def math_transform_process(event):
print(event.widget.get())
math_transform = event.widget.get()
upperband, middleband, lowerband = ta.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
fig, axes = plt.subplots(2, 1, sharex=True)
ax1, ax2 = axes[0], axes[1]
axes[0].plot(close, 'rd-', markersize=3)
axes[0].plot(upperband, 'y-')
axes[0].plot(middleband, 'b-')
axes[0].plot(lowerband, 'y-')
axes[0].set_title(math_transform, fontproperties="SimHei")
if math_transform == '反余弦':
real = ta.ACOS(close)
axes[1].plot(real, 'r-')
elif math_transform == '反正弦':
real = ta.ASIN(close)
axes[1].plot(real, 'r-')
elif math_transform == '反正切':
real = ta.ATAN(close)
axes[1].plot(real, 'r-')
elif math_transform == '向上取整':
real = ta.CEIL(close)
axes[1].plot(real, 'r-')
elif math_transform == '余弦':
real = ta.COS(close)
axes[1].plot(real, 'r-')
elif math_transform == '双曲余弦':
real = ta.COSH(close)
axes[1].plot(real, 'r-')
elif math_transform == '指数':
real = ta.EXP(close)
axes[1].plot(real, 'r-')
elif math_transform == '向下取整':
real = ta.FLOOR(close)
axes[1].plot(real, 'r-')
elif math_transform == '自然对数':
real = ta.LN(close)
axes[1].plot(real, 'r-')
elif math_transform == '常用对数':
real = ta.LOG10(close)
axes[1].plot(real, 'r-')
elif math_transform == '正弦':
real = ta.SIN(close)
axes[1].plot(real, 'r-')
elif math_transform == '双曲正弦':
real = ta.SINH(close)
axes[1].plot(real, 'r-')
elif math_transform == '平方根':
real = ta.SQRT(close)
axes[1].plot(real, 'r-')
elif math_transform == '正切':
real = ta.TAN(close)
axes[1].plot(real, 'r-')
elif math_transform == '双曲正切':
real = ta.TANH(close)
axes[1].plot(real, 'r-')
plt.show()
def genTA(data, y, t): #t is timeperiod
indicators = {}
y_ind = copy.deepcopy(y)
for ticker in data:
## Overlap
indicators[ticker] = ta.SMA(data[ticker].iloc[:,3], timeperiod=t).to_frame()
indicators[ticker]['EMA'] = ta.EMA(data[ticker].iloc[:,3], timeperiod=t)
indicators[ticker]['BBAND_Upper'], indicators[ticker]['BBAND_Middle' ], indicators[ticker]['BBAND_Lower' ] = ta.BBANDS(data[ticker].iloc[:,3], timeperiod=t, nbdevup=2, nbdevdn=2, matype=0)
indicators[ticker]['HT_TRENDLINE'] = ta.HT_TRENDLINE(data[ticker].iloc[:,3])
indicators[ticker]['SAR'] = ta.SAR(data[ticker].iloc[:,1], data[ticker].iloc[:,2], acceleration=0, maximum=0)
#rename SMA column
indicators[ticker].rename(columns={indicators[ticker].columns[0]: "SMA"}, inplace=True)
## Momentum
indicators[ticker]['RSI'] = ta.RSI(data[ticker].iloc[:,3], timeperiod=(t-1))
indicators[ticker]['MOM'] = ta.MOM(data[ticker].iloc[:,3], timeperiod=(t-1))
indicators[ticker]['ROC'] = ta.ROC(data[ticker].iloc[:,3], timeperiod=(t-1))
indicators[ticker]['ROCP']= ta.ROCP(data[ticker].iloc[:,3],timeperiod=(t-1))
indicators[ticker]['STOCH_SLOWK'], indicators[ticker]['STOCH_SLOWD'] = ta.STOCH(data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3], fastk_period=t, slowk_period=int(.6*t), slowk_matype=0, slowd_period=int(.6*t), slowd_matype=0)
indicators[ticker]['MACD'], indicators[ticker]['MACDSIGNAL'], indicators[ticker]['MACDHIST'] = ta.MACD(data[ticker].iloc[:,3], fastperiod=t,slowperiod=2*t,signalperiod=int(.7*t))
## Volume
indicators[ticker]['OBV'] = ta.OBV(data[ticker].iloc[:,3], data[ticker].iloc[:,4])
