def preprocess_rev(data_4, ver='v2'):
"""
# 액면분할 수정주가 반영영
data_2 = data.iloc[:186]
columns = ['close', 'open', 'high', 'low', 'eps', 'bps', 'dividend_per_stock']
for column in columns:
data_2[column] = data_2[column] / 5.0
data_3 = data.iloc[189:]
data_4 = pd.concat([data_2, data_3],axis=0)
data_4 = data_4.dropna()
"""
#
del data_4['eps']
del data_4['per']
data_4 = data_4.dropna()
# ['date_'] = data_4['date']
data_4['open_'] = data_4['open']
data_4['high_'] = data_4['high']
data_4['low_'] = data_4['low']
data_4['close_'] = data_4['close']
data_4['volume_'] = data_4['volume']
# 보조지표 추가
# 1) 이평선(SMA, EMA, WMA) (w = 5,10,15,20,30,60,120)
data_4['ma_5'] = ta.SMA(data_4.close, timeperiod=5)
data_4['ma_10'] = ta.SMA(data_4.close, timeperiod=10)
data_4['ma_15'] = ta.SMA(data_4.close, timeperiod=15)
data_4['ma_20'] = ta.SMA(data_4.close, timeperiod=20)
data_4['ma_30'] = ta.SMA(data_4.close, timeperiod=30)
data_4['ma_60'] = ta.SMA(data_4.close, timeperiod=60)
data_4['ma_120'] = ta.SMA(data_4.close, timeperiod=120)
data_4['ema_5'] = ta.EMA(data_4.close, timeperiod=5)
data_4['ema_10'] = ta.EMA(data_4.close, timeperiod=10)
data_4['ema_15'] = ta.EMA(data_4.close, timeperiod=15)
data_4['ema_20'] = ta.EMA(data_4.close, timeperiod=20)
data_4['ema_30'] = ta.EMA(data_4.close, timeperiod=30)
data_4['ema_60'] = ta.EMA(data_4.close, timeperiod=60)
data_4['ema_120'] = ta.EMA(data_4.close, timeperiod=120)
data_4['wma_5'] = ta.WMA(data_4.close, timeperiod=5)
data_4['wma_10'] = ta.WMA(data_4.close, timeperiod=10)
data_4['wma_15'] = ta.WMA(data_4.close, timeperiod=15)
data_4['wma_20'] = ta.WMA(data_4.close, timeperiod=20)
data_4['wma_30'] = ta.WMA(data_4.close, timeperiod=30)
data_4['wma_60'] = ta.WMA(data_4.close, timeperiod=60)
data_4['wma_120'] = ta.WMA(data_4.close, timeperiod=120)
data_4['ma_v5'] = ta.SMA(data_4.volume, timeperiod=5)
data_4['ma_v10'] = ta.SMA(data_4.volume, timeperiod=10)
data_4['ma_v20'] = ta.SMA(data_4.volume, timeperiod=20)
data_4['ma_v60'] = ta.SMA(data_4.volume, timeperiod=60)
data_4['ma_v120'] = ta.SMA(data_4.volume, timeperiod=120)
data_4['volume_inst_sub'] = data_4['volume_inst_buy'] - data_4[
'volume_inst_sell']
data_4['volume_fore_sub'] = data_4['volume_fore_buy'] - data_4[
'volume_fore_sell']
data_4['ma_inst_v5'] = ta.SMA(data_4.volume_inst_sub, timeperiod=5)
data_4['ma_inst_v10'] = ta.SMA(data_4.volume_inst_sub, timeperiod=10)
data_4['ma_inst_v20'] = ta.SMA(data_4.volume_inst_sub, timeperiod=20)
data_4['ma_inst_v60'] = ta.SMA(data_4.volume_inst_sub, timeperiod=60)
data_4['ma_inst_v120'] = ta.SMA(data_4.volume_inst_sub, timeperiod=120)
data_4['ma_fore_v5'] = ta.SMA(data_4.volume_fore_sub, timeperiod=5)
data_4['ma_fore_v10'] = ta.SMA(data_4.volume_fore_sub, timeperiod=10)
data_4['ma_fore_v20'] = ta.SMA(data_4.volume_fore_sub, timeperiod=20)
data_4['ma_fore_v60'] = ta.SMA(data_4.volume_fore_sub, timeperiod=60)
data_4['ma_fore_v120'] = ta.SMA(data_4.volume_fore_sub, timeperiod=120)
# 2) 볼린저밴드 (주가의 이동평균선을 중심으로 표준편차 범위를 표시)
ubb, mbb, lbb = ta.BBANDS(data_4.close, 20, 2)
data_4['ubb'] = ubb
data_4['mbb'] = mbb
data_4['lbb'] = lbb
# 3) MACD 이동평균수렴확산 (단기(EMA12)와 장기(EMA26) EMA로 모멘텀을 추정)
macd, macdsignal9, macdhist = ta.MACD(data_4.close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
data_4['macd'] = macd
data_4['macdsignal9'] = macdsignal9
data_4['macdhist'] = macdhist
# 4) RSI 상대강도지수 - 추세의 강도 파악, 과매수, 과매도 국면 판단
data_4['rsi'] = ta.RSI(data_4.close, timeperiod=14)
# 5) 스토캐스틱 오늘의 주가가 일정 동안 주가의 변동폭 중에서 어느 정도인 지?
slowk, slowd = ta.STOCH(data_4.high,
data_4.low,
data_4.close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
fastk, fastd = ta.STOCHF(data_4.high,
data_4.low,
data_4.close,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
fastk_rsi, fastd_rsi = ta.STOCHRSI(data_4.close,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
data_4['slowk'] = slowk
data_4['slowd'] = slowd
data_4['fastk'] = fastk
data_4['fastd'] = fastd
data_4['fastk_rsi'] = fastk_rsi
data_4['fastd_rsi'] = fastd_rsi
# 6) 기타 자주 사용되는 것들
# CCI (Commodity Channel Index), williams'%R, parabolic SAR
# ADX (Average Directional Movement Index)
# plusDI(Plus Directional Indicator), plusDM Plus Directional Movement)
# ATR (Average True Range), OBV (On Balance Volume) 거래량 분석을 통한 주가분석, Variance
data_4['cci'] = ta.CCI(data_4.high,
data_4.low,
data_4.close,
timeperiod=14)
data_4['willR'] = ta.WILLR(data_4.high,
data_4.low,
data_4.close,
timeperiod=14)
data_4['sar'] = ta.SAR(data_4.high, data_4.low, acceleration=0, maximum=0)
data_4['adx'] = ta.ADX(data_4.high,
data_4.low,
data_4.close,
timeperiod=14)
data_4['plus_di'] = ta.PLUS_DI(data_4.high,
data_4.low,
data_4.close,
timeperiod=14)
data_4['plus_dm'] = ta.PLUS_DM(data_4.high, data_4.low, timeperiod=14)
data_4['atr'] = ta.ATR(data_4.high,
data_4.low,
data_4.close,
timeperiod=14)
data_4['obv'] = ta.OBV(data_4.close, data_4.volume)
data_4['var'] = ta.VAR(data_4.close, timeperiod=5, nbdev=1)
# 7) Pattern REcognition
# Investopedia "The 5 Most Powerful Candlestick Patterns"
# Three Line Strike, Three Black Crows
# Evening Star, Abandoned Baby
# df4['line_str'] = ta.CDL3LINESTRIKE(df4.open, df4.high, df4.low, df4.close)
# df4['blk_crw'] = ta.CDL3BLACKCROWS(df4.open, df4.high, df4.low, df4.close)
# df4['evn_star'] = ta.CDLEVENINGSTAR(df4.open, df4.high, df4.low, df4.close, penetration=0)
# df4['abn_baby'] = ta.CDLABANDONEDBABY(df4.open, df4.high, df4.low, df4.close, penetration=0)
scaling_col = [column for column in data_4.columns]
for i in range(6):
scaling_col.pop(0)
data_rev = data_4.copy()
feature_scaling(data_rev, scaling_strategy="z-score", column=scaling_col)
return data_rev
def extract_by_type(feature_type,
open_prices=None,
close_prices=None,
high_prices=None,
low_prices=None):
# This function defines the features which are categorized with supporting technical indicators
if feature_type == 'ROCP':
rocp1 = talib.ROCP(close_prices, timeperiod=1)
rocp2 = talib.ROCP(close_prices, timeperiod=2)
feature.append(rocp1)
feature.append(rocp2)
if feature_type == 'OROCP':
orocp = talib.ROCP(open_prices, timeperiod=1)
feature.append(orocp)
if feature_type == 'HROCP':
hrocp = talib.ROCP(high_prices, timeperiod=1)
feature.append(hrocp)
if feature_type == 'LROCP':
lrocp = talib.ROCP(low_prices, timeperiod=1)
feature.append(lrocp)
if feature_type == 'MACD':
macd, signal, hist = talib.MACD(close_prices,
fastperiod=12,
slowperiod=26,
signalperiod=9)
norm_macd = numpy.nan_to_num(macd) / math.sqrt(
numpy.var(numpy.nan_to_num(macd)))
norm_signal = numpy.nan_to_num(signal) / math.sqrt(
numpy.var(numpy.nan_to_num(signal)))
norm_hist = numpy.nan_to_num(hist) / math.sqrt(
numpy.var(numpy.nan_to_num(hist)))
macdrocp = talib.ROCP(norm_macd + numpy.max(norm_macd) -
numpy.min(norm_macd),
timeperiod=1)
signalrocp = talib.ROCP(norm_signal + numpy.max(norm_signal) -
numpy.min(norm_signal),
timeperiod=1)
histrocp = talib.ROCP(norm_hist + numpy.max(norm_hist) -
numpy.min(norm_hist),
timeperiod=1)
feature.append(norm_macd)
feature.append(norm_signal)
feature.append(norm_hist)
feature.append(macdrocp)
feature.append(signalrocp)
feature.append(histrocp)
if feature_type == 'RSI':
rsi6 = talib.RSI(close_prices, timeperiod=6)
rsi12 = talib.RSI(close_prices, timeperiod=12)
rsi24 = talib.RSI(close_prices, timeperiod=24)
rsi6rocp = talib.ROCP(rsi6 + 100., timeperiod=1)
rsi12rocp = talib.ROCP(rsi12 + 100., timeperiod=1)
rsi24rocp = talib.ROCP(rsi24 + 100., timeperiod=1)
feature.append(rsi6 / 100.0 - 0.5)
feature.append(rsi12 / 100.0 - 0.5)
feature.append(rsi24 / 100.0 - 0.5)
feature.append(rsi6rocp)
feature.append(rsi12rocp)
feature.append(rsi24rocp)
if feature_type == 'UO':
ult_osc = talib.ULTOSC(high_prices,
low_prices,
close_prices,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
feature.append(ult_osc / 100.0 - 0.5)
if feature_type == 'BOLL':
upperband, middleband, lowerband = talib.BBANDS(close_prices,
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)
feature.append((upperband - close_prices) / close_prices)
feature.append((middleband - close_prices) / close_prices)
feature.append((lowerband - close_prices) / close_prices)
if feature_type == 'MA':
ma5 = talib.MA(close_prices, timeperiod=5)
ma10 = talib.MA(close_prices, timeperiod=10)
ma20 = talib.MA(close_prices, timeperiod=20)
