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dsyoon
2023-01-23 16:48:57 +09:00
parent fcb3671341
commit 85611bec77
1 changed files with 110 additions and 104 deletions
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Bithumb_minute.py
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@@ -443,26 +443,25 @@ class Bithumb_minute(HTS):
return False
return True
def checkWithEnvelope(self, data, analyzed_day=120, isRealTime=False):
def checkWithEnvelope(self, data1, data2=None, isRealTime=False):
bsLine = {}
size = len(data["close"])
size = len(data1["close"])
bsLine['buy'] = [-1 for i in range(size)]
bsLine['buy_weight'] = [-1 for i in range(size)]
bsLine['sell'] = [-1 for i in range(size)]
bsLine['sell_weight'] = [-1 for i in range(size)]
gap_interval = analyzed_day
gap_state = False
for i in range(size):
if isRealTime:
if i < size - 1:
continue
if i > 10:
"""
# 만약 전일 저가와 오늘 종의 차이가 1만원이 넘으면 향후 60일은 분석하지 않는다.
if data['high'][i] < int(data['low'][i - 1] * 0.7):
if data1['high'][i] < int(data1['low'][i - 1] * 0.7):
gap_state = True
gap_interval -= 1
continue
@@ -474,149 +473,150 @@ class Bithumb_minute(HTS):
gap_interval -= 1
continue
if data['disparity'][i] < 2:
if data1['disparity'][i] < 2:
check = True
for l in range(i - 3, i):
if (
data['gradients_avg60'][l - 1] > data['gradients_avg60'][l] or
data['gradients_avg20'][l - 1] > data['gradients_avg20'][l] or
data['gradients_low'][l - 1] > data['gradients_low'][l] or
data['disparity_avg5'][l - 1] > data['disparity_avg5'][l] or
data['disparity'][l - 1] < data['disparity'][l]
data1['gradients_avg60'][l - 1] > data1['gradients_avg60'][l] or
data1['gradients_avg20'][l - 1] > data1['gradients_avg20'][l] or
data1['gradients_low'][l - 1] > data1['gradients_low'][l] or
data1['disparity_avg5'][l - 1] > data1['disparity_avg5'][l] or
data1['disparity'][l - 1] < data1['disparity'][l]
):
check = False
break
if check and 99 < sum(data['disparity_avg5'][i - 4:i + 1]) / 5 < 100 and 99 < sum(
data['disparity_avg60'][i - 4:i + 1]) / 5 < 100:
buy = data['low'][i]
data['buy'][i] = buy
if check and 99 < sum(data1['disparity_avg5'][i - 4:i + 1]) / 5 < 100 and 99 < sum(
data1['disparity_avg60'][i - 4:i + 1]) / 5 < 100:
buy = data1['low'][i]
data1['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 0.1
check = True
for l in range(i - 2, i):
if (
data['gradients_avg60'][l - 1] > data['gradients_avg60'][l] or
data['gradients_low'][l - 1] > data['gradients_low'][l]
data1['gradients_avg60'][l - 1] > data1['gradients_avg60'][l] or
data1['gradients_low'][l - 1] > data1['gradients_low'][l]
):
check = False
break
if (
check and
-0.0011 < data['gradients_low'][i] < 0 and -0.007 < data['gradients_avg5'][i] < 0.001 and
-0.0012 < data['gradients_avg60'][i] < 0 and
98.90 < data['disparity_avg5'][i] < 101
-0.0011 < data1['gradients_low'][i] < 0 and -0.007 < data1['gradients_avg5'][i] < 0.001 and
-0.0012 < data1['gradients_avg60'][i] < 0 and
98.90 < data1['disparity_avg5'][i] < 101
):
buy = data['low'][i]
data['buy'][i] = buy
buy = data1['low'][i]
data1['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 0.1
check = True
for l in range(i - 6, i):
if (
data['gradients_avg60'][l - 1] < data['gradients_avg60'][l] or
data['gradients_avg20'][l - 1] < data['gradients_avg20'][l] or
data['gradients_low'][l - 1] < data['gradients_low'][l] or
-0.039 < data['gradients_low'][l - 1] < -0.35 or
-0.05 < data['gradients_avg20'][l - 1] < -0.30 or
-0.40 < data['gradients_avg60'][l - 1] < -0.30
data1['gradients_avg60'][l - 1] < data1['gradients_avg60'][l] or
data1['gradients_avg20'][l - 1] < data1['gradients_avg20'][l] or
data1['gradients_low'][l - 1] < data1['gradients_low'][l] or