indicators[ticker]['AD'] = ta.AD(data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3], data[ticker].iloc[:,4])
indicators[ticker]['ADOSC'] = ta.ADOSC(data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3], data[ticker].iloc[:,4], fastperiod=int(.3*t), slowperiod=t)
## Cycle
indicators[ticker]['HT_DCPERIOD'] = ta.HT_DCPERIOD(data[ticker].iloc[:,3])
indicators[ticker]['HT_TRENDMODE']= ta.HT_TRENDMODE(data[ticker].iloc[:,3])
## Price
indicators[ticker]['AVGPRICE'] = ta.AVGPRICE(data[ticker].iloc[:,0], data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3])
indicators[ticker]['TYPPRICE'] = ta.TYPPRICE(data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3])
## Volatility
indicators[ticker]['ATR'] = ta.ATR(data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3], timeperiod=(t-1))
## Statistics
indicators[ticker]['BETA'] = ta.BETA(data[ticker].iloc[:,1], data[ticker].iloc[:,2], timeperiod=(t-1))
indicators[ticker]['LINEARREG'] = ta.LINEARREG(data[ticker].iloc[:,3], timeperiod=t)
indicators[ticker]['VAR'] = ta.VAR(data[ticker].iloc[:,3], timeperiod=t, nbdev=1)
## Math Transform
indicators[ticker]['EXP'] = ta.EXP(data[ticker].iloc[:,3])
indicators[ticker]['LN'] = ta.LN(data[ticker].iloc[:,3])
## Patterns (returns integers - but norming might not really do anything but wondering if they should be normed)
indicators[ticker]['CDLENGULFING'] = ta.CDLENGULFING(data[ticker].iloc[:,0], data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3])
indicators[ticker]['CDLDOJI'] = ta.CDLDOJI(data[ticker].iloc[:,0], data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3])
indicators[ticker]['CDLHAMMER'] = ta.CDLHAMMER(data[ticker].iloc[:,0], data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3])
indicators[ticker]['CDLHANGINGMAN']= ta.CDLHANGINGMAN(data[ticker].iloc[:,0], data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3])
#drop 'nan' values
indicators[ticker].drop(indicators[ticker].index[np.arange(0,63)], inplace=True)
y_ind[ticker].drop(y_ind[ticker].index[np.arange(0,63)], inplace=True)
#Normalize Features
indicators_norm = normData(indicators)
return indicators_norm, indicators, y_ind
def Math_Transform(dataframe):
#Math Transform Functions
#ACOS - Vector Trigonometric ACos
df[f'{ratio}_ACOS'] = talib.ACOS(Close)
#ASIN - Vector Trigonometric ASin
df[f'{ratio}_ASIN'] = talib.ASIN(Close)
#ATAN - Vector Trigonometric ATan
df[f'{ratio}_ATAN'] = talib.ATAN(Close)
#CEIL - Vector Ceil
df[f'{ratio}_CEIL'] = talib.CEIL(Close)
#COS - Vector Trigonometric Cos
df[f'{ratio}_COS'] = talib.COS(Close)
#COSH - Vector Trigonometric Cosh
df[f'{ratio}_COSH'] = talib.COSH(Close)
#EXP - Vector Arithmetic Exp
df[f'{ratio}_EXP'] = talib.EXP(Close)
#FLOOR - Vector Floor
df[f'{ratio}_FLOOR'] = talib.FLOOR(Close)
#LN - Vector Log Natural
df[f'{ratio}_LN'] = talib.LN(Close)
#LOG10 - Vector Log10
df[f'{ratio}_LOG10'] = talib.LOG10(Close)
#SIN - Vector Trigonometric Sin
df[f'{ratio}_SIN'] = talib.SIN(Close)
#SINH - Vector Trigonometric Sinh
df[f'{ratio}_SINH'] = talib.SINH(Close)
#SQRT - Vector Square Root
df[f'{ratio}_SQRT'] = talib.SQRT(Close)
#TAN - Vector Trigonometric Tan