ma25 = talib.MA(close_prices, timeperiod=25)
ma30 = talib.MA(close_prices, timeperiod=30)
ma40 = talib.MA(close_prices, timeperiod=40)
ma50 = talib.MA(close_prices, timeperiod=50)
ma60 = talib.MA(close_prices, timeperiod=60)
ma5rocp = talib.ROCP(ma5, timeperiod=1)
ma10rocp = talib.ROCP(ma10, timeperiod=1)
ma20rocp = talib.ROCP(ma20, timeperiod=1)
ma25rocp = talib.ROCP(ma25, timeperiod=1)
ma30rocp = talib.ROCP(ma30, timeperiod=1)
ma40rocp = talib.ROCP(ma40, timeperiod=1)
ma50rocp = talib.ROCP(ma50, timeperiod=1)
ma60rocp = talib.ROCP(ma60, timeperiod=1)
feature.append(ma5rocp)
feature.append(ma10rocp)
feature.append(ma20rocp)
feature.append(ma25rocp)
feature.append(ma30rocp)
feature.append(ma40rocp)
feature.append(ma50rocp)
feature.append(ma60rocp)
feature.append((ma5 - close_prices) / close_prices)
feature.append((ma10 - close_prices) / close_prices)
feature.append((ma20 - close_prices) / close_prices)
feature.append((ma25 - close_prices) / close_prices)
feature.append((ma30 - close_prices) / close_prices)
feature.append((ma40 - close_prices) / close_prices)
feature.append((ma50 - close_prices) / close_prices)
feature.append((ma60 - close_prices) / close_prices)
if feature_type == 'STOCH':
slow_stoch_k, slow_stoch_d = talib.STOCH(high_prices,
low_prices,
close_prices,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
fast_stoch_k, fast_stoch_d = talib.STOCHF(high_prices,
low_prices,
close_prices,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
fast_rsi_k, fast_rsi_d = talib.STOCHRSI(close_prices,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
feature.append(slow_stoch_k / 100.0 - 0.5)
feature.append(slow_stoch_d / 100.0 - 0.5)
feature.append(fast_stoch_k / 100.0 - 0.5)
feature.append(fast_stoch_d / 100.0 - 0.5)
feature.append(fast_rsi_k / 100.0 - 0.5)
feature.append(fast_rsi_d / 100.0 - 0.5)
if feature_type == 'AO':
median_price = (high_prices + low_prices) / 2
ao = talib.SMA(median_price, 5) - talib.SMA(median_price, 34)
feature.append(ao)
if feature_type == 'ROC':
roc5 = talib.ROC(close_prices, timeperiod=5)
roc10 = talib.ROC(close_prices, timeperiod=10)
roc20 = talib.ROC(close_prices, timeperiod=20)
roc25 = talib.ROC(close_prices, timeperiod=25)
feature.append(roc5)
feature.append(roc10)
feature.append(roc20)
feature.append(roc25)
if feature_type == 'WILLR':
willr = talib.WILLR(high_prices,
low_prices,
close_prices,
timeperiod=14)
feature.append(willr / 100.0 - 0.5)
return feature
def techIndi(self):
try:
daySqlOra = "SELECT NVL(MAX(SUBSTR(TRADE_TIME,1,8)),TO_CHAR(SYSDATE,'YYYYMMDD')) YMD FROM TRADE_TECH " \
"WHERE SUBSTR(TRADE_TIME,1,8) <> '%s' " \
% datetime.datetime.today().strftime("%Y%m%d")
dateD = self.kiwoom.selectDBOra(daySqlOra)
dateS = dateD[0][0]
except Exception as e:
e.msg = "techindi daySql 에러"
print(e)
try:
sqlOra71 = "SELECT DISTINCT TRIM(A.STOCK_CODE) STOCK_CODE " \
"FROM TRADE_HOLD A " \
"WHERE A.YMD >= '20200401' " \
" AND A.REQ_VOLUME <> 0" \
"UNION " \
"SELECT DISTINCT TRIM(B.STOCK_CODE) STOCK_CODE " \
"FROM TRADE_LIST B " \
"WHERE B.YMD >= '20200401' "
summaryG = self.kiwoom.selectDBOra(sqlOra71)
except Exception as e:
e.msg = "techIndi Sql 에러"
print(e)
print("-----------------------------------------------------------------")
print("["+ datetime.datetime.today().strftime("%Y-%m-%d %H:%M:%S") + "] " + "Process2 : 보조지표 만들기 시작!" )
try:
if len(summaryG) > 0:
for row_data in summaryG:
try:
sql = "SELECT RN, STOCK_CODE, TRADE_TIME, START_PRICE, HIGH_PRICE, LOW_PRICE, CURRENT_PRICE, VOLUME " \
"FROM ( " \
" SELECT A.*, ROW_NUMBER() OVER(PARTITION BY STOCK_CODE ORDER BY TRADE_TIME DESC) RN" \
" , ROW_NUMBER() OVER(PARTITION BY STOCK_CODE ORDER BY TRADE_TIME ASC) RN2" \
" FROM TRADE_CRR_BACK A" \
" WHERE 1=1 " \
" AND A.STOCK_CODE = '%s' " \
" AND A.STOCK_CODE NOT IN ('155960','900090') " \
" ) X " % row_data[0]
sql2 = "WHERE RN BETWEEN 1 AND 800 " \
"ORDER BY RN2"
summaryG = self.kiwoom.selectDBOra(sql + sql2)
except Exception as e:
print(e)
df = pd.DataFrame(summaryG)
try:
if len(df) > 0:
df.columns = ["rn", "stockCd", "tradeTime", "open", "high", "low", "close", "volume"]
# cloF = pd.Series(df.iloc[len(df) - 1]['open'], name='close') # 3분봉 종가 만들어주는 로직
# clo = pd.Series(df.iloc[1:len(df)]['open'], name='close')
# clo = pd.DataFrame(clo.append(cloF))
# clo = clo.reset_index(drop=True)
#
# df = df.join(clo)
macd, macdsignal, macdhist = talib.MACD(df['close'], fastperiod=12, slowperiod=26,
signalperiod=9)
slowk, slowd = talib.STOCH(df['high'], df['low'], df['close'], fastk_period=5, slowk_period=3,
slowk_matype=3,
slowd_period=3, slowd_matype=0)
fastk, fastd = talib.STOCHF(df['high'], df['low'], df['close'], fastk_period=20,
fastd_period=10)
sma5 = talib.SMA(df['close'], 5)
sma10 = talib.SMA(df['close'], 10)
sma20 = talib.SMA(df['close'], 20)
sma60 = talib.SMA(df['close'], 60)
sma120 = talib.SMA(df['close'], 120)
cci = talib.CCI(df['high'], df['low'], df['close'], 20)
rsi = talib.RSI(df['close'], 14)
roc1 = talib.ROC(df['close'], 1)
roc5 = talib.ROC(df['close'], 5)
rocr1 = talib.ROCR(df['close'], 1)
rocr5 = talib.ROCR(df['close'], 5)
df = df.join(pd.DataFrame(pd.Series(macd, name="MACD")))
df = df.join(pd.DataFrame(pd.Series(macdsignal, name="MACDSIG")))
df = df.join(pd.DataFrame(pd.Series(slowd, name="SLOWD")))
df = df.join(pd.DataFrame(pd.Series(fastd, name="FASTD")))
df = df.join(pd.DataFrame(pd.Series(sma5, name="SMA5")))
df = df.join(pd.DataFrame(pd.Series(sma10, name="SMA10")))
df = df.join(pd.DataFrame(pd.Series(sma20, name="SMA20")))
df = df.join(pd.DataFrame(pd.Series(sma60, name="SMA60")))
df = df.join(pd.DataFrame(pd.Series(sma120, name="SMA120")))
df = df.join(pd.DataFrame(pd.Series(cci, name="CCI")))
df = df.join(pd.DataFrame(pd.Series(rsi, name="RSI")))
df = df.join(pd.DataFrame(pd.Series(roc1, name="ROC1")))
df = df.join(pd.DataFrame(pd.Series(roc5, name="ROC5")))
df = df.join(pd.DataFrame(pd.Series(rocr1, name="ROCR1")))
df = df.join(pd.DataFrame(pd.Series(rocr5, name="ROCR5")))
df = df.fillna(0)
try:
for i in range(4, len(df)):
if df.iloc[i]['SMA10'] == 0:
dis10 = 0
else:
dis10 = float(df.iloc[i]['close']) / float(df.iloc[i]['SMA10']) * 100
if df.iloc[i]['SMA20'] == 0:
dis20 = 0
else:
dis20 = float(df.iloc[i]['close']) / float(df.iloc[i]['SMA20']) * 100
file = open("C:/Users/jini/PycharmProjects/stock/sql/sqlOra72_1.sql", 'r') # TRADE_TECH에 데이터 넣기
sqlOra72 = ''
line = file.readline()
while line:
sqlOra72 += ' ' + line.strip('\n').strip('\t')
line = file.readline()
file.close()
sqlOra72 = sqlOra72 % (
df.iloc[i]['tradeTime'], df.iloc[i]['stockCd']
, df.iloc[i - 3]['high'], df.iloc[i - 2]['high'], df.iloc[i - 1]['high']
, df.iloc[i]['high'], df.iloc[i - 3]['low'], df.iloc[i - 2]['low'],
df.iloc[i - 1]['low']
, df.iloc[i]['low'], df.iloc[i - 3]['open'], df.iloc[i - 2]['open'],
df.iloc[i - 1]['open']
, df.iloc[i]['open'], df.iloc[i - 3]['close'], df.iloc[i - 2]['close']
, df.iloc[i - 1]['close'], df.iloc[i]['close'], df.iloc[i - 3]['volume']
, df.iloc[i - 2]['volume'], df.iloc[i - 1]['volume'],
df.iloc[i]['volume']
, (df.iloc[i - 2]['high'] - df.iloc[i - 3]['high']) / df.iloc[i - 3][
'high']
, (df.iloc[i - 1]['high'] - df.iloc[i - 2]['high']) / df.iloc[i - 2][
'high']
, (df.iloc[i]['high'] - df.iloc[i - 1]['high']) / df.iloc[i - 1]['high']
, (df.iloc[i]['high'] - df.iloc[i - 3]['high']) / df.iloc[i - 3]['high']
, (df.iloc[i]['high'] - df.iloc[i - 2]['high']) / df.iloc[i - 2]['high']
, (df.iloc[i - 2]['low'] - df.iloc[i - 3]['low']) / df.iloc[i - 3]['low']
, (df.iloc[i - 1]['low'] - df.iloc[i - 2]['low']) / df.iloc[i - 2]['low']
, (df.iloc[i]['low'] - df.iloc[i - 1]['low']) / df.iloc[i - 1]['low']
, (df.iloc[i]['low'] - df.iloc[i - 3]['low']) / df.iloc[i - 3]['low']
, (df.iloc[i]['low'] - df.iloc[i - 2]['low']) / df.iloc[i - 2]['low']
, df.iloc[i]['SMA5'], df.iloc[i]['SMA10'], df.iloc[i]['SMA20'], df.iloc[i]['SMA60']
, df.iloc[i]['SMA120'], df.iloc[i]['RSI'], df.iloc[i]['CCI']
, float(dis10)
, float(dis20)
, df.iloc[i]['MACD'], df.iloc[i]['MACDSIG'], df.iloc[i]['SLOWD'], df.iloc[i]['FASTD']
, (max(df.iloc[i]['high'],df.iloc[i-1]['high'],df.iloc[i-2]['high']) - min(df.iloc[i]['low'],df.iloc[i-1]['low'],df.iloc[i-2]['low']))
/ min(df.iloc[i]['low'],df.iloc[i-1]['low'],df.iloc[i-2]['low']) * 100
, df.iloc[i]['MACD'] - df.iloc[i]['MACDSIG']