-0.039 < data1['gradients_low'][l - 1] < -0.35 or
-0.05 < data1['gradients_avg20'][l - 1] < -0.30 or
-0.40 < data1['gradients_avg60'][l - 1] < -0.30
):
check = False
break
if check and 99 < min(data['disparity_avg5'][i - 6:i]) < max(data['disparity_avg5'][i - 6:i]) < 101:
buy = data['low'][i]
data['buy'][i] = buy
if check and 99 < min(data1['disparity_avg5'][i - 6:i]) < max(data1['disparity_avg5'][i - 6:i]) < 101:
buy = data1['low'][i]
data1['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 0.1
check = True
for l in range(i - 3, i):
if (
data['gradients_low'][l - 1] < data['gradients_low'][l] or
data['gradients_avg60'][l - 1] < data['gradients_avg60'][l] or
data['gradients_avg20'][l - 1] < data['gradients_avg20'][l] or
0.01 < data['gradients_low'][l - 1] < 0.21 or
-0.09 < data['gradients_avg20'][l - 1] < -0.002 or
0.01 < data['gradients_avg60'][l - 1] < 0.021
data1['gradients_low'][l - 1] < data1['gradients_low'][l] or
data1['gradients_avg60'][l - 1] < data1['gradients_avg60'][l] or
data1['gradients_avg20'][l - 1] < data1['gradients_avg20'][l] or
0.01 < data1['gradients_low'][l - 1] < 0.21 or
-0.09 < data1['gradients_avg20'][l - 1] < -0.002 or
0.01 < data1['gradients_avg60'][l - 1] < 0.021
):
check = False
break
if check:
buy = data['low'][i]
data['buy'][i] = buy
buy = data1['low'][i]
data1['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 0.1
if (data['disparity'][i] < 5 and 99.0 < data['disparity_avg60'][i] < 99.1 and
-0.009 < data['gradients_avg60'][i] < -0.008 and 0.015 < data['gradients_avg20'][i] < 0.016 and
-0.006 < data['gradients_avg5'][i] < -0.005 and -0.009 < data['gradients_low'][i] < -0.008):
if (data1['disparity'][i] < 5 and 99.0 < data1['disparity_avg60'][i] < 99.1 and
-0.009 < data1['gradients_avg60'][i] < -0.008 and 0.015 < data1['gradients_avg20'][i] < 0.016 and
-0.006 < data1['gradients_avg5'][i] < -0.005 and -0.009 < data1['gradients_low'][i] < -0.008):
check = True
for l in range(i - 5, i):
if (
data['gradients_avg60'][l - 1] > data['gradients_avg60'][l] or
data['gradients_low'][l - 1] > data['gradients_low'][l] or
data['disparity'][l - 1] < data['disparity'][l]
data1['gradients_avg60'][l - 1] > data1['gradients_avg60'][l] or
data1['gradients_low'][l - 1] > data1['gradients_low'][l] or
data1['disparity'][l - 1] < data1['disparity'][l]
):
check = False
break
if check:
buy = data['low'][i]
data['buy'][i] = buy
buy = data1['low'][i]
data1['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 0.1
if data['macd'][i] < -4000:
if data['macd'][i - 1] < data['macd'][i]:
if not self.notBuy(data, i) and data['slow_k'][i] < 30:
buy = data['low'][i]
data['buy'][i] = buy
if data1['macd'][i] < -4000:
if data1['macd'][i - 1] < data1['macd'][i]:
if not self.notBuy(data1, i) and data1['slow_k'][i] < 30:
buy = data1['low'][i]
data1['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 0.1
# macd 이전에 없던 바닥인 경우 상승할 찰나 매수
if data['macds'][i - 1] < min(data['macds'][:i - 1]):
if data['macds'][i - 1] < data['macds'][i]:
if not self.notBuy(data, i) and data['slow_k'][i] < 30:
buy = data['low'][i]
data['buy'][i] = buy
if data1['macds'][i - 1] < min(data1['macds'][:i - 1]):
if data1['macds'][i - 1] < data1['macds'][i]:
if not self.notBuy(data1, i) and data1['slow_k'][i] < 30:
buy = data1['low'][i]
data1['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 0.1
if (
98 < data['disparity_avg5'][i] < 100 and data['disparity_avg20'][i] < 93.5 and
data['disparity_avg60'][i] < 89 and
-0.014 < data['gradients_avg60'][i] < -0.013 and -0.03 < data['gradients_avg20'][i] < -0.02 and -0.014 < data['gradients_low'][i] < -0.013 and
data['slow_k'][i] < 11
98 < data1['disparity_avg5'][i] < 100 and data1['disparity_avg20'][i] < 93.5 and