df[f'{ratio}_TAN'] = talib.TAN(Close)
#TANH - Vector Trigonometric Tanh
df[f'{ratio}_TANH'] = talib.TANH(Close)
return
def vol(self, n, array=False):
"""波动率指标"""
logrtn = talib.LN(self.high / self.low)
stdrtn = talib.STDDEV(logrtn, n)
vol = talib.EXP(stdrtn) - 1
if array:
return vol
return vol[-1]
def LN(close):
''' Vector Log Natural 自然对数
分组: Math Transform 数学变换
简介:
real = LN(close)
'''
return talib.LN(close)
def cal_vola(data):
# 计算波动率。输入为权益序列np.array
if len(data[data <= 0]) == 0:
temp = [0] * len(data)
temp2 = range(len(data))
for i in temp2:
temp[i] = data[i]/data[i-1]
temp[0] = 1
temp = talib.LN(np.array(temp))
vola = np.std(temp)
else:
vola = -1
return vola
def fisher(high, low, timeperiod=10, fish_ratio=0.33):
mid = (high + low) / 2
max, min = ta.MAX(mid,
timeperiod=timeperiod), ta.MIN(mid,
timeperiod=timeperiod)
vv = 2 * ((mid - min) / (max - min) - 0.5)
vv = vv.apply(lambda x: 0.999 if x > 0.99 else x)
vv = vv.apply(lambda x: -0.999 if x < -0.99 else x)
f0 = preKeep(vv, fish_ratio)
vv2 = ta.LN((1 + f0) / (1 - f0))
# fish 值
fish = preKeep(vv2, 0.5)
return fish
def ln(client, symbol, timeframe="6m", col="close"):
"""This will return a dataframe of
Vector Log Natural
for the given symbol across the given timeframe
Args:
client (pyEX.Client); Client
symbol (string); Ticker
timeframe (string); timeframe to use, for pyEX.chart
col (string); column to use to calculate
Returns:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
x = t.LN(df[col].values)
return pd.DataFrame({col: df[col].values, "ln": x})
def buyOrSellCheck(security, context):
panduan = 0
his = attribute_history(security,
100,
'1d', ('open', 'low', 'close', 'high'),
df=False)
closeArray = his['close']
highArray = his['high']
openArray = his['open']
lowArray = his['low']
####计算MA####
MAArray = talib.EMA(closeArray, g.ma)
####a用来计算k线的实体部分长度占总长度的比例
#a越小，说明是十字星形状的k线，说明要变盘####
a = (openArray[-1] - closeArray[-1]) / (highArray[-1] - lowArray[-1])
a = abs(a)
####计算择时主要指标jump-RSI####
#jumpArray = talib.LN(closeArray / highArray)
jumpArray = talib.LN(closeArray / highArray)
RSIArray = talib.RSI(jumpArray, g.nRSI)
####买卖逻辑####
if RSIArray[-1] <= g.buyThreshold: #RSI低于买入阈值
if closeArray[-1] > MAArray[-1]: #判断强弱市
panduan = 1
elif a <= g.bian1: #弱市状态下，需要确认有变盘信号才买进
panduan = 1
elif RSIArray[-1] >= g.sellThreshold: #RSI高于卖出阈值
if closeArray[-1] <= MAArray[-1]:
panduan = -1
elif a <= g.bian2: #强市状态下，需要确认有变盘信号才卖出
panduan = -1
####止盈止损####
if security in context.portfolio.positions.keys():
avgCost = context.portfolio.positions[security].avg_cost
if closeArray[-1] / avgCost - 1 < -0.1:
panduan = -1
if closeArray[-1] / avgCost - 1 > 0.1:
panduan = -1
return panduan
def LN(data, **kwargs):
_check_talib_presence()
prices = _extract_series(data)
return talib.LN(prices, **kwargs)
def add_ta_features(df, ta_settings):
"""Add technial analysis features from typical financial dataset that
typically include columns such as "open", "high", "low", "price" and
"volume".
http://mrjbq7.github.io/ta-lib/
Args:
df(pandas.DataFrame): original DataFrame.
ta_settings(dict): configuration.
Returns:
pandas.DataFrame: DataFrame with new features included.