, (df.iloc[i]['close'] - df.iloc[i]['high']) * 100 / df.iloc[i]['high']
, (df.iloc[i]['close'] - max(df.iloc[i]['high'],df.iloc[i-1]['high'],df.iloc[i-2]['high'],df.iloc[i-3]['high'])) * 100 / max(df.iloc[i]['high'],df.iloc[i-1]['high'],df.iloc[i-2]['high'],df.iloc[i-3]['high'])
, (df.iloc[i]['open'] - max(df.iloc[i]['high'],df.iloc[i-1]['high'],df.iloc[i-2]['high'],df.iloc[i-3]['high'])) * 100 / max(df.iloc[i]['high'],df.iloc[i-1]['high'],df.iloc[i-2]['high'],df.iloc[i-3]['high'])
, (df.iloc[i]['close'] - min(df.iloc[i]['low'],df.iloc[i-1]['low'],df.iloc[i-2]['low'])) * 100 / min(df.iloc[i]['low'],df.iloc[i-1]['low'],df.iloc[i-2]['low'])
, df.iloc[i]['tradeTime'])
try:
self.kiwoom.updateDBOra(sqlOra72)
except Exception as e:
print(e)
except Exception as e:
print(e)
except Exception as e:
print(e)
except Exception as e:
print(e)
print("["+ datetime.datetime.today().strftime("%Y-%m-%d %H:%M:%S") + "] " + "Process2 : 보조지표 만들기 끝!" )
def execute(self):
return np.array([
self.open,
self.high,
self.low,
self.close,
self.volume,
talib.HT_DCPERIOD(self.close),
talib.HT_DCPHASE(self.close),
talib.HT_PHASOR(self.close)[0],
talib.HT_PHASOR(self.close)[1],
talib.HT_SINE(self.close)[0],
talib.HT_SINE(self.close)[1],
talib.HT_TRENDMODE(self.close),
talib.ADX(self.high, self.low, self.close),
talib.ADXR(self.high, self.low, self.close),
talib.APO(self.close),
talib.AROON(self.high, self.low)[0],
talib.AROON(self.high, self.low)[1],
talib.AROONOSC(self.high, self.low),
talib.BOP(self.open, self.high, self.low, self.close),
talib.CCI(self.high, self.low, self.close),
talib.CMO(self.close),
talib.DX(self.high, self.low, self.close),
talib.MACD(self.close)[0],
talib.MACD(self.close)[1],
talib.MACD(self.close)[2],
talib.MACDEXT(self.close)[0],
talib.MACDEXT(self.close)[1],
talib.MACDEXT(self.close)[2],
talib.MACDFIX(self.close)[0],
talib.MACDFIX(self.close)[1],
talib.MACDFIX(self.close)[2],
talib.MFI(self.high, self.low, self.close, self.volume),
talib.MINUS_DI(self.high, self.low, self.close),
talib.MINUS_DM(self.high, self.low),
talib.MOM(self.close),
talib.PLUS_DI(self.high, self.low, self.close),
talib.PLUS_DM(self.high, self.low),
talib.PPO(self.close),
talib.ROC(self.close),
talib.ROCP(self.close),
talib.ROCR(self.close),
talib.ROCR100(self.close),
talib.RSI(self.close),
talib.STOCH(self.high, self.low, self.close)[0],
talib.STOCH(self.high, self.low, self.close)[1],
talib.STOCHF(self.high, self.low, self.close)[0],
talib.STOCHF(self.high, self.low, self.close)[1],
talib.STOCHRSI(self.close)[0],
talib.STOCHRSI(self.close)[1],
talib.TRIX(self.close),
talib.ULTOSC(self.high, self.low, self.close),
talib.WILLR(self.high, self.low, self.close),
talib.BBANDS(self.close)[0],
talib.BBANDS(self.close)[1],
talib.BBANDS(self.close)[2],
talib.DEMA(self.close),
talib.EMA(self.close),
talib.HT_TRENDLINE(self.close),
talib.KAMA(self.close),
talib.MA(self.close),
talib.MAMA(self.close)[0],
talib.MAMA(self.close)[1],
talib.MIDPOINT(self.close),
talib.MIDPRICE(self.high, self.low),
talib.SAR(self.high, self.low),
talib.SAREXT(self.high, self.low),
talib.T3(self.close),
talib.TEMA(self.close),
talib.TRIMA(self.close),
talib.WMA(self.close),
talib.AVGPRICE(self.open, self.high, self.low, self.close),
talib.MEDPRICE(self.high, self.low),
talib.TYPPRICE(self.high, self.low, self.close),
talib.WCLPRICE(self.high, self.low, self.close),
talib.ATR(self.high, self.low, self.close),
talib.NATR(self.high, self.low, self.close),
talib.TRANGE(self.high, self.low, self.close),
talib.AD(self.high, self.low, self.close, self.volume),
talib.ADOSC(self.high, self.low, self.close, self.volume),
talib.OBV(self.close, self.volume),
talib.CDL2CROWS(self.open, self.high, self.low, self.close),
talib.CDL3BLACKCROWS(self.open, self.high, self.low, self.close),
talib.CDL3INSIDE(self.open, self.high, self.low, self.close),
talib.CDL3LINESTRIKE(self.open, self.high, self.low, self.close),
talib.CDL3OUTSIDE(self.open, self.high, self.low, self.close),
talib.CDL3STARSINSOUTH(self.open, self.high, self.low, self.close),
talib.CDL3WHITESOLDIERS(self.open, self.high, self.low,
self.close),
talib.CDLABANDONEDBABY(self.open, self.high, self.low, self.close),
talib.CDLADVANCEBLOCK(self.open, self.high, self.low, self.close),
talib.CDLBELTHOLD(self.open, self.high, self.low, self.close),
talib.CDLBREAKAWAY(self.open, self.high, self.low, self.close),
talib.CDLCLOSINGMARUBOZU(self.open, self.high, self.low,
self.close),
talib.CDLCONCEALBABYSWALL(self.open, self.high, self.low,
self.close),
talib.CDLCOUNTERATTACK(self.open, self.high, self.low, self.close),
talib.CDLDARKCLOUDCOVER(self.open, self.high, self.low,
self.close),
talib.CDLDOJI(self.open, self.high, self.low, self.close),
talib.CDLDOJISTAR(self.open, self.high, self.low, self.close),
talib.CDLDRAGONFLYDOJI(self.open, self.high, self.low, self.close),
talib.CDLENGULFING(self.open, self.high, self.low, self.close),
talib.CDLEVENINGDOJISTAR(self.open, self.high, self.low,
self.close),
talib.CDLEVENINGSTAR(self.open, self.high, self.low, self.close),
talib.CDLGAPSIDESIDEWHITE(self.open, self.high, self.low,
self.close),
talib.CDLGRAVESTONEDOJI(self.open, self.high, self.low,
self.close),
talib.CDLHAMMER(self.open, self.high, self.low, self.close),
talib.CDLHANGINGMAN(self.open, self.high, self.low, self.close),
talib.CDLHARAMI(self.open, self.high, self.low, self.close),
talib.CDLHARAMICROSS(self.open, self.high, self.low, self.close),
talib.CDLHIGHWAVE(self.open, self.high, self.low, self.close),
talib.CDLHIKKAKE(self.open, self.high, self.low, self.close),
talib.CDLHIKKAKEMOD(self.open, self.high, self.low, self.close),
talib.CDLHOMINGPIGEON(self.open, self.high, self.low, self.close),
talib.CDLIDENTICAL3CROWS(self.open, self.high, self.low,
self.close),
talib.CDLINNECK(self.open, self.high, self.low, self.close),
talib.CDLINVERTEDHAMMER(self.open, self.high, self.low,
self.close),
talib.CDLKICKING(self.open, self.high, self.low, self.close),
talib.CDLKICKINGBYLENGTH(self.open, self.high, self.low,
self.close),
talib.CDLLADDERBOTTOM(self.open, self.high, self.low, self.close),
talib.CDLLONGLEGGEDDOJI(self.open, self.high, self.low,
self.close),
talib.CDLLONGLINE(self.open, self.high, self.low, self.close),
talib.CDLMARUBOZU(self.open, self.high, self.low, self.close),
talib.CDLMATCHINGLOW(self.open, self.high, self.low, self.close),
talib.CDLMATHOLD(self.open, self.high, self.low, self.close),
talib.CDLMORNINGDOJISTAR(self.open, self.high, self.low,
self.close),
talib.CDLMORNINGSTAR(self.open, self.high, self.low, self.close),
talib.CDLONNECK(self.open, self.high, self.low, self.close),
talib.CDLPIERCING(self.open, self.high, self.low, self.close),
talib.CDLRICKSHAWMAN(self.open, self.high, self.low, self.close),
talib.CDLRISEFALL3METHODS(self.open, self.high, self.low,
self.close),
talib.CDLSEPARATINGLINES(self.open, self.high, self.low,
self.close),
talib.CDLSHOOTINGSTAR(self.open, self.high, self.low, self.close),
talib.CDLSHORTLINE(self.open, self.high, self.low, self.close),
talib.CDLSPINNINGTOP(self.open, self.high, self.low, self.close),
talib.CDLSTALLEDPATTERN(self.open, self.high, self.low,
self.close),
talib.CDLSTICKSANDWICH(self.open, self.high, self.low, self.close),
talib.CDLTAKURI(self.open, self.high, self.low, self.close),
talib.CDLTASUKIGAP(self.open, self.high, self.low, self.close),
talib.CDLTHRUSTING(self.open, self.high, self.low, self.close),
talib.CDLTRISTAR(self.open, self.high, self.low, self.close),
talib.CDLUNIQUE3RIVER(self.open, self.high, self.low, self.close),
talib.CDLUPSIDEGAP2CROWS(self.open, self.high, self.low,
self.close),
talib.CDLXSIDEGAP3METHODS(self.open, self.high, self.low,
self.close),
]).transpose()
def execute(self):
ema = talib.EMA(self.close, self.ema_period)
rsi = talib.RSI(self.close, self.rsi_period)
stoch_K, stoch_D = talib.STOCHF(self.high,
self.low,
self.close,
fastk_period=self.stoch_period)
macd, macdsignal, macdhist = talib.MACD(self.close)
sar = talib.SAR(self.high, self.low)
mom = talib.MOM(self.close, self.mom_period)
adx = talib.ADX(self.high, self.low, self.close, self.adx_period)
willr = talib.WILLR(self.high, self.low, self.close, self.willr_period)
cci = talib.CCI(self.high, self.low, self.close, self.cci_period)