data1['disparity_avg60'][i] < 89 and
-0.014 < data1['gradients_avg60'][i] < -0.013 and -0.03 < data1['gradients_avg20'][i] < -0.02 and -0.014 < data1['gradients_low'][i] < -0.013 and
data1['slow_k'][i] < 11
):
if not self.notBuy(data, i):
buy = data['low'][i]
data['buy'][i] = buy
if not self.notBuy(data1, i):
buy = data1['low'][i]
data1['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 0.1
if data['slow_k'][i] < 20 and data['slow_k'][i - 1] < data['slow_d'][i - 1] and data['slow_d'][i] < data['slow_k'][i]:
buy = data['low'][i]
data['buy'][i] = buy
if data1['slow_k'][i] < 20 and data1['slow_k'][i - 1] < data1['slow_d'][i - 1] and data1['slow_d'][i] < data1['slow_k'][i]:
buy = data1['low'][i]
data1['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 0.3
"""
if data['slow_k'][i] > 90:
sell = data['close'][i]
data['sell'][i] = sell
if data2['slow_k'][i] < 30:
if data1['slow_k'][i] < 30 and data1['avg5'][i] < data1['close'][i]:
buy = data1['close'][i]
data1['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 1
if data1['slow_k'][i] > 80 and (data1['slow_d'][i-1] < data1['slow_k'][i-1] and data1['slow_k'][i] < data1['slow_d'][i]):
sell = data1['close'][i]
data1['sell'][i] = sell
bsLine['sell'][i] = sell
bsLine['sell_weight'][i] = 100
return bsLine
if data['slow_k'][i] > 80 and data['slow_d'][i-1] < data['slow_k'][i-1] and data['slow_k'][i] < data['slow_d'][i]:
sell = data['low'][i]
data['sell'][i] = sell
bsLine['sell'][i] = sell
bsLine['sell_weight'][i] = 100
return bsLine, data
def get_ohlcv(self, ticker):
url = "https://api.upbit.com/v1/candles/minutes/5"
def get_ohlcv(self, ticker, minute=5):
url = "https://api.upbit.com/v1/candles/minutes/"+str(minute)
querystring = {"market": "KRW-"+ticker, "count": "300"}
response = requests.request("GET", url, params=querystring)
json_response = json.loads(response.text)
@@ -656,24 +656,10 @@ class Bithumb_minute(HTS):
return
def buyRealTime(self, ticker, analyzed_day=120, isRealTime=False):
def getStock(self, ticker, analyzed_day, minute=5):
stock = {"CODE": ticker, "NAME": ticker, "PRICE": []}
"""
# binance
btc_ohlcv = self.binance.fetch_ohlcv(ticker + "/BKRW")
df = pd.DataFrame(btc_ohlcv, columns=['datetime', 'open', 'high', 'low', 'close', 'volume'])
df['datetime'] = pd.to_datetime(df['datetime'], unit='ms')
df.set_index('datetime', inplace=True)
"""
"""
# bithumb
df_ = pybithumb.get_ohlcv(ticker)
"""
df = self.get_ohlcv(ticker)
df = self.get_ohlcv(ticker, minute)
if df is None:
return
close = pybithumb.get_current_price(ticker)
@@ -690,11 +676,31 @@ class Bithumb_minute(HTS):
# 분석일 데이터만 활용한다 (이전 데이터는 제거)
data.drop(data.index[:len(data) - analyzed_day], inplace=True)
bsLine, data = self.checkWithEnvelope(data, analyzed_day, isRealTime=isRealTime)
print(ticker, "/", datetime.now().strftime('%Y-%m-%d %H:%M:%S'), "/", data['close'][len(data['close'])-1], "/", data['slow_k'][len(data['slow_k'])-1])
return data
def buyRealTime(self, ticker, analyzed_day=120, isRealTime=False):
"""
# binance
btc_ohlcv = self.binance.fetch_ohlcv(ticker + "/BKRW")
df = pd.DataFrame(btc_ohlcv, columns=['datetime', 'open', 'high', 'low', 'close', 'volume'])
df['datetime'] = pd.to_datetime(df['datetime'], unit='ms')
df.set_index('datetime', inplace=True)
"""
"""
# bithumb
df_ = pybithumb.get_ohlcv(ticker)
"""
stock1 = self.getStock(ticker, analyzed_day, minute=5)
stock2 = self.getStock(ticker, analyzed_day, minute=30)
# 매수 매도 체크
bsLine = self.checkWithEnvelope(stock1, stock2, isRealTime=isRealTime)
print(ticker, "/", datetime.now().strftime('%Y-%m-%d %H:%M:%S'), "/", stock1['close'][len(stock1['close'])-1], "/", stock1['slow_k'][len(stock1['slow_k'])-1])