"""
open = df['open']
high = df['high']
low = df['low']
close = df['price']
volume = df['volume']
if ta_settings['overlap']:
df['ta_overlap_bbands_upper'], df['ta_overlap_bbands_middle'], df[
'ta_overlap_bbands_lower'] = ta.BBANDS(close,
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)
df['ta_overlap_dema'] = ta.DEMA(
close, timeperiod=15) # NOTE: Changed to avoid a lot of Nan values
df['ta_overlap_ema'] = ta.EMA(close, timeperiod=30)
df['ta_overlap_kama'] = ta.KAMA(close, timeperiod=30)
df['ta_overlap_ma'] = ta.MA(close, timeperiod=30, matype=0)
df['ta_overlap_mama_mama'], df['ta_overlap_mama_fama'] = ta.MAMA(close)
period = np.random.randint(10, 20, size=len(close)).astype(float)
df['ta_overlap_mavp'] = ta.MAVP(close,
period,
minperiod=2,
maxperiod=30,
matype=0)
df['ta_overlap_midpoint'] = ta.MIDPOINT(close, timeperiod=14)
df['ta_overlap_midprice'] = ta.MIDPRICE(high, low, timeperiod=14)
df['ta_overlap_sar'] = ta.SAR(high, low, acceleration=0, maximum=0)
df['ta_overlap_sarext'] = ta.SAREXT(high,
low,
startvalue=0,
offsetonreverse=0,
accelerationinitlong=0,
accelerationlong=0,
accelerationmaxlong=0,
accelerationinitshort=0,
accelerationshort=0,
accelerationmaxshort=0)
df['ta_overlap_sma'] = ta.SMA(close, timeperiod=30)
df['ta_overlap_t3'] = ta.T3(close, timeperiod=5, vfactor=0)
df['ta_overlap_tema'] = ta.TEMA(
close, timeperiod=12) # NOTE: Changed to avoid a lot of Nan values
df['ta_overlap_trima'] = ta.TRIMA(close, timeperiod=30)
df['ta_overlap_wma'] = ta.WMA(close, timeperiod=30)
# NOTE: Commented to avoid a lot of Nan values
# df['ta_overlap_ht_trendline'] = ta.HT_TRENDLINE(close)
if ta_settings['momentum']:
df['ta_momentum_adx'] = ta.ADX(high, low, close, timeperiod=14)
df['ta_momentum_adxr'] = ta.ADXR(high, low, close, timeperiod=14)
df['ta_momentum_apo'] = ta.APO(close,
fastperiod=12,
slowperiod=26,
matype=0)
df['ta_momentum_aroondown'], df['ta_momentum_aroonup'] = ta.AROON(
high, low, timeperiod=14)
df['ta_momentum_aroonosc'] = ta.AROONOSC(high, low, timeperiod=14)
df['ta_momentum_bop'] = ta.BOP(open, high, low, close)
df['ta_momentum_cci'] = ta.CCI(high, low, close, timeperiod=14)
df['ta_momentum_cmo'] = ta.CMO(close, timeperiod=14)
df['ta_momentum_dx'] = ta.DX(high, low, close, timeperiod=14)
df['ta_momentum_macd_macd'], df['ta_momentum_macd_signal'], df[
'ta_momentum_macd_hist'] = ta.MACD(close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
df['ta_momentum_macdext_macd'], df['ta_momentum_macdext_signal'], df[
'ta_momentum_macdext_hist'] = ta.MACDEXT(close,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
df['ta_momentum_macdfix_macd'], df['ta_momentum_macdfix_signal'], df[
'ta_momentum_macdfix_hist'] = ta.MACDFIX(close, signalperiod=9)
df['ta_momentum_mfi'] = ta.MFI(high, low, close, volume, timeperiod=14)
df['ta_momentum_minus_di'] = ta.MINUS_DI(high,
low,
close,
timeperiod=14)
df['ta_momentum_minus_dm'] = ta.MINUS_DM(high, low, timeperiod=14)
df['ta_momentum_mom'] = ta.MOM(close, timeperiod=10)
df['ta_momentum_plus_di'] = ta.PLUS_DI(high, low, close, timeperiod=14)
df['ta_momentum_plus_dm'] = ta.PLUS_DM(high, low, timeperiod=14)
df['ta_momentum_ppo'] = ta.PPO(close,
fastperiod=12,
slowperiod=26,
matype=0)
df['ta_momentum_roc'] = ta.ROC(close, timeperiod=10)
df['ta_momentum_rocp'] = ta.ROCP(close, timeperiod=10)