roc = talib.ROC(self.close, self.roc_period)
stochrsi_K, stochrsi_D = talib.STOCHRSI(self.close,
self.stochrsi_period)
trix = talib.TRIX(self.close, self.trix_period)
mfi = talib.MFI(self.high, self.low, self.close, self.volume,
self.mfi_period)
ultosc = talib.ULTOSC(self.high, self.low, self.close,
self.ultosc_1_period, self.ultosc_2_period,
self.ultosc_3_period)
aroon_down, aroon_up = talib.AROON(self.high, self.low,
self.aroon_period)
aroonosc = talib.AROONOSC(self.high, self.low, self.aroonosc_period)
atr = talib.ATR(self.high, self.low, self.close, self.atr_period)
ad = talib.AD(self.high, self.low, self.close, self.volume)
obv = talib.OBV(self.close, self.volume)
adosc = talib.ADOSC(self.high, self.low, self.close, self.volume,
self.adoscfast_period, self.adoscslow_period)
spread_ema = ema - self.close
spread_stoch = stoch_K - stoch_D
spread_stochrsi = stochrsi_K - stochrsi_D
spread_aroon = aroon_up - aroon_down
return np.array([
self.close,
spread_ema,
rsi,
macd,
macdhist,
stoch_K,
spread_stoch,
sar,
mom,
adx,
willr,
cci,
roc,
spread_stochrsi,
trix,
mfi,
ultosc,
aroon_down,
aroon_up,
spread_aroon,
aroonosc,
atr,
ad,
obv,
adosc,
]).transpose()
def STOCHF(data, **kwargs):
_check_talib_presence()
_, phigh, plow, pclose, _ = _extract_ohlc(data)
return talib.STOCHF(phigh, plow, pclose, **kwargs)
def STOCHF(DataFrame, fastk_period=5, fastd_period=3, fastd_matype=0):
fastk, fastd = talib.STOCHF(DataFrame.high.values, DataFrame.low.values, DataFrame.close.values,
fastk_period, fastd_period, fastd_matype)
return pd.DataFrame({'STOCHF_FASTK': fastk, 'STOCHF_FASTD': fastd}, index=DataFrame.index)
def add_momentum_indicators(data_list):
# (momentum) Indicators common for all Time-frames
for data in data_list:
# 1) ADX - Average Directional Movement Index
real = talib.ADX(data.High, data.Low, data.Close, timeperiod=14)
data['ADX_14'] = real
# 2) ADXR - Average Directional Movement Index Rating
real = talib.ADXR(data.High, data.Low, data.Close, timeperiod=14)
data['ADXR_14'] = real
# 3) APO - Absolute Price Oscillator
real = talib.APO(data.Close, fastperiod=12, slowperiod=26, matype=0)
data['APO'] = real
# 4) AROON - Aroon
aroondown, aroonup = talib.AROON(data.High, data.Low, timeperiod=14)
data['Aroon_down_14'] = aroondown
data['Aroon_up_14'] = aroonup
# 5) AROONOSC - Aroon Oscillator
real = talib.AROONOSC(data.High, data.Low, timeperiod=14)
data['AROONOSC_14'] = real
# 6) BOP - Balance Of Power
real = talib.BOP(data.Open, data.High, data.Low, data.Close)
data['BOP'] = real
# 7) CCI - Commodity Channel Index
real = talib.CCI(data.High, data.Low, data.Close, timeperiod=14)
data['CCI_14'] = real
# 8) CMO - Chande Momentum Oscillator
real = talib.CMO(data.Close, timeperiod=14)
data['CMO_14'] = real
# 9) DX - Directional Movement Index
real = talib.DX(data.High, data.Low, data.Close, timeperiod=14)
data['DMI_14'] = real
# 10) MACD - Moving Average Convergence/Divergence
macd, macdsignal, macdhist = talib.MACD(data.Close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
data['macd'] = macd
data['macd_sig'] = macdsignal
data['macd_hist'] = macdhist
# 11) MFI - Money Flow Index
real = talib.MFI(data.High,
data.Low,
data.Close,
data.Volume,
timeperiod=14)
data['MFT_14'] = real
# 12) MINUS_DI - Minus Directional Indicator
real = talib.MINUS_DI(data.High, data.Low, data.Close, timeperiod=14)
data['MINUS_DI_14'] = real
# 13) MINUS_DM - Minus Directional Movement
real = talib.MINUS_DM(data.High, data.Low, timeperiod=14)
data['MINUS_DM_14'] = real
# 14) MOM - Momentum
real = talib.MOM(data.Close, timeperiod=14)
data['MOM_14'] = real
# 15) PLUS_DI - Plus Directional Indicator
real = talib.PLUS_DI(data.High, data.Low, data.Close, timeperiod=14)
data['PLUS_DI_14'] = real
# 16) PLUS_DM - Plus Directional Movement
real = talib.PLUS_DM(data.High, data.Low, timeperiod=14)
data['PLUS_DM_14'] = real
# 17) PPO - Percentage Price Oscillator
real = talib.PPO(data.Close, fastperiod=12, slowperiod=26, matype=0)
data['PPO'] = real
# 18) ROC - Rate of change : ((price/prevPrice)-1)*100
real = talib.ROC(data.Close, timeperiod=14)
data['ROC_14'] = real
# 19) RSI - Relative Strength Index
real = talib.RSI(data.Close, timeperiod=14)
data['RSI_14'] = real
# 20) STOCH - Stochastic
slowk, slowd = talib.STOCH(data.High,
data.Low,
data.Close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
data['slowk'] = slowk
data['slowd'] = slowd
# 21) STOCHF - Stochastic Fast
fastk, fastd = talib.STOCHF(data.High,
data.Low,
data.Close,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
data['fastk'] = fastk
data['fastd'] = fastd
# 22) TRIX - 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
real = talib.TRIX(data.Close, timeperiod=20)
data['TRIX_20'] = real
# 23) ULTOSC - Ultimate Oscillator
real = talib.ULTOSC(data.High,
data.Low,
data.Close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
data['ULTOSC'] = real
# 24) WILLR - Williams' %R
real = talib.WILLR(data.High, data.Low, data.Close, timeperiod=14)
data['WILLR_14'] = real
data_weekly = data_list[6]
data_monthly = data_list[7]
data_15min = data_list[2]
data_daily = data_list[5]
data_60min = data_list[4]
data_1min = data_list[0]
data_5min = data_list[1]
data_30min = data_list[3]
# Create (momentum) indicators for a only to a particular timeframe here..
return data_list
def third_making_indicator(dataframe):
Open_lis = np.array(dataframe['open'], dtype='float')
High_lis = np.array(dataframe['high'], dtype='float')
Low_lis = np.array(dataframe['low'], dtype='float')
Clz_lis = np.array(dataframe['weigted_price'], dtype='float')
Vol_lis = np.array(dataframe['volume'], dtype='float')
##지표##
SMA_3_H = ta.SMA(High_lis, timeperiod=3)
SMA_3_L = ta.SMA(Low_lis, timeperiod=3)
SMA_3_C = ta.SMA(Clz_lis, timeperiod=3)
SMA_5_H = ta.SMA(High_lis, timeperiod=5)
SMA_5_L = ta.SMA(Low_lis, timeperiod=5)
SMA_5_C = ta.SMA(Clz_lis, timeperiod=5)
SMA_7_H = ta.SMA(High_lis, timeperiod=7)
SMA_7_L = ta.SMA(Low_lis, timeperiod=7)
SMA_7_C = ta.SMA(Clz_lis, timeperiod=7)
RSI_3_H = ta.RSI(High_lis, timeperiod=3)
RSI_3_L = ta.RSI(Low_lis, timeperiod=3)
RSI_3_C = ta.RSI(Clz_lis, timeperiod=3)
RSI_5_H = ta.RSI(High_lis, timeperiod=5)
RSI_5_L = ta.RSI(Low_lis, timeperiod=5)
RSI_5_C = ta.RSI(Clz_lis, timeperiod=5)
RSI_7_H = ta.RSI(High_lis, timeperiod=7)
RSI_7_L = ta.RSI(Low_lis, timeperiod=7)
RSI_7_C = ta.RSI(Clz_lis, timeperiod=7)
ADX = ta.ADX(High_lis, Low_lis, Clz_lis, timeperiod=7)
ADXR = ta.ADXR(High_lis, Low_lis, Clz_lis, timeperiod=7)
ADX_2 = ta.ADX(High_lis, Low_lis, Clz_lis, timeperiod=5)
ADXR_2 = ta.ADXR(High_lis, Low_lis, Clz_lis, timeperiod=5)
Aroondown, Aroonup = ta.AROON(High_lis, Low_lis, timeperiod=14)
Aroondown_2, Aroonup_2 = ta.AROON(High_lis, Low_lis, timeperiod=7)
Aroonosc = ta.AROONOSC(High_lis, Low_lis, timeperiod=14)
Aroonosc_2 = ta.AROONOSC(High_lis, Low_lis, timeperiod=7)
BOP = ta.BOP(Open_lis, High_lis, Low_lis, Clz_lis)
CMO = ta.CMO(Clz_lis, timeperiod=7)
DX = ta.DX(High_lis, Low_lis, Clz_lis, timeperiod=7)
MINUS_DI = ta.MINUS_DI(High_lis, Low_lis, Clz_lis, timeperiod=7)
PLUSDI = ta.PLUS_DI(High_lis, Low_lis, Clz_lis, timeperiod=7)
PPO = ta.PPO(Clz_lis, fastperiod=12, slowperiod=26, matype=0)
PPO_2 = ta.PPO(Clz_lis, fastperiod=6, slowperiod=13, matype=0)
ROCR100 = ta.ROCR100(Clz_lis, timeperiod=10)
ROCR100_2 = ta.ROCR100(Clz_lis, timeperiod=5)
STOCHF_f, _ = ta.STOCHF(High_lis,
Low_lis,
Clz_lis,
fastk_period=3,
fastd_period=2,
fastd_matype=0)
Fastk, _ = ta.STOCHRSI(Clz_lis,
timeperiod=7,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
TRIX = ta.TRIX(Clz_lis, timeperiod=7)
ULTOSC = ta.ULTOSC(High_lis,
Low_lis,
Clz_lis,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
WILLR = ta.WILLR(High_lis, Low_lis, Clz_lis, timeperiod=14)
WILLR_2 = ta.WILLR(High_lis, Low_lis, Clz_lis, timeperiod=7)
NATR = ta.NATR(High_lis, Low_lis, Clz_lis, timeperiod=7)
integer = ta.HT_TRENDMODE(Clz_lis)
#append
dataframe['SMA_3_H'] = SMA_3_H
dataframe['SMA_3_L'] = SMA_3_L
dataframe['SMA_3_C'] = SMA_3_C
dataframe['SMA_5_H'] = SMA_5_H
dataframe['SMA_5_L'] = SMA_5_L
dataframe['SMA_5_C'] = SMA_5_C
dataframe['SMA_7_H'] = SMA_7_H
dataframe['SMA_7_L'] = SMA_7_L
dataframe['SMA_7_C'] = SMA_7_C
dataframe['RSI_3_H'] = (RSI_3_H / 100.) * 2 - 1.
dataframe['RSI_3_L'] = (RSI_3_L / 100.) * 2 - 1.
