def bull_market(self, ticker, num):
dayData = pybithumb.get_ohlcv(ticker)
ma = dayData["close"].rolling(num).mean()
last_ma = ma[-2]
price = pybithumb.get_current_price(ticker)
state = None
if price > last_ma:
state = "상승장"
else:
state = "하락장"
return price, last_ma, state
def get_target_price(ticker):
df = pybithumb.get_ohlcv("XRP")
yesterday = df.iloc[-2]
today_open = yesterday['close']
yesterday_high = yesterday['high']
yesterday_low = yesterday['low']
target = today_open + (yesterday_high - yesterday_low) * 0.5
target = int(target)
return target
def get_ror(k=0.5):
df = pybithumb.get_ohlcv("BTC")
df['range'] = (df['high'] - df['low']) * k
df['target'] = df['open'] + df['range'].shift(1)
fee = 0.0032
df['ror'] = np.where(df['high'] > df['target'],
df['close'] / df['target'] - fee, 1)
ror = df['ror'].cumprod()[-2]
return ror
def get_ma2_min(Coin):
df = pybithumb.get_ohlcv(Coin, 'minute1')
df['MA20'] = df['close'].rolling(20).mean()
period = 5
slope, intercept, r_value, p_value, stderr = stats.linregress(
[i for i in range(period)], df.MA20[-period:])
return slope
def get_market_infos(self, ticker):
df = pybithumb.get_ohlcv(ticker)
ma5 = df['close'].rolling(window=5).mean()
price = pybithumb.get_current_price(ticker)
last_ma5 = ma5[-2]
state = None
if price > last_ma5:
state = '상승장'
else:
state = '하락장'
return price, last_ma5, state
def get_target_price(ticker):
df = pybithumb.get_ohlcv("BTC") # 비트코인의 일봉 정보를 dataframe 객체로 얻어온다.
yesterday = df.iloc[
-2] # 끝에서 두 번째 행 (전일 데이터)를 가져온다. yesterday에는 전일 데이터가 Series 객체로 바인딩된다.
today_open = yesterday['close'] # 당일 시가를 얻어온다.
yesterday_high = yesterday['high'] # 전일 고가를 얻어온다.
yesterday_low = yesterday['low'] # 전일 저가를 얻어온다.
target = today_open + (
yesterday_high - yesterday_low
) * 0.5 # 래리 윌리엄스 변동성 돌파 전략의 목표가를 계산한다. 목표가 = (당일 시가 + 레인지 x 0.5)
return target
def inquiry_high_prices(tickers):
try:
high_prices = {}
for ticker in tickers:
df = pybithumb.get_ohlcv(ticker, interval="hour")
cur_hour = df.iloc[-1]
cur_hour_high = cur_hour['high']
high_prices[ticker] = cur_hour_high
return high_prices
except:
return {ticker: 0 for ticker in tickers}
def cal_target():
try:
df = pybithumb.get_ohlcv("BTC")
yesterday = df.iloc[-2]
today_open = yesterday['close']
yesterday_high = yesterday['high']
yesterday_low = yesterday['low']
target = today_open + (yesterday_high - yesterday_low) * 0.5
return target
except:
return None
def loadCoinHistory(self):
try:
coin = self.comboBox_coinName.currentText()
period = self.comboBox_periodType.currentText()
self.df = pybithumb.get_ohlcv(coin, period)
self.dateTimeEdit_start.setDateTime(self.df.index[1])
self.dateTimeEdit_end.setDateTime(self.df.index[len(self.df) - 1])
except Exception as err:
QMessageBox.information(self, "Error", str(err))
def inquiry_high_prices(tickers):
try:
high_prices = {}
for ticker in tickers:
df = pybithumb.get_ohlcv(ticker)
today = df.iloc[-1]
today_high = today['high']
high_prices[ticker] = today_high
return high_prices
except:
logger.info("inquiry_high_prices error")
return {ticker: 0 for ticker in tickers}
def get_market_infos(self, ticker):
df = pybithumb.get_ohlcv(ticker)
ma5 = df['close'].rolling(window=5).mean()
ma20 = df['close'].rolling(window=20).mean()
volume = df['volume'][-1]
# 전일 이동 편균
last_ma5 = ma5[-2]
last_ma20 = ma20[-2]
price = pybithumb.get_current_price(ticker)
#all_data = pybithumb.get_current_price("ALL")
# for ticker, data in all.items()
return price, volume, last_ma5, last_ma20
def get_target_price(ticker):
df = pybithumb.get_ohlcv(ticker)
# -1 today
yesterday = df.iloc[-2]
today_open = yesterday['close']
yesterday_high = yesterday['high']
yesterday_low = yesterday['low']