df['ta_momentum_rocr'] = ta.ROCR(close, timeperiod=10)
df['ta_momentum_rocr100'] = ta.ROCR100(close, timeperiod=10)
df['ta_momentum_rsi'] = ta.RSI(close, timeperiod=14)
df['ta_momentum_slowk'], df['ta_momentum_slowd'] = ta.STOCH(
high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
df['ta_momentum_fastk'], df['ta_momentum_fastd'] = ta.STOCHF(
high, low, close, fastk_period=5, fastd_period=3, fastd_matype=0)
df['ta_momentum_fastk'], df['ta_momentum_fastd'] = ta.STOCHRSI(
close,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
df['ta_momentum_trix'] = ta.TRIX(
close, timeperiod=12) # NOTE: Changed to avoid a lot of Nan values
df['ta_momentum_ultosc'] = ta.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
df['ta_momentum_willr'] = ta.WILLR(high, low, close, timeperiod=14)
if ta_settings['volume']:
df['ta_volume_ad'] = ta.AD(high, low, close, volume)
df['ta_volume_adosc'] = ta.ADOSC(high,
low,
close,
volume,
fastperiod=3,
slowperiod=10)
df['ta_volume_obv'] = ta.OBV(close, volume)
if ta_settings['volatility']:
df['ta_volatility_atr'] = ta.ATR(high, low, close, timeperiod=14)
df['ta_volatility_natr'] = ta.NATR(high, low, close, timeperiod=14)
df['ta_volatility_trange'] = ta.TRANGE(high, low, close)
if ta_settings['price']:
df['ta_price_avgprice'] = ta.AVGPRICE(open, high, low, close)
df['ta_price_medprice'] = ta.MEDPRICE(high, low)
df['ta_price_typprice'] = ta.TYPPRICE(high, low, close)
df['ta_price_wclprice'] = ta.WCLPRICE(high, low, close)
if ta_settings['cycle']:
df['ta_cycle_ht_dcperiod'] = ta.HT_DCPERIOD(close)
df['ta_cycle_ht_phasor_inphase'], df[
'ta_cycle_ht_phasor_quadrature'] = ta.HT_PHASOR(close)
df['ta_cycle_ht_trendmode'] = ta.HT_TRENDMODE(close)
# NOTE: Commented to avoid a lot of Nan values
# df['ta_cycle_ht_dcphase'] = ta.HT_DCPHASE(close)
# df['ta_cycle_ht_sine_sine'], df['ta_cycle_ht_sine_leadsine'] = ta.HT_SINE(close)
if ta_settings['pattern']:
df['ta_pattern_cdl2crows'] = ta.CDL2CROWS(open, high, low, close)
df['ta_pattern_cdl3blackrows'] = ta.CDL3BLACKCROWS(
open, high, low, close)
df['ta_pattern_cdl3inside'] = ta.CDL3INSIDE(open, high, low, close)
df['ta_pattern_cdl3linestrike'] = ta.CDL3LINESTRIKE(
open, high, low, close)
df['ta_pattern_cdl3outside'] = ta.CDL3OUTSIDE(open, high, low, close)
df['ta_pattern_cdl3starsinsouth'] = ta.CDL3STARSINSOUTH(
open, high, low, close)
df['ta_pattern_cdl3whitesoldiers'] = ta.CDL3WHITESOLDIERS(
open, high, low, close)
df['ta_pattern_cdlabandonedbaby'] = ta.CDLABANDONEDBABY(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdladvanceblock'] = ta.CDLADVANCEBLOCK(
open, high, low, close)
df['ta_pattern_cdlbelthold'] = ta.CDLBELTHOLD(open, high, low, close)
df['ta_pattern_cdlbreakaway'] = ta.CDLBREAKAWAY(open, high, low, close)
df['ta_pattern_cdlclosingmarubozu'] = ta.CDLCLOSINGMARUBOZU(
open, high, low, close)
df['ta_pattern_cdlconcealbabyswall'] = ta.CDLCONCEALBABYSWALL(
open, high, low, close)
df['ta_pattern_cdlcounterattack'] = ta.CDLCOUNTERATTACK(
open, high, low, close)
df['ta_pattern_cdldarkcloudcover'] = ta.CDLDARKCLOUDCOVER(
open, high, low, close, penetration=0)
df['ta_pattern_cdldoji'] = ta.CDLDOJI(open, high, low, close)
df['ta_pattern_cdldojistar'] = ta.CDLDOJISTAR(open, high, low, close)
df['ta_pattern_cdldragonflydoji'] = ta.CDLDRAGONFLYDOJI(
open, high, low, close)
df['ta_pattern_cdlengulfing'] = ta.CDLENGULFING(open, high, low, close)