dataframe['RSI_3_C'] = (RSI_3_C / 100.) * 2 - 1.
dataframe['RSI_5_H'] = (RSI_5_H / 100.) * 2 - 1.
dataframe['RSI_5_L'] = (RSI_5_L / 100.) * 2 - 1.
dataframe['RSI_5_C'] = (RSI_5_C / 100.) * 2 - 1.
dataframe['RSI_7_H'] = (RSI_7_H / 100.) * 2 - 1.
dataframe['RSI_7_L'] = (RSI_7_L / 100.) * 2 - 1.
dataframe['RSI_7_C'] = (RSI_7_C / 100.) * 2 - 1.
dataframe['ADX'] = (ADX / 100.) * 2 - 1.
dataframe['ADX_2'] = (ADX_2 / 100.) * 2 - 1.
dataframe['ADXR'] = (ADXR / 100.) * 2 - 1.
dataframe['ADXR_2'] = (ADXR_2 / 100.) * 2 - 1.
dataframe['Aroondown'] = (Aroondown / 100.) * 2 - 1
dataframe['Aroondown_2'] = (Aroondown_2 / 100.) * 2 - 1
dataframe['Aroonup'] = (Aroonup / 100.) * 2 - 1
dataframe['Aroonup_2'] = (Aroonup_2 / 100.) * 2 - 1
dataframe['Aroonosc'] = Aroonosc / 100.
dataframe['Aroonosc_2'] = Aroonosc_2 / 100.
dataframe['BOP'] = BOP
dataframe['CMO'] = CMO / 100.
dataframe['DX'] = (DX / 100.) * 2 - 1
dataframe['MINUS_DI'] = (MINUS_DI / 100.) * 2 - 1
dataframe['PLUSDI'] = (PLUSDI / 100.) * 2 - 1
dataframe['PPO'] = PPO
dataframe['PPO_2'] = PPO_2
dataframe['ROCR100'] = ((ROCR100 / 100.) - 1.0) * 100.
dataframe['STOCHF_f'] = (STOCHF_f / 100.) * 2 - 1
dataframe['Fastk'] = (Fastk / 100.) * 2 - 1
dataframe['TRIX'] = TRIX * 10.
dataframe['ULTOSC'] = (ULTOSC / 100.) * 2 - 1
dataframe['WILLR'] = (WILLR / 100.) * 2 + 1
dataframe['WILLR_2'] = (WILLR_2 / 100.) * 2 + 1
dataframe['NATR'] = NATR * 2 - 1
dataframe['integer'] = integer
return dataframe
def ExecuteTrendFollowing(self, exchange, moneta, tool, risultato):
#Attention: static stoploss and dynamic stoploss cannot be activated simultaneously
tool.attivazione_stoploss_statico = 0 #with zero not activated, with 1 activated
tool.attivazione_stoploss_dinamico = 1 # with zero not activated, with 1 activated
tool.profitto = 0.4 #minimum percentage of profit
tool.stoploss_1 = 0.8 #percentage of stoploss
tool.stoploss_2 = 2.0 #second level stoploss percentage (not active for now)
tool.commissioni = 0.25 #percentage of fees in buy and sell
tool.nrtransizioni = 2000 #do not use
tool.stoploss_dinamico_moltiplicatore = 0.997 #multiplier of the trailing stop
tool.stoploss_dinamico_formula = "row.close * tool.stoploss_dinamico_moltiplicatore" #formula per il calcolo dello stoploss dinamico (trailing stop)
tool.attivazione_stoploss_dinamico_limiti = 1 #with zero not activated, with 1 activated. It works if dynamic stoploss is activated
tool.stoploss_dinamico_min_profitto = 0.30 #minimum profit compared to lastbuy if activated stoploss_dinamico_limiti
tool.stoploss_dinamico_max_perdita = 2 #max loss in percentage compared to lastbuy if activated stoploss_dinamico_limiti
tool.hours = 36 #timedelta from now
tool.periodo = '5m' #candlestick timeframe
self.nome = " Test Strategy Trend Following "
candle = candlestick.Candlestick()
self.df1 = candle.getCandle(exchange, moneta, tool.hours, tool.periodo)
moneta = moneta
exchange = exchange
self.df1 = self.df1.apply(pd.to_numeric)
#-------------Indicators and oscillators
self.df1['rsi'] = ta.RSI(self.df1.close.values, timeperiod=14)
self.df1['adx'] = ta.ADX(self.df1['high'].values,
self.df1['low'].values,
self.df1['close'].values,
timeperiod=14)
# Bollinger bands with qtpylib--------------------------------------
""" bollinger = qt.bollinger_bands(qt.typical_price(self.df1), window=20, stds=2)
self.df1['lower'] = bollinger['lower']
self.df1['middle'] = bollinger['mid']
self.df1['upper'] = bollinger['upper'] """
#------------------------------------------------------------------
# Bollinger bands with TA library
self.df1['upper'], self.df1['middle'], self.df1['lower'] = ta.BBANDS(
self.df1['close'].values, timeperiod=5,nbdevup=1,nbdevdn=1, matype=1)
self.df1['upper2'], self.df1['middle2'], self.df1[
'lower2'] = ta.BBANDS(self.df1['close'].values, timeperiod=5,nbdevup=1.6,nbdevdn=1.6, matype=1)
self.df1['upper3'], self.df1['middle3'], self.df1[
'lower3'] = ta.BBANDS(self.df1['close'].values, timeperiod=5,nbdevup=2,nbdevdn=2, matype=1)
#------------------------------------------------------------------
self.df1['PrezzoMed'] = self.df1['close'].mean()
self.df1['STDDEV'] = ta.STDDEV(self.df1['close'].values,
timeperiod=15,
nbdev=1)
self.df1['macd'], self.df1['macdsignal'], self.df1[
'macdhist'] = ta.MACD(self.df1.close.values,
fastperiod=12,
slowperiod=26,
signalperiod=9)
self.df1['minus_di'] = ta.MINUS_DI(self.df1['high'].values,
self.df1['low'].values,
self.df1['close'].values,
timeperiod=25)
self.df1['plus_di'] = ta.PLUS_DI(self.df1['high'].values,
self.df1['low'].values,
self.df1['close'].values,
timeperiod=25)
self.df1['sar'] = ta.SAR(self.df1['high'].values,
self.df1['low'].values,
acceleration=0,
maximum=0)
self.df1['mom'] = ta.MOM(self.df1['close'].values, timeperiod=14)
self.df1['atr'] = ta.ATR(self.df1['high'].values,
self.df1['low'].values,
self.df1['close'].values,
timeperiod=14)
self.df1['ema'] = ta.EMA(self.df1['close'].values, timeperiod=20)
self.df1['ema1'] = ta.EMA(self.df1['close'].values, timeperiod=28)
self.df1['ema2'] = ta.EMA(self.df1['close'].values, timeperiod=35)
self.df1['ema3'] = ta.EMA(self.df1['close'].values, timeperiod=48)
self.df1['ema50'] = ta.EMA(self.df1['close'].values, timeperiod=50)
self.df1['dema20']= ta.DEMA(self.df1['close'].values, timeperiod=20)
self.df1['dema50']= ta.DEMA(self.df1['close'].values, timeperiod=50)
self.df1['tema20']= ta.TEMA(self.df1['close'].values, timeperiod=5)
self.df1['tema50']= ta.TEMA(self.df1['close'].values, timeperiod=20)
self.df1['cci'] = ta.CCI(self.df1['high'].values,
self.df1['low'].values,
self.df1['close'].values,
timeperiod=14)
#ta lib moving average
self.df1['shortma']= ta.SMA(self.df1['close'].values, timeperiod=10)
self.df1['longma']= ta.SMA(self.df1['close'].values, timeperiod=20)
# MFI
self.df1['mfi'] = ta.MFI(self.df1['high'].values,self.df1['low'].values, self.df1['close'].values, self.df1['volume'].values, timeperiod=14)
# Stoch
self.df1['slowk'],self.df1['slowd'] = ta.STOCH(self.df1['high'].values,self.df1['low'].values, self.df1['close'].values)
# Hammer: values [0, 100] - pattern recognised
self.df1['CDLHAMMER'] = ta.CDLHAMMER(self.df1['open'].values,self.df1['high'].values,self.df1['low'].values, self.df1['close'].values)
# Inverted Hammer: values
self.df1['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(self.df1['open'].values,self.df1['high'].values,self.df1['low'].values, self.df1['close'].values)
# Engulfing
self.df1['CDLENGULFING'] = ta.CDLENGULFING(self.df1['open'].values,self.df1['high'].values,self.df1['low'].values, self.df1['close'].values)
# Hiddake
self.df1['CDLHIKKAKE'] = ta.CDLHIKKAKE(self.df1['open'].values,self.df1['high'].values,self.df1['low'].values, self.df1['close'].values)
# Doji
self.df1['CDLDOJI'] = ta.CDLDOJI(self.df1['open'].values,self.df1['high'].values,self.df1['low'].values, self.df1['close'].values)
# DojiStar
self.df1['CDLDOJISTAR'] = ta.CDLDOJISTAR(self.df1['open'].values,self.df1['high'].values,self.df1['low'].values, self.df1['close'].values)
# Stoch fast
self.df1['fastk'], self.df1['fastd'] = ta.STOCHF(self.df1.high.values, self.df1.low.values, self.df1.close.values)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (self.df1['rsi'] - 50)
self.df1['fisher_rsi'] =(np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# EMA - Exponential Moving Average
self.df1['ema5'] = ta.EMA(self.df1.close.values, timeperiod=5)
self.df1['ema10'] = ta.EMA(self.df1.close.values, timeperiod=10)
self.df1['ema50'] = ta.EMA(self.df1.close.values, timeperiod=50)
self.df1['ema100'] = ta.EMA(self.df1.close.values, timeperiod=100)
# SMA - Simple Moving Average
self.df1['sma'] = ta.SMA(self.df1.close.values, timeperiod=40)
#-----------------------------------------------------------------------
self.df1['Sellsignal'] = 0
self.df1['Buysignal'] = 0
#---------------------example 1 strategy buy and sell ----------------------------------------
#-------------Buy conditions----------------------------------------------------------
""" self.df1.loc[(((
(self.df1['macd'] > self.df1['macdsignal']) &
(self.df1['cci'] <= -50.0)))),'BuySignal'] = 1 """
#-------------Sell conditions----------------------------------------------------------
""" self.df1.loc[(((
(self.df1['macd'] < self.df1['macdsignal']) &
(self.df1['cci'] >= 100.0)))),'SellSignal'] = -1 """
#------------------------example 2 strategy buy and sell---------------------------------------
#-------------Buy conditions----------------------------------------------------------
""" self.df1.loc[(((
(self.df1['adx'] > 25) &
(self.df1['mom'] < 0) &
(self.df1['minus_di'] > 25) &
(self.df1['plus_di'] < self.df1['minus_di'])))),'BuySignal'] = 1 """
#-------------Sell conditions----------------------------------------------------------
""" self.df1.loc[(((
(self.df1['adx'] > 25) &
(self.df1['mom'] > 0) &
(self.df1['minus_di'] > 25) &
(self.df1['plus_di'] > self.df1['minus_di'])))),'SellSignal'] = -1 """
# --------------------------------------------------------------------------
#example 3 strategy macd and midprice with qtpylib Sell & Buy