# 그래프를 그린다.
if len(data.index) > 10:
if len(stock1.index) > 10:
today = datetime.today().strftime('%Y%m%d')
log_filename = os.path.join(RESOURCE_PATH, 'order', "bithumb"+"_"+today + '.log')
@@ -707,14 +713,14 @@ class Bithumb_minute(HTS):
count = round((balance * (bsLine['buy_weight'][len(bsLine['buy_weight']) - 1] / 100)) / bsLine['buy'][len(bsLine['buy']) - 1], 2)
order = self.bithumb.buy_limit_order(ticker, bsLine['buy'][len(bsLine['buy']) - 1], count)
# order: ('bid', 'BTC', 'C0101000000322993432', 'KRW')
print(ticker, "/", datetime.now().strftime('%Y-%m-%d %H:%M:%S'), "/", data['close'][len(data['close']) - 1], "/ BUY / ", data['slow_k'][len(data['slow_k']) - 1], "/", bsLine['buy'][len(bsLine['buy']) - 1], "/", count)
print(ticker, "/", datetime.now().strftime('%Y-%m-%d %H:%M:%S'), "/", stock1['close'][len(stock1['close']) - 1], "/ BUY / ", stock1['slow_k'][len(stock1['slow_k']) - 1], "/", bsLine['buy'][len(bsLine['buy']) - 1], "/", count)
with open(log_filename, 'a', newline='', encoding='utf-8') as log_file:
wr = csv.writer(log_file)
wr.writerow(["buy", datetime.now().strftime('%Y-%m-%d %H:%M:%S'), order[0], order[1], order[2], order[3]])
dirName = os.path.join(RESOURCE_PATH, 'analysis', 'bithumb')
self.writeFile(dirName, ticker, data, bsLine, datetime.now().strftime('%Y%m%d %H%M%S'), 'buy')
self.writeFile(dirName, ticker, stock1, bsLine, datetime.now().strftime('%Y%m%d %H%M%S'), 'buy')
if max(bsLine['sell'][len(bsLine['sell']) - 2:]) > 100:
tmp = self.bithumb.get_balance(ticker)
@@ -722,13 +728,13 @@ class Bithumb_minute(HTS):
return
count = tmp[0]
order = self.bithumb.sell_limit_order(ticker, bsLine['sell'][len(bsLine['sell'])-1], count)
print(ticker, "/", datetime.now().strftime('%Y-%m-%d %H:%M:%S'), "/", data['close'][len(data['close']) - 1], "/ BUY / ", data['slow_k'][len(data['slow_k']) - 1], "/", bsLine['sell'][len(bsLine['sell']) - 1], "/", count)
print(ticker, "/", datetime.now().strftime('%Y-%m-%d %H:%M:%S'), "/", stock1['close'][len(stock1['close']) - 1], "/ BUY / ", stock1['slow_k'][len(stock1['slow_k']) - 1], "/", bsLine['sell'][len(bsLine['sell']) - 1], "/", count)
dirName = os.path.join(RESOURCE_PATH, 'analysis', 'bithumb')
self.writeFile(dirName, ticker, data, bsLine, datetime.now().strftime('%Y%m%d %H%M%S'), 'sell')
self.writeFile(dirName, ticker, stock1, bsLine, datetime.now().strftime('%Y%m%d %H%M%S'), 'sell')
else:
dirName = os.path.join(RESOURCE_PATH, 'analysis', 'bithumb')
self.writeFile(dirName, ticker, data, bsLine, datetime.now().strftime('%Y%m%d %H%M%S'))
self.writeFile(dirName, ticker, stock1, bsLine, datetime.now().strftime('%Y%m%d %H%M%S'))
return