df['ta_pattern_cdleveningdojistar'] = ta.CDLEVENINGDOJISTAR(
open, high, low, close, penetration=0)
df['ta_pattern_cdleveningstar'] = ta.CDLEVENINGSTAR(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdlgapsidesidewhite'] = ta.CDLGAPSIDESIDEWHITE(
open, high, low, close)
df['ta_pattern_cdlgravestonedoji'] = ta.CDLGRAVESTONEDOJI(
open, high, low, close)
df['ta_pattern_cdlhammer'] = ta.CDLHAMMER(open, high, low, close)
df['ta_pattern_cdlhangingman'] = ta.CDLHANGINGMAN(
open, high, low, close)
df['ta_pattern_cdlharami'] = ta.CDLHARAMI(open, high, low, close)
df['ta_pattern_cdlharamicross'] = ta.CDLHARAMICROSS(
open, high, low, close)
df['ta_pattern_cdlhighwave'] = ta.CDLHIGHWAVE(open, high, low, close)
df['ta_pattern_cdlhikkake'] = ta.CDLHIKKAKE(open, high, low, close)
df['ta_pattern_cdlhikkakemod'] = ta.CDLHIKKAKEMOD(
open, high, low, close)
df['ta_pattern_cdlhomingpigeon'] = ta.CDLHOMINGPIGEON(
open, high, low, close)
df['ta_pattern_cdlidentical3crows'] = ta.CDLIDENTICAL3CROWS(
open, high, low, close)
df['ta_pattern_cdlinneck'] = ta.CDLINNECK(open, high, low, close)
df['ta_pattern_cdlinvertedhammer'] = ta.CDLINVERTEDHAMMER(
open, high, low, close)
df['ta_pattern_cdlkicking'] = ta.CDLKICKING(open, high, low, close)
df['ta_pattern_cdlkickingbylength'] = ta.CDLKICKINGBYLENGTH(
open, high, low, close)
df['ta_pattern_cdlladderbottom'] = ta.CDLLADDERBOTTOM(
open, high, low, close)
df['ta_pattern_cdllongleggeddoji'] = ta.CDLLONGLEGGEDDOJI(
open, high, low, close)
df['ta_pattern_cdllongline'] = ta.CDLLONGLINE(open, high, low, close)
df['ta_pattern_cdlmarubozu'] = ta.CDLMARUBOZU(open, high, low, close)
df['ta_pattern_cdlmatchinglow'] = ta.CDLMATCHINGLOW(
open, high, low, close)
df['ta_pattern_cdlmathold'] = ta.CDLMATHOLD(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdlmorningdojistar'] = ta.CDLMORNINGDOJISTAR(
open, high, low, close, penetration=0)
df['ta_pattern_cdlmorningstar'] = ta.CDLMORNINGSTAR(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdllonneck'] = ta.CDLONNECK(open, high, low, close)
df['ta_pattern_cdlpiercing'] = ta.CDLPIERCING(open, high, low, close)
df['ta_pattern_cdlrickshawman'] = ta.CDLRICKSHAWMAN(
open, high, low, close)
df['ta_pattern_cdlrisefall3methods'] = ta.CDLRISEFALL3METHODS(
open, high, low, close)
df['ta_pattern_cdlseparatinglines'] = ta.CDLSEPARATINGLINES(
open, high, low, close)
df['ta_pattern_cdlshootingstar'] = ta.CDLSHOOTINGSTAR(
open, high, low, close)
df['ta_pattern_cdlshortline'] = ta.CDLSHORTLINE(open, high, low, close)
df['ta_pattern_cdlspinningtop'] = ta.CDLSPINNINGTOP(
open, high, low, close)
df['ta_pattern_cdlstalledpattern'] = ta.CDLSTALLEDPATTERN(
open, high, low, close)
df['ta_pattern_cdlsticksandwich'] = ta.CDLSTICKSANDWICH(
open, high, low, close)
df['ta_pattern_cdltakuri'] = ta.CDLTAKURI(open, high, low, close)
df['ta_pattern_cdltasukigap'] = ta.CDLTASUKIGAP(open, high, low, close)
df['ta_pattern_cdlthrusting'] = ta.CDLTHRUSTING(open, high, low, close)
df['ta_pattern_cdltristar'] = ta.CDLTRISTAR(open, high, low, close)
df['ta_pattern_cdlunique3river'] = ta.CDLUNIQUE3RIVER(
open, high, low, close)
df['ta_pattern_cdlupsidegap2crows'] = ta.CDLUPSIDEGAP2CROWS(
open, high, low, close)
df['ta_pattern_cdlxsidegap3methods'] = ta.CDLXSIDEGAP3METHODS(
open, high, low, close)