#-------------Sell conditions----------------------------------------------------------
self.df1.loc[(((
self.qt.crossed_below(self.df1['macd'], self.df1['macdsignal'])))), 'Sellsignal'] = -1
#-------------Buy conditions----------------------------------------------------------
self.df1.loc[(((
self.qt.crossed_above(self.df1['macd'], self.df1['macdsignal'])) & (self.df1['close'] < self.df1['PrezzoMed']))), 'Buysignal'] = 1
# --------------------------------------------------------------------------
#example 4 strategy Bollinger Bands with rsi
#-------------Buy conditions----------------------------------------------------------
""" self.df1.loc[
((self.df1['close'] < self.df1['lower']) &
(self.df1['rsi'] < 30)
),'Buysignal'] = 1
#-------------Sell conditions----------------------------------------------------------
self.df1.loc[
((self.df1['close']>self.df1['upper']) |
(self.df1['rsi'] > 70)
),'Sellsignal'] = -1 """
#---------------------------------------------------------------------------
#example 5 Bollinger Bands with rsi qtpylib library Sell & Buy
#-------------Buy conditions----------------------------------------------------------
""" self.df1.loc[
(
(self.df1['rsi'] < 30) &
(self.df1['close'] < self.df1['lower'])
),
'Buysignal'] = 1
#------------Sell conditions--------------------------------------------
self.df1.loc[
(
(self.df1['rsi'] > 70)
),
'Sellsignal'] = -1 """
# --------------------------------------------------------------------------
#example 6 strategy longma and shortma with qtpylib Sell & Buy
#-------------Sell conditions----------------------------------------------------------
""" self.df1.loc[(((
self.qt.crossed_below(self.df1['shortma'], self.df1['longma'])))), 'Sellsignal'] = -1
#-------------Buy and Sell conditions----------------------------------------------------------
self.df1.loc[(((
self.qt.crossed_above(self.df1['shortma'], self.df1['longma'])) & (self.df1['close'] < self.df1['PrezzoMed']))), 'Buysignal'] = 1
"""
#--------------------------------------------------------------------------
#example 7 strategy with qtpylib and TA-lib Sell & Buy
#-------------Buy conditions--------------------------------
""" self.df1.loc[
(
(self.df1['rsi'] < 28) &
(self.df1['rsi'] > 0) &
(self.df1['close'] < self.df1['sma']) &
(self.df1['fisher_rsi'] < -0.94) &
(self.df1['mfi'] < 16.0) &
(
(self.df1['ema50'] > self.df1['ema100']) |
(qt.crossed_above(self.df1['ema5'], self.df1['ema10']))
) &
(self.df1['fastd'] > self.df1['fastk']) &
(self.df1['fastd'] > 0)
),
'Buysignal'] = 1
#------------Sell conditions---------------------------------------------
self.df1.loc[
(
(self.df1['sar'] > self.df1['close']) &
(self.df1['fisher_rsi'] > 0.3)
),
'Sellsignal'] = -1 """
# ----------------------------------------------------------------------
#example 8 strategy - multiple indicators with pattern indicator HAMMER
#------------------Sell conditions--------------------------------------
""" self.df1.loc[
(
(self.df1['rsi'] < 30) &
(self.df1['slowk'] < 20) &
(self.df1['lower'] > self.df1['close']) &
(self.df1['CDLHAMMER'] == 100)
),
'Buysignal'] = 1
#---------------Buy conditions------------------------------------------
self.df1.loc[
(
(self.df1['sar'] > self.df1['close']) &
(self.df1['fisher_rsi'] > 0.3)
),
'Sellsignal'] = -1 """
# ----------------------------------------------------------------------
#example 9 strategy pattern strategy ENGULFING strategy Sell and Buy
#---------------Sell conditions------------------------------------------
""" print(self.df1['CDLENGULFING'].values)
self.df1.loc[
(
(self.df1['CDLENGULFING'].values < -99)
),
'Buysignal'] = 1
#---------------Buy conditions------------------------------------------
self.df1.loc[
(
(self.df1['CDLENGULFING'].values > 99)
),
'Sellsignal'] = -1 """
#---------------------------------------------------------------------------------
#example 10 strategy Bollinger Bands with rsi with technical indicators Sell & Buy
"""risultati_index_buy = self.df1.index[self.df1['CDLINVERTEDHAMMER'] == 100].tolist()
risultato_last_index = self.df1.index[-1]
if ((risultati_index_buy == None) or (risultato_last_index == None) or (risultato_last_index == '') or (len(risultati_index_buy) == 0)):
print('None Result')
else:
risultato_last_index = str(risultati_index_buy[-1])
risultato_last_index = risultato_last_index[:-3]
risultato_last_index = (str(risultato_last_index))[:-3] #rimozione dei tre caratteri
data1 = datetime.fromtimestamp(int(risultato_last_index))
data2 = datetime.fromtimestamp(int(risultato_last_index))
print(data2,data1)
print(self.df1)
#------------------Buy conditions----------------------------------
self.df1.loc[(
self.df1['CDLINVERTEDHAMMER'] == 100
),'CDLHIKKAKE_CLOSE'] = self.df1['close']
self.df1.loc[
(
(self.df1['close'] < self.df1['lower3'])
)
,'Buysignal'] = 1
#------------------Sell conditions----------------------------------
self.df1.loc[
(
self.qt.crossed_above(self.df1['dema20'], self.df1['dema50'])
),'Sellsignal'] = -1
"""
#----------------------------------------------------------------------
#example 11 strategy DOJISTAR strategy Sell and Buy
#---------------Buy conditions------------------------------------------
""" self.df1.loc[
(
(self.df1['CDLDOJISTAR'].values == -100)
),
'Buysignal'] = 1
#---------------Sell conditions----------------------------------------
self.df1.loc[
(
(self.df1['CDLHIKKAKE'].values == 200)
),
'Sellsignal'] = -1 """
#-----------------------------------------------------------------------
#example 12 strategy DEMA of EMA BuySignal and SellSignal
#---------------Buy conditions------------------------------------------
""" self.df1.loc[(((
self.qt.crossed_below(self.df1['ema50'], self.df1['dema200'])))), 'BuySignal'] = 1
#---------------Sell conditions------------------------------------------
self.df1.loc[
(
(self.df1['CDLHIKKAKE'].values > 99)
),
'Sellsignal'] = -1 """
#---------------------------trailing stoploss---------------------------
#example 1
#tool.stoploss_dinamico_formula = "row.close * tool.stoploss_dinamico_moltiplicatore" #formula for calculating dynamic stoploss (trailing stop)
#example 2
tool.stoploss_dinamico_formula = "row.ema3"
x = []
y = []
balance = []
lower_vect = []
lower2_vect = []
upper_vect = []
upper2_vect = []
pattern_vect = []
close_vect = []
buysignal_vect = []
sellsignal_vect = []
prezzmed_vect = []
stdev_vect = []
stoplosssignal_vect = []
stoplossprezzo_vect = []
ema_vect = []
ema_long_vect = []
ema_short_vect = []
date = []
volume_vect = []
macd_vect = []
macd_signal_vect = []
macd_hist_vect = []
atr_vect = []
rsi_vect = []
i = 0
for row in self.df1.itertuples():
a = row.Index / 1000.0
b = datetime.fromtimestamp(a)
self.stoploss = self.getStoploss(row, i, tool)
logging.info("-----------Loop line processing " + str(i))
logging.info("1) self.stoploss value = " + str(self.stoploss))
if (i > 0):
stoplossprezzo_vect.append(self.stoplossprezzo)
logging.info(
"2) Value of i greater than zero not first loop for i =" +
str(i))
logging.info("2.1) Value of self.stoplossprezzo = " +
str(self.stoplossprezzo))
else:
self.stoplossprezzo = row.close * 0.99
logging.info(
"2) Value of i equal to zero first loop for i = " + str(i))
stoplossprezzo_vect.append(self.stoplossprezzo)
logging.info("2.1) Value of self.stoplossprezzo = " +
str(self.stoplossprezzo))
if (self.stoploss == 1):
logging.info(
"3) self.stoploss value = 1 after getstoploss method ")
x.append(i)
y.append(row.close)
if (row.Sellsignal == 0
and self.stoploss == 0) or (row.Sellsignal == 1
and self.stoploss == 1):
logging.info("4) Value of self.stoploss before If for sell " +
str(self.stoploss))
logging.info("4.1) Value of row.Sellsignal " +
str(row.Sellsignal))
sellsignal_vect.append(np.nan)
stoplosssignal_vect.append(np.nan)
else:
if (self.stoploss == 1):
logging.info("5) self.stoploss value = 1 else before if ")
logging.info(
"5.1) Indication of sell in presence of self.stoploss = 1"
)
if (self.sell == 0) and (self.buy == 1):
logging.info(
"5.2) Sell with self.stoploss = 1 self.stoploss = "
+ str(self.stoploss))
logging.info(
"5.2.1) Sell with self.stoploss = 1 self.sell = " +
str(self.sell))
logging.info(
"5.2.2) Sell with self.stoploss = 1 self.buy = " +
str(self.buy))
#increases variable by 1 num_of_trades by one
self.nr_of_trades = self.nr_of_trades+1
sellsignal_vect.append(row.close)
stoplosssignal_vect.append(row.close)
percentualecommissioni = (
float(row.close) / 100) * float(tool.commissioni)
logging.info(
"5.3) Percentage of fees in presence of self.stoploss = 1"
+ str(percentualecommissioni))
balance.append(
[row.close - (percentualecommissioni), 1])
if ((row.close - percentualecommissioni) > self.lastbuy):
self.nr_positive_trades = self.nr_positive_trades +1
elif ((row.close - percentualecommissioni) < self.lastbuy):
self.nr_negative_trades = self.nr_negative_trades +1
else:
print('Non risulta positiva ne negativa')
self.totalecommissioni = self.totalecommissioni + percentualecommissioni
logging.info(
"5.4) Total fees in presence of self.stoploss = 1 "
+ str(self.totalecommissioni))
self.sell = 1