#가격 변동폭 : 전일 고가 - 전일 저가
#매수 기준 :당일 시간에서 (변동폭 * 0.5) 이상 상승하면 매수
#매도 기준: 당일 종가에 매도
target = today_open + (yesterday_high - yesterday_low) * 0.5
return target
def getATR(currency, interval):
df = pybithumb.get_ohlcv(currency, interval)
close = df['close'][-15:-1]
high = df['high'][-15:-1]
low = df['low'][-15:-1]
TRs = []
TRs.append(high[0] - low[0])
for i in range(1, 14):
TRs.append(
max(abs(high[i] - low[i]), abs(high[i] - close[i - 1]),
abs(low[i] - close[i - 1])))
return sum(TRs) / len(TRs)
def get_market_infos(self, ticker):
try:
df = pybithumb.get_ohlcv(ticker)
ma5 = df['close'].rolling(window=5).mean()
ma20 = df['close'].rolling(window=20).mean()
# 전일 이동 편균
last_ma5 = ma5[-2]
last_ma20 = ma20[-2]
price = pybithumb.get_current_price(ticker)
return price, last_ma5, last_ma20
except:
None, None, None
def get_ror(ticker):
df = pybithumb.get_ohlcv(ticker, interval="day")
df = df["2019"]
df["변동성"] = (df['high'] - df['low']) * 0.5
df["목표가"] = df["open"] + df["변동성"].shift(1)
df["수익률"] = np.where(df['high'] > df['목표가'],
df['목표가'] / df['close'],
1)
df["기간수익률"] = df["수익률"].cumprod()
return df["기간수익률"].iloc[-1]
def get_market_infos(self, ticker): # 나머지 데이터 추가하기
df = pybithumb.get_ohlcv(ticker)
ma5 = df['close'].rolling(window=5).mean()
last_ma5 = ma5[-2]
price = pybithumb.get_current_price(ticker)
state = None
if price > last_ma5:
state = "상승장"
else:
state = "하락장"
return price, last_ma5, state
def bull_market(self, ticker):
ohlcv_btc = pybithumb.get_ohlcv(ticker, "KRW", "day")
ma5 = ohlcv_btc['close'].rolling(window=5).mean() # 5일 이동평균선 연산.
price = pybithumb.get_current_price(ticker)
last_ma5 = ma5[-2]
state = None
if price > last_ma5:
state = "상승장"
else:
state = "하락장"
return price, last_ma5, state
def bull_market(ticker):
df = pybithumb.get_ohlcv(ticker) # 티커의 과거 시세 가져오기
ma5 = df['close'].rolling(window=5).mean() # close(종가)를 5개씩 그룹화하여 평균을 낸다.
price = pybithumb.get_current_price(ticker) # 티커의 현재가 가져오기
last_ma5 = ma5[-2] # 끝에서 두 번째 위치한 전일 이동평균을 last_ma5 변수에 바인딩한다.
if price > last_ma5:
print("TICKER : {}, 상승장, 현재가격 : {}, 전일이동평균 : {}".format(
ticker, price, last_ma5))
return True
else:
print("TICKER : {}, 하락장, 현재가격 : {}, 전일이동평균 : {}".format(
ticker, price, last_ma5))
return False
def get_market_infos(self, ticker):
try:
df = pybithumb.get_ohlcv(ticker)
ma5 = df['close'].rolling(window=5).mean()
ma5_last = ma5[-2]
price = pybithumb.get_current_price(ticker)
if price > ma5_last:
status = "상승장"
else:
status = "하락장"
return price, ma5_last, status
except:
return None, None, None
def cal_moving_average(ticker="BTC", window=5):
'''
5일 이동평균을 계산
:param ticker:
:param window:
:return:
'''
try:
df = pybithumb.get_ohlcv(ticker)
close = df['close']
ma_series = close.rolling(window=window).mean()
yesterday_ma = ma_series[-2]
return yesterday_ma
except:
return None
def get_hpr(ticker):
df = pybithumb.get_ohlcv(ticker)
df = df['2018']
df['ma5'] = df['close'].rolling(window=5).mean().shift(1)
df['range'] = (df['high'] - df['low']) * 0.5
df['target'] = df['open'] + df['range'].shift(1)
df['bull'] = df['open'] > df['ma5']
fee = 0.0032
df['ror'] = np.where((df['high'] > df['target']) & df['bull'],
df['close'] / df['target'] - fee, 1)
df['hpr'] = df['ror'].cumprod()
df['dd'] = (df['hpr'].cummax() - df['hpr']) / df['hpr'].cummax() * 100
return df['hpr'][-2]
def get_ror(k):
df = pybithumb.get_ohlcv("BTC")
# df = df['2019']
df['range'] = (df['high'] - df['low']) * k
df['target'] = df['open'] + df['range'].shift(1)
fee = 0.0032
# 최고가가 타겟가 보다 높은 건 매수가 일어난 것이고, 일어난 매수의 수익률은 종가/타겟가의 비율. 매수가 안일어나면 1
df['ror'] = np.where(df['high'] > df['target'],
df['close'] / df['target'] - fee, 1)
# df.to_excel('btc.xlsx')
#각 거래의 수익 비율들의 곱은 전체 수익률이 됨.