if ta_settings['statistic']:
df['ta_statistic_beta'] = ta.BETA(high, low, timeperiod=5)
df['ta_statistic_correl'] = ta.CORREL(high, low, timeperiod=30)
df['ta_statistic_linearreg'] = ta.LINEARREG(close, timeperiod=14)
df['ta_statistic_linearreg_angle'] = ta.LINEARREG_ANGLE(close,
timeperiod=14)
df['ta_statistic_linearreg_intercept'] = ta.LINEARREG_INTERCEPT(
close, timeperiod=14)
df['ta_statistic_linearreg_slope'] = ta.LINEARREG_SLOPE(close,
timeperiod=14)
df['ta_statistic_stddev'] = ta.STDDEV(close, timeperiod=5, nbdev=1)
df['ta_statistic_tsf'] = ta.TSF(close, timeperiod=14)
df['ta_statistic_var'] = ta.VAR(close, timeperiod=5, nbdev=1)
if ta_settings['math_transforms']:
df['ta_math_transforms_atan'] = ta.ATAN(close)
df['ta_math_transforms_ceil'] = ta.CEIL(close)
df['ta_math_transforms_cos'] = ta.COS(close)
df['ta_math_transforms_floor'] = ta.FLOOR(close)
df['ta_math_transforms_ln'] = ta.LN(close)
df['ta_math_transforms_log10'] = ta.LOG10(close)
df['ta_math_transforms_sin'] = ta.SIN(close)
df['ta_math_transforms_sqrt'] = ta.SQRT(close)
df['ta_math_transforms_tan'] = ta.TAN(close)
if ta_settings['math_operators']:
df['ta_math_operators_add'] = ta.ADD(high, low)
df['ta_math_operators_div'] = ta.DIV(high, low)
df['ta_math_operators_min'], df['ta_math_operators_max'] = ta.MINMAX(
close, timeperiod=30)
df['ta_math_operators_minidx'], df[
'ta_math_operators_maxidx'] = ta.MINMAXINDEX(close, timeperiod=30)
df['ta_math_operators_mult'] = ta.MULT(high, low)
df['ta_math_operators_sub'] = ta.SUB(high, low)
df['ta_math_operators_sum'] = ta.SUM(close, timeperiod=30)
return df
def main():
# read csv file and transform it to datafeed (df):
df = pd.read_csv(current_dir + "/" + base_dir + "/" + in_dir + "/" +
in_dir + '_' + stock_symbol + '.csv')
# set numpy datafeed from df:
df_numpy = {
'Date': np.array(df['date']),
'Open': np.array(df['open'], dtype='float'),
'High': np.array(df['high'], dtype='float'),
'Low': np.array(df['low'], dtype='float'),
'Close': np.array(df['close'], dtype='float'),
'Volume': np.array(df['volume'], dtype='float')
}
date = df_numpy['Date']
openp = df_numpy['Open']
high = df_numpy['High']
low = df_numpy['Low']
close = df_numpy['Close']
volume = df_numpy['Volume']
#########################################
##### Math Transform Functions ######
#########################################
#ACOS - Vector Trigonometric ACos
acos = ta.ACOS(close)
#ASIN - Vector Trigonometric ASin
asin = ta.ASIN(close)
#ATAN - Vector Trigonometric ATan
atan = ta.ATAN(close)
#CEIL - Vector Ceil
ceil = ta.CEIL(close)
#COS - Vector Trigonometric Cos
cos = ta.COS(close)
#COSH - Vector Trigonometric Cosh
cosh = ta.COSH(close)
#EXP - Vector Arithmetic Exp
exp = ta.EXP(close)
#FLOOR - Vector Floor
floor = ta.FLOOR(close)
#LN - Vector Log Natural
ln = ta.LN(close)
#LOG10 - Vector Log10
log10 = ta.LOG10(close)
#SIN - Vector Trigonometric Sin
sin = ta.SIN(close)
#SINH - Vector Trigonometric Sinh
sinh = ta.SINH(close)
#SQRT - Vector Square Root
sqrt = ta.SQRT(close)
#TAN - Vector Trigonometric Tan
tan = ta.TAN(close)
#TANH - Vector Trigonometric Tanh
tanh = ta.TANH(close)
df_save = pd.DataFrame(
data={
'date': np.array(df['date']),
'acos': acos,
'asin': asin,
'atan': atan,
'ceil': ceil,
'cos': cos,
'cosh': cosh,
'exp': exp,
'floor': floor,
'ln': ln,
'log10': log10,
'sin': sin,
'sinh': sinh,
'sqrt': sqrt,