self.buy = 0
self.stoploss = 0
logging.info(
"5.5) Assign value zero to self.stoploss = " +
str(self.stoploss))
logging.info("5.6) Assign value 1 to self.sell = " +
str(self.sell))
logging.info("5.7) Assign value zero to self.buy = " +
str(self.buy))
else:
sellsignal_vect.append(np.nan)
stoplosssignal_vect.append(np.nan)
self.stoploss = 0
else:
stoplosssignal_vect.append(np.nan)
if ((self.sell == 0) and (self.buy == 1)
and (tool.attivazione_stoploss_dinamico == 0)):
logging.info(
"6.0) Sell normal not in the presence of dynamic stoploss"
)
logging.info("6.1) Value of self.sell: " +
str(self.sell))
logging.info("6.2) Value of self.buy: " +
str(self.buy))
logging.info(
"6.3) Dynamic stoploss activation parameter value: "
+ str(tool.attivazione_stoploss_dinamico))
price1 = float(self.lastbuy) + (
(float(self.lastbuy)) / 100) * float(tool.profitto)
logging.info("6.4) Value of self.lastbuy: " +
str(self.lastbuy))
logging.info("6.5) Price1 price of sell: " +
str(price1))
logging.info("6.6) Profit parameters: " +
str(tool.profitto))
if (float(row.close) > float(price1)):
logging.info(
"6.7) Sell in presence of row.close greater than value of price1"
)
logging.info("6.8) Value of row.close: " +
str(row.close))
logging.info("6.9) Value of sell price: " +
str(price1))
#increases variable by 1 num_of_trades by one
self.nr_positive_trades = self.nr_positive_trades +1
sellsignal_vect.append(row.close)
percentualecommissioni = (float(
row.close) / 100) * float(tool.commissioni)
logging.info(
"6.9.1) Percentage of fees in presence of self.stoploss = 1 "
+ str(percentualecommissioni))
balance.append(
[row.close - (percentualecommissioni), 1])
self.totalecommissioni = self.totalecommissioni + percentualecommissioni
logging.info(
"6.9.2) Total of fees in presence of self.stoploss = 1"
+ str(self.totalecommissioni))
self.sell = 1
self.buy = 0
self.stoploss = 0
logging.info(
"6.9.3) Assign value zero to self.stoploss = "
+ str(self.stoploss))
logging.info(
"6.9.4) Assign value 1 to self.sell = " +
str(self.sell))
logging.info(
"6.9.5) Assign value 0 to self.buy = " +
str(self.buy))
else:
sellsignal_vect.append(np.nan)
else:
sellsignal_vect.append(np.nan)
if ((row.Buysignal == 0 or row.Buysignal == '0')):
logging.info(
"7) Value of self.stoploss in if row.BuySignal = 0 " +
str(self.stoploss))
logging.info("7.1) Value of row.Buysignal " +
str(row.Buysignal))
buysignal_vect.append(np.nan)
else:
if ((row.Buysignal == 1 or row.Buysignal == '1')):
logging.info("8) Value of row.Buysignal " +
str(row.Buysignal))
if (self.buy == '0' or self.buy == 0):
logging.info(
"8.1) Buy in presence of self.stoploss = " +
str(self.stoploss))
logging.info("8.2) Value of self.sell = " +
str(self.sell))
logging.info("8.3) Value of self.buy = " +
str(self.buy))
buysignal_vect.append(row.close)
percentualecommissioni = (
float(row.close) / 100) * float(tool.commissioni)
logging.info(
"8.4) Percentage of fees in presence of self.stoploss = 1 "
+ str(percentualecommissioni))
self.totalecommissioni = self.totalecommissioni + percentualecommissioni
logging.info(
"8.5) Total of fees in presence of stoploss = 1" +
str(self.totalecommissioni))
balance.append([(-row.close - percentualecommissioni),
0])
self.buy = 1
self.sell = 0
logging.info(
"8.5.1) Assign value zero to self.stoploss = " +
str(self.stoploss))
logging.info(
"8.5.2) Assign value zero to self.sell = " +
str(self.sell))
logging.info("8.5.3) Assign value 1 to self.buy = " +
str(self.buy))
if (tool.attivazione_stoploss_dinamico == 1):
logging.info(
"8.6) Value of tool.attivazione_stoploss_dinamico equal to : "
+ str(tool.attivazione_stoploss_dinamico))
self.stoplossprezzo = eval(
tool.stoploss_dinamico_formula)
logging.info(
"8.6.1) Value of self.stoplossprezzo " +
str(self.stoplossprezzo))
else:
self.stoplossprezzo = (row.close - (
(row.close / 100) * tool.stoploss_1))
logging.info(
"8.7) Value of self.stoplossprezzo with tool.attivazione_stoploss_dinamico equal to 1 = "
+ str(self.stoplossprezzo))
self.lastbuy = row.close
logging.info("8.8) Value of self.lastbuy" +
str(self.lastbuy))
logging.info("8.9.0) Value of self.stoploss = " +
str(self.stoploss))
logging.info("8.9.1) Value of self.sell = " +
str(self.sell))
logging.info("8.9.2) Value of self.buy = " +
str(self.buy))
logging.info("8.9.3) Value of self.stoplossprezzo = " +
str(self.stoplossprezzo))
logging.info("8.9.4) Value of self.lastbuy = " +
str(self.lastbuy))
logging.info(
"8.9.5) Value of tool.attivazione_stoploss_dinamico = "
+ str(tool.attivazione_stoploss_dinamico))
logging.info(
"8.9.6) Value of tool.attivazione_stoploss_statico = "
+ str(tool.attivazione_stoploss_statico))
else:
buysignal_vect.append(np.nan)
else:
buysignal_vect.append(np.nan)
#add indicators,oscillators and others
prezzmed_vect.append(row.PrezzoMed)
stdev_vect.append(row.STDDEV)
ema_vect.append(row.ema3)
date.append(b)
rsi_vect.append(row.rsi)
#pattern_vect.append(row.CDLHIKKAKE_CLOSE)
volume_vect.append(row.volume)
lower2_vect.append(row.lower2)
upper2_vect.append(row.upper2)
ema_long_vect.append(row.tema50)
ema_short_vect.append(row.tema20)
lower_vect.append(row.lower)
upper_vect.append(row.upper)
macd_vect.append(row.macd)
macd_signal_vect.append(row.macdsignal)
macd_hist_vect.append(row.macdhist)
atr_vect.append(row.atr)
i = i + 1
self.valorestrategia = self.getValoreStrategia(balance)
tool.setVisualizzaGrafico(1)
if (tool.visualizzagrafico == 1 or tool.visualizzagrafico == '1'):
ds = draw.Draw()
ds.setNrGrafici(2)
ds.draw_graphic(moneta, x, y, self.nr_of_trades, self.nr_positive_trades,self.nr_negative_trades, buysignal_vect, sellsignal_vect,
prezzmed_vect, stoplossprezzo_vect, stdev_vect,
self.nome, self.valorestrategia, date, ema_vect,
rsi_vect,pattern_vect,volume_vect,lower2_vect,
upper2_vect,ema_short_vect,ema_long_vect,lower_vect,upper_vect,macd_vect,macd_signal_vect,macd_hist_vect,atr_vect)
def Stock_Ana(arg_stock, str_prev_date, str_today):
sear_comp_id = arg_stock[0]
#日線資料讀取
strsql = "select quo_date, open, high, low, close, vol from STOCK_QUO "
strsql += "where "
strsql += "SEAR_COMP_ID='" + sear_comp_id + "' and "
strsql += "QUO_DATE between '" + str_prev_date + "' and '" + str_today + "' "
strsql += "order by QUO_DATE "
cursor = conn.execute(strsql)
result = cursor.fetchall()
none_flag = False
if len(result) > 0:
df = pd.DataFrame(
result, columns=['date', 'open', 'high', 'low', 'close', 'vol'])
#print(df)
else:
none_flag = True
#關閉cursor
cursor.close()
ls_result = []
if none_flag == False:
#LIST轉換為Numpy Array
npy_high = np.array(df['high'])
npy_low = np.array(df['low'])
npy_close = np.array(df['close'])
stock_vol = df['vol'].tail(6) / 1000 #取近六天成交量，並由股換算為張
last_vol = df['vol'].tail(1) / 1000 #取最近一天成交量，並換算為張
last_open = df['open'].tail(1)
last_close = df['close'].tail(1)
prev_close = df['close'].shift(1).tail(1)
avg_vol = stock_vol.mean() #取平均
#最近一天交易日，成交量漲幅
rt = 0
if avg_vol > 0:
rt = (last_vol.iloc[0] - avg_vol) / avg_vol * 100
#最近一天交易日，K棒本體漲幅
kbody_chg = 0
if last_open.iloc[0] > 0:
kbody_chg = (last_close.iloc[0] -
last_open.iloc[0]) / last_open.iloc[0] * 100
#最近一天交易日當天漲幅
rise_rt = 0
if prev_close.iloc[0] > 0:
rise_rt = (last_close.iloc[0] -
prev_close.iloc[0]) / prev_close.iloc[0] * 100
#計算20MA
ma20 = talib.MA(npy_close, timeperiod=20, matype=0)
df['ma20'] = ma20
#最近一個交易日20MA值
last_20ma = df['ma20'].tail(1)
#倒數第2個交易日20MA值
prev_20ma = df['ma20'].shift(1).tail(1)
#print('## last_close=' + str(last_close))
#print(prev_close.iloc[0])
# http://www.tadoc.org/indicator/STOCH.htm
slowk, slowd = talib.STOCH(npy_high,
npy_low,
npy_close,
fastk_period=9,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
print("slowk=" + str(slowk[-1]))
print("slowd=" + str(slowd[-1]))
# http://www.tadoc.org/indicator/STOCHF.htm
fastk, fastd = talib.STOCHF(npy_high,
npy_low,
npy_close,
fastk_period=9,
fastd_period=9,
fastd_matype=0)
print("fastk=" + str(fastk[-1]))
print("fastd=" + str(fastd[-1]))
# http://www.tadoc.org/indicator/STOCHRSI.htm
fastk, fastd = talib.STOCHRSI(npy_close,
timeperiod=9,
fastk_period=3,
fastd_period=3,
fastd_matype=0)
print("fastk=" + str(fastk[-1]))
print("fastd=" + str(fastd[-1]))
if (prev_close.iloc[0] <= prev_20ma.iloc[0]) and \
(last_close.iloc[0] > last_20ma.iloc[0]):
print("##" + arg_stock[0] + "##" + arg_stock[1] + "##\n")
"""
#三條均線變異數介於0~1間、最近一天成交量相對平均量成長20%、當天上漲3%以下、
#最近一天收盤價在8MA跟50MA之上、成交量均量需大於500張
if (var_val > 0 and var_val < 1) and \
rt >= 20 and \
(rise_rt > 0 and rise_rt < 3) and \
(last_close.iloc[0] > last_8ma.iloc[0]) and \
(last_close.iloc[0] > last_50ma.iloc[0]) and \
avg_vol > 500:
#讀取個股籌碼資料
strsql = "select FR_BAS_CNT, IT_BAS_CNT, DE_BAS_CNT, RANK "
strsql += "from REPORT_CHIP_ANA "
strsql += "where "
strsql += "SEAR_COMP_ID='" + sear_comp_id + "' "
cursor = conn.execute(strsql)
result = list(cursor.fetchone())
if len(result) > 0:
fr_bas_cnt = str(result[0])
it_bas_cnt = str(result[1])
de_bas_cnt = str(result[2])
rank = result[3]
else:
fr_bas_cnt = "0"
it_bas_cnt = "0"
de_bas_cnt = "0"
rank = " "
#關閉cursor
cursor.close()
