orders_dict)):
if str(resu_orders_dict[i]['type'])[:4] == 'sell':
break
elif str(resu_orders_dict[i]['type'])[:3] == 'buy':
his_amount.append(float(resu_orders_dict[i]['price'])*float(resu_orders_dict[i]['filled-amount']))
his_vol.append(float(resu_orders_dict[i]['filled-amount']))
last_buy_ts = max(last_buy_ts,int(resu_orders_dict[i]['created-at']))
if len(his_amount) == 0:
return 0.00,0
else:
ans = float((np.array(his_amount).mean())/(np.array(his_vol).mean()))
return ans,math.floor(last_buy_ts/1000)
def Signal2(coin_symbol, para_min, para_up, para_low, show_len):
resu_temp = hb.get_kline(coin_symbol, para_min, show_len)
cur_kline_ts = resu_temp['data'][0]['id']
date_seq = [time.strftime("%Y-%m-%d-%H-%M", time.localtime(int(x['id']))) for x in resu_temp['data']][::-1]
amount_temp = [x['vol'] for x in resu_temp['data']]
amount = np.array(amount_temp[::-1])
vol_temp = [x['amount'] for x in resu_temp['data']]
vol = np.array(vol_temp[::-1])
close_temp = [x['close'] for x in resu_temp['data']][1:]
close = np.array(close_temp[::-1])
std = close.std()
up_limit = para_up*std
low_limit = para_low*std
macd_temp = ta.MACD(close, 12, 26, 9)
action_flag1 = 0
if (macd_temp[2][-2] < macd_temp[2][-1]) and (macd_temp[2][-2] < macd_temp[2][-3]) and (macd_temp[2][-1] < 0) and (macd_temp[2][-3] < 0) and (macd_temp[2][-3]+macd_temp[2][-2]+macd_temp[2][-1]) < -0.079:
action_flag1 = 1
elif (macd_temp[2][-2] > macd_temp[2][-1]) and (macd_temp[2][-2] > macd_temp[2][-3]) and (macd_temp[2][-1] > 0) and (macd_temp[2][-3] > 0) and (macd_temp[2][-3]+macd_temp[2][-2]+macd_temp[2][-1]) > 0.079:
action_flag1 = -1
price_avg_long = float(amount.sum() / vol.sum())
price_avg_short = float(amount[-1] / vol[-1])
action_flag2 = 0
delt = price_avg_short - price_avg_long
if price_avg_short - price_avg_long > para_up*std:
action_flag2 = -1
elif price_avg_short - price_avg_long < para_low*std:
action_flag2 = 1
action_flag = 0
if action_flag1 == action_flag2 == 1 :
action_flag = 1
elif action_flag1 == action_flag2 == -1 :
action_flag = -1
return action_flag, cur_kline_ts, up_limit, low_limit, delt,std
def get_jingdu(self, coin, precision_type):
try:
for i in range(3):
try:
rq_jingdu = HuobiServices.get_symbols()
except:
continue
if rq_jingdu.find('"status":"ok"') != -1:
jingdu = json.loads(rq_jingdu)['data']
break
else:
print("jingdu,jingdu,jingdu")
time.sleep(5)
for i in jingdu:
if i['base-currency'] == coin and i['quote-currency'] == 'usdt':
amount = i[precision_type]
return int(amount)
break
except Exception as ex:
print(Exception, ":", ex)
return
def get_data(self, exCoin, period, size):
try:
List_data = []
for i in range(3):
try:
rq_data = HuobiServices.get_kline(exCoin, period, size)
except:
continue
if rq_data.find('"status":"ok"') != -1:
market = json.loads(rq_data)['data']
break
else:
print("data,data,data")
print(rq_data)
time.sleep(5)
for i in market:
List_data.append(float(i['close']))
return list(reversed(List_data))
except Exception as ex:
print(Exception, ":", ex)
return
def produce_model(timeInterval,column):
np.random.seed(42)
global data
data = hb.get_kline('btcusdt', timeInterval, size=2000)
data['data'].reverse()
hist = pd.DataFrame(data['data'])
hist = hist.set_index('id')
hist.index = pd.to_datetime(hist.index, unit='s')
hist_t = hist[:-1]
target_col = column
# data params
window_len = 7
zero_base = True
# model params
lstm_neurons = 20
epochs = 50
batch_size = 4
loss = 'mae'
dropout = 0.25
optimizer = 'adam'
#df, target_col, window_len=10, zero_base=True, test_size=0.2
X_train, y_train = prepare_data(hist_t, target_col, window_len=window_len, zero_base=zero_base)
global model_close
global model_high
global model_low
if column == 'close':
model_close = build_lstm_model(X_train, output_size=1, neurons=lstm_neurons, dropout=dropout, loss=loss,optimizer=optimizer)
history = model_close.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=1, shuffle=True)
elif column == 'high':
model_high = build_lstm_model(X_train, output_size=1, neurons=lstm_neurons, dropout=dropout, loss=loss,optimizer=optimizer)
history = model_high.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=1, shuffle=True)
else:
model_low = build_lstm_model(X_train, output_size=1, neurons=lstm_neurons, dropout=dropout, loss=loss,optimizer=optimizer)
history = model_low.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=1, shuffle=True)