ror = df['ror'].cumprod()[-2]
return ror
def cal_target(ticker):
'''
각 코인에 대한 목표가 저장
:param ticker: 티커, 'BTC'
:return: 목표가
'''
try:
df = pybithumb.get_ohlcv(ticker)
yesterday = df.iloc[-2]
today_open = yesterday['close']
yesterday_high = yesterday['high']
yesterday_low = yesterday['low']
target = today_open + (yesterday_high - yesterday_low) * LARRY_K
return target
except:
return None
def get_ror(k=0.8):
df = pybithumb.get_ohlcv("BTC")
df['range'] = (df['high'] - df['low']) * k
df['target'] = df['open'] + df['range'].shift(1)
df['ror'] = np.where(df['high'] > df['target'],
df['close'] / df['target'] - FEE, 1)
# df = df['2020'] 2020년의 수익률을 알아보기 위한 코드
# 거래일 마다의 기간 수익률
df['hpr'] = df['ror'].cumprod()
df['dd'] = (df['hpr'].cummax() - df['hpr']) / df['hpr'].cummax() * 100
MDD = df['dd'].max()
print(MDD)
return df
def get_target_price(tickers):
for k, v in tickers.items():
# 매수 목표 가격 만들기
df = pybithumb.get_ohlcv(k)
yesterday = df.iloc[-2]
today_open = yesterday['close']
yesterday_high = yesterday['high']
yesterday_low = yesterday['low']
target_price = round(today_open + (yesterday_high - yesterday_low) * K, 1)
# 이동평균 값 만들기
close = df['close']
ema13 = close.ewm(span=13).mean()[-2]
# 매수 목표 가격, 지수이동평균 값, 24시간 가격 변동률, 매수 여부, 20% 이상 가격 상승 시 메세지 전송 여부
target_prices[k] = [target_price, ema13, True, True]
return target_prices
def get_market_infos(self, ticker):
try:
df = pybithumb.get_ohlcv(ticker)
ma5 = df['close'].rolling(window=5).mean()
price = pybithumb.get_current_price(ticker)
last_ma5 = ma5[-2]
state = None
if price > last_ma5:
state = "상승장"
else:
state = "하락장"
return (price, last_ma5, state)
except:
return (None, None, None)
def cal_multiple_moving_average(ticker="BTC"):
'''
3일 ~ 20일의 18개의 이동 평균값을 계산
:param ticker: 티커
:return:
'''
try:
df = pybithumb.get_ohlcv(ticker)
close = df['close']
multiple_ma = []
for window in range(3, 21):
ma_series = close.rolling(window=window).mean()
ma = ma_series[-2]
multiple_ma.append(ma)
return multiple_ma
except:
return None
def cal_target(ticker):
'''
각 코인에 대한 목표가 계산
:param ticker: 코인에 대한 티커
:return:
'''
try:
df = pybithumb.get_ohlcv(ticker)
yesterday = df.iloc[-2]
today = df.iloc[-1]
today_open = today['open']
yesterday_high = yesterday['high']
yesterday_low = yesterday['low']
target = today_open + (yesterday_high - yesterday_low) * LARRY_K
return target
except:
logger.info("cal_target error {}".format(ticker))
return None
def cal_target(ticker):
'''
각 코인에 대한 목표가 저장
:param ticker: 티커, 'BTC'
:return: 목표가
'''
try:
df = pybithumb.get_ohlcv(ticker, interval="hour")
one_hour_ago = df.iloc[-2]
cur_hour = df.iloc[-1]
cur_hour_open = cur_hour['open']
one_hour_ago_high = one_hour_ago['high']
one_hour_ago_low = one_hour_ago['low']
target = cur_hour_open + (one_hour_ago_high -
one_hour_ago_low) * LARRY_K
return target
except:
return None
def cal_target(ticker, kvalues):
'''
각 코인에 대한 목표가 저장
:param ticker: 티커, 'BTC'
:return: 목표가
'''
try:
df = pybithumb.get_ohlcv(ticker)
k = cal_adaptive_k(df)
kvalues[ticker] = k
yesterday = df.iloc[-2]
today_open = yesterday['close']
yesterday_high = yesterday['high']
yesterday_low = yesterday['low']
target = today_open + (yesterday_high - yesterday_low) * k
return target
except:
return None