#print("##" + arg_stock[0] + "##" + arg_stock[1] + "##" + fr_bas_cnt + "##" + it_bas_cnt + "##" + de_bas_cnt + "##" + rank + "##\n")
ls_result = [[arg_stock[0],arg_stock[1],var_val,rt,fr_bas_cnt,it_bas_cnt,de_bas_cnt,rank]]
df_result = pd.DataFrame(ls_result, columns=['代號', '名稱', 'var', 'burst_rt','外資買賣超天數','投信買賣超天數','自營商買賣超天數','類別'])
"""
if len(ls_result) == 0:
df_result = pd.DataFrame()
return df_result
matype=0)
df['ROC'] = ta.ROC(np.array(df['Adj Close'].shift(1)), timeperiod=n)
df['ROCP'] = ta.ROCP(np.array(df['Adj Close'].shift(1)), timeperiod=n)
df['ROCR'] = ta.ROCR(np.array(df['Adj Close'].shift(1)), timeperiod=n)
df['ROCR100'] = ta.ROCR100(np.array(df['Adj Close'].shift(1)), timeperiod=n)
df['slowk'], df['slowd'] = ta.STOCH(np.array(df['High'].shift(1)),
np.array(df['Low'].shift(1)),
np.array(df['Adj Close']),
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
df['fastk'], df['fastd'] = ta.STOCHF(np.array(df['High'].shift(1)),
np.array(df['Low'].shift(1)),
np.array(df['Adj Close']),
fastk_period=5,
fastd_period=3,
fastd_matype=0)
# df['fastk'], df['fastd'] =ta.STOCHRIS(np.array(df['Adj Close'].shift(1)), timeperiod=N, fastk_period=5, fastd_period=3, fastd_matype=0)
df['TRIX'] = ta.TRIX(np.array(df['Adj Close'].shift(1)), timeperiod=n)
df['ULTOSC'] = ta.ULTOSC(np.array(df['High'].shift(1)),
np.array(df['Low'].shift(1)),
np.array(df['Adj Close']),
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
df['WILLR'] = ta.WILLR(np.array(df['High'].shift(1)),
np.array(df['Low'].shift(1)),
np.array(df['Adj Close'].shift(1)),
timeperiod=n)
def test_stoch(self):
result = pandas_ta.stoch(self.high,
self.low,
self.close,
fast_k=14,
slow_k=14,
slow_d=14)
self.assertIsInstance(result, DataFrame)
self.assertEqual(result.name, "STOCH_14_14_14")
self.assertEqual(len(result.columns), 4)
result = pandas_ta.stoch(self.high, self.low, self.close)
self.assertIsInstance(result, DataFrame)
self.assertEqual(result.name, "STOCH_14_5_3")
try:
tal_stochf = tal.STOCHF(self.high, self.low, self.close)
tal_stoch = tal.STOCH(self.high, self.low, self.close)
tal_stochdf = DataFrame({
"STOCHF_14": tal_stochf[0],
"STOCHF_3": tal_stochf[1],
"STOCH_5": tal_stoch[0],
"STOCH_3": tal_stoch[1]
})
pdt.assert_frame_equal(result, tal_stochdf)
except AssertionError as ae:
try:
stochfk_corr = pandas_ta.utils.df_error_analysis(
result.iloc[:, 0], tal_stochdf.iloc[:, 0], col=CORRELATION)
self.assertGreater(stochfk_corr, CORRELATION_THRESHOLD)
except Exception as ex:
error_analysis(result.iloc[:, 0], CORRELATION, ex)
try:
stochfd_corr = pandas_ta.utils.df_error_analysis(
result.iloc[:, 1], tal_stochdf.iloc[:, 1], col=CORRELATION)
self.assertGreater(stochfd_corr, CORRELATION_THRESHOLD)
except Exception as ex:
error_analysis(result.iloc[:, 1],
CORRELATION,
ex,
newline=False)
try:
stochsk_corr = pandas_ta.utils.df_error_analysis(
result.iloc[:, 2], tal_stochdf.iloc[:, 2], col=CORRELATION)
self.assertGreater(stochsk_corr, CORRELATION_THRESHOLD)
except Exception as ex:
error_analysis(result.iloc[:, 2],
CORRELATION,
ex,
newline=False)
try:
stochsd_corr = pandas_ta.utils.df_error_analysis(
result.iloc[:, 3], tal_stochdf.iloc[:, 3], col=CORRELATION)
self.assertGreater(stochsd_corr, CORRELATION_THRESHOLD)
except Exception as ex:
error_analysis(result.iloc[:, 3],
CORRELATION,
ex,
newline=False)
def extract_by_type(self,
feature_type,
open_prices=None,
close_prices=None,
high_prices=None,
low_prices=None):
if feature_type == 'ROCP':
rocp = talib.ROCP(close_prices, timeperiod=1)
self.feature.append(rocp)
if feature_type == 'OROCP':
orocp = talib.ROCP(open_prices, timeperiod=1)
self.feature.append(orocp)
if feature_type == 'HROCP':
hrocp = talib.ROCP(high_prices, timeperiod=1)
self.feature.append(hrocp)
if feature_type == 'LROCP':
lrocp = talib.ROCP(low_prices, timeperiod=1)
self.feature.append(lrocp)
if feature_type == 'MACD':
macd, signal, hist = talib.MACD(close_prices,
fastperiod=12,
slowperiod=26,
signalperiod=9)
norm_macd = numpy.nan_to_num(macd) / math.sqrt(
numpy.var(numpy.nan_to_num(macd)))
norm_signal = numpy.nan_to_num(signal) / math.sqrt(
numpy.var(numpy.nan_to_num(signal)))
norm_hist = numpy.nan_to_num(hist) / math.sqrt(
numpy.var(numpy.nan_to_num(hist)))
macdrocp = talib.ROCP(norm_macd + numpy.max(norm_macd) -
numpy.min(norm_macd),
timeperiod=1)
signalrocp = talib.ROCP(norm_signal + numpy.max(norm_signal) -
numpy.min(norm_signal),
timeperiod=1)
histrocp = talib.ROCP(norm_hist + numpy.max(norm_hist) -
numpy.min(norm_hist),
timeperiod=1)
# self.feature.append(macd / 100.0)
# self.feature.append(signal / 100.0)
# self.feature.append(hist / 100.0)
self.feature.append(norm_macd)
self.feature.append(norm_signal)
self.feature.append(norm_hist)
self.feature.append(macdrocp)
self.feature.append(signalrocp)
self.feature.append(histrocp)
if feature_type == 'RSI':
rsi6 = talib.RSI(close_prices, timeperiod=6)
rsi12 = talib.RSI(close_prices, timeperiod=12)
rsi24 = talib.RSI(close_prices, timeperiod=24)
rsi6rocp = talib.ROCP(rsi6 + 100., timeperiod=1)
rsi12rocp = talib.ROCP(rsi12 + 100., timeperiod=1)
rsi24rocp = talib.ROCP(rsi24 + 100., timeperiod=1)
self.feature.append(rsi6 / 100.0 - 0.5)
self.feature.append(rsi12 / 100.0 - 0.5)
self.feature.append(rsi24 / 100.0 - 0.5)
# self.feature.append(numpy.maximum(rsi6 / 100.0 - 0.8, 0))
# self.feature.append(numpy.maximum(rsi12 / 100.0 - 0.8, 0))
# self.feature.append(numpy.maximum(rsi24 / 100.0 - 0.8, 0))
# self.feature.append(numpy.minimum(rsi6 / 100.0 - 0.2, 0))
# self.feature.append(numpy.minimum(rsi6 / 100.0 - 0.2, 0))
# self.feature.append(numpy.minimum(rsi6 / 100.0 - 0.2, 0))
# self.feature.append(numpy.maximum(numpy.minimum(rsi6 / 100.0 - 0.5, 0.3), -0.3))
# self.feature.append(numpy.maximum(numpy.minimum(rsi6 / 100.0 - 0.5, 0.3), -0.3))
# self.feature.append(numpy.maximum(numpy.minimum(rsi6 / 100.0 - 0.5, 0.3), -0.3))
self.feature.append(rsi6rocp)
self.feature.append(rsi12rocp)
self.feature.append(rsi24rocp)
if feature_type == 'UO':
ult_osc = talib.ULTOSC(high_prices,
low_prices,
close_prices,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
self.feature.append(ult_osc / 100.0 - 0.5)
if feature_type == 'BOLL':
upperband, middleband, lowerband = talib.BBANDS(close_prices,
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)
self.feature.append((upperband - close_prices) / close_prices)
self.feature.append((middleband - close_prices) / close_prices)
self.feature.append((lowerband - close_prices) / close_prices)
if feature_type == 'MA':
ma5 = talib.MA(close_prices, timeperiod=5)
ma10 = talib.MA(close_prices, timeperiod=10)
ma20 = talib.MA(close_prices, timeperiod=20)
ma30 = talib.MA(close_prices, timeperiod=30)
ma60 = talib.MA(close_prices, timeperiod=60)
ma90 = talib.MA(close_prices, timeperiod=90)
ma120 = talib.MA(close_prices, timeperiod=120)
ma180 = talib.MA(close_prices, timeperiod=180)
#ma360 = talib.MA(close_prices, timeperiod=360)
#ma720 = talib.MA(close_prices, timeperiod=720)
ma5rocp = talib.ROCP(ma5, timeperiod=1)
ma10rocp = talib.ROCP(ma10, timeperiod=1)
ma20rocp = talib.ROCP(ma20, timeperiod=1)
ma30rocp = talib.ROCP(ma30, timeperiod=1)
ma60rocp = talib.ROCP(ma60, timeperiod=1)
ma90rocp = talib.ROCP(ma90, timeperiod=1)
ma120rocp = talib.ROCP(ma120, timeperiod=1)
ma180rocp = talib.ROCP(ma180, timeperiod=1)
#ma360rocp = talib.ROCP(ma360, timeperiod=1)
#ma720rocp = talib.ROCP(ma720, timeperiod=1)
self.feature.append(ma5rocp)
self.feature.append(ma10rocp)
self.feature.append(ma20rocp)
self.feature.append(ma30rocp)
self.feature.append(ma60rocp)
self.feature.append(ma90rocp)
self.feature.append(ma120rocp)
self.feature.append(ma180rocp)
#self.feature.append(ma360rocp)
#self.feature.append(ma720rocp)
self.feature.append((ma5 - close_prices) / close_prices)
self.feature.append((ma10 - close_prices) / close_prices)
self.feature.append((ma20 - close_prices) / close_prices)
self.feature.append((ma30 - close_prices) / close_prices)
self.feature.append((ma60 - close_prices) / close_prices)
self.feature.append((ma90 - close_prices) / close_prices)
self.feature.append((ma120 - close_prices) / close_prices)
self.feature.append((ma180 - close_prices) / close_prices)
#self.feature.append((ma360 - close_prices) / close_prices)
#self.feature.append((ma720 - close_prices) / close_prices)
if feature_type == 'STOCH':
slow_stoch_k, slow_stoch_d = talib.STOCH(high_prices,
low_prices,
close_prices,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
fast_stoch_k, fast_stoch_d = talib.STOCHF(high_prices,
low_prices,
close_prices,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
fast_rsi_k, fast_rsi_d = talib.STOCHRSI(close_prices,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
self.feature.append(slow_stoch_k / 100.0 - 0.5)
self.feature.append(slow_stoch_d / 100.0 - 0.5)
self.feature.append(fast_stoch_k / 100.0 - 0.5)
self.feature.append(fast_stoch_d / 100.0 - 0.5)
self.feature.append(fast_rsi_k / 100.0 - 0.5)
self.feature.append(fast_rsi_d / 100.0 - 0.5)
if feature_type == 'AO':
median_price = (high_prices + low_prices) / 2
ao = talib.SMA(median_price, 5) - talib.SMA(median_price, 34)
self.feature.append(ao)
return self.feature