'tan': tan,
'tanh': tanh
})
df_save.to_csv(current_dir + "/" + base_dir + "/" + out_dir + '/' +
stock_symbol + "/" + out_dir + '_ta_math_transform_' +
stock_symbol + '.csv',
index=False)
mpl.rcParams['font.sans-serif'] = ['SimHei']
mpl.rcParams['axes.unicode_minus'] = False
#引入TA-Lib库
import talib as ta
#获取交易数据用于示例分析
import tushare as ts
def get_data(code, start='2015-01-01'):
df = ts.get_k_data(code, start)
df.index = pd.to_datetime(df.date)
df = df.sort_index()
return df
#获取上证指数收盘价、最高、最低价格
df = get_data('sh')[['open', 'close', 'high', 'low']]
df['sin'] = ta.SIN(df.close)
df['cos'] = ta.COS(df.close)
df['ln'] = ta.LN(df.close)
#将上述函数计算得到的结果进行可视化
df[['close', 'sin', 'cos', 'ln']].plot(figsize=(12, 8),
subplots=True,
layout=(2, 2))
plt.subplots_adjust(wspace=0, hspace=0.2)
plt.show()
def LN(Close):
return Close.apply(lambda col: ta.LN(col), axis=0)
def LN(self, name, **parameters):
data = self.__data[name]
return talib.LN(data, **parameters)
def get_talib_indicators(df, freq_lst=(5, 15, 30), divided_by_close=False):
"""
Additional indicators with different indicator parameters customized
:param df: pandas.DataFrame with {'open', 'close', 'high', 'low', 'volume'} and index == date
:return: pandas.DataFrame
"""
for n in freq_lst:
# Overlap Studies Functions
#BBANDS
df['BBANDS_' + str(n) + '_UP'], df['BBANDS_' + str(n) +
'_MID'], df['BBANDS_' + str(n) +
'_LOW'] = ta.BBANDS(
df.close,
timeperiod=n,
nbdevup=2,
nbdevdn=2,
matype=0)
#DEMA
df['DEMA' + str(n)] = ta.DEMA(df.close, timeperiod=n)
#EMA
df['EMA' + str(n)] = ta.EMA(df.close, timeperiod=n)
#KAMA
df['KAMA' + str(n)] = ta.KAMA(df.close, timeperiod=n)
#MA
df['MA' + str(n)] = ta.MA(df.close, timeperiod=n)
#MAMA(ERROR)
#df['MAMA' + str(n)] = ta.MAMA(df.close, fastlimit=0, slowlimit=0)
#MAVP(periods 参数暂时无法确定)
#df['' + str(n)] = ta.MAVP(df.close, periods, minperiod=2, maxperiod=30, matype=0)
#MIDPOINT
df['MIDPOINT' + str(n)] = ta.MIDPOINT(df.close, timeperiod=n)
#MIDPRICE
df['MIDPRICE' + str(n)] = ta.MIDPRICE(df.high, df.low, timeperiod=n)
#SAR
df['SAR' + str(n)] = ta.SAR(df.high, df.low, acceleration=0, maximum=0)
#SAREXT（参数太多，暂时无法知道如何设置）
#df['SAREXT' + str(n)] = ta.SAREXT(df.high, df.low, startvalue=0, offsetonreverse=0, accelerationinitlong=0, accelerationlong=0, accelerationmaxlong=0, accelerationinitshort=0, accelerationshort=0, accelerationmaxshort=0)
#SMA
df['SMA' + str(n)] = ta.SMA(df.close, timeperiod=n)
#T3
df['T3' + str(n)] = ta.T3(df.close, timeperiod=n, vfactor=0.7)
#TEMA
df['TEMA' + str(n)] = ta.TEMA(df.close, timeperiod=n)
#TRIMA
df['TRIMA' + str(n)] = ta.TRIMA(df.close, timeperiod=n)
#WMA
df['WMA' + str(n)] = ta.WMA(df.close, timeperiod=n)
#Momentum Indicator Functions
#ADX
df['ADX' + str(n)] = ta.ADX(df.high, df.low, df.close, timeperiod=n)
#ADXR
df['ADXR' + str(n)] = ta.ADXR(df.high, df.low, df.close, timeperiod=n)
#APO
df['APO' + str(n)] = ta.APO(df.close,
fastperiod=12,
slowperiod=26,
matype=0)
#AROON
df['AROON_DOWN_' + str(n)], df['AROON_UP_' + str(n)] = ta.AROON(
df.high, df.low, timeperiod=n)
#AROONOSC
df['AROONOSC' + str(n)] = ta.AROONOSC(df.high, df.low, timeperiod=n)
#CCI
df['CCI' + str(n)] = ta.CCI(df.high, df.low, df.close, timeperiod=n)
#CMO
df['CMO' + str(n)] = ta.CMO(df.close, timeperiod=n)
#DX
df['DX' + str(n)] = ta.DX(df.high, df.low, df.close, timeperiod=n)
#MFI
df['MFI' + str(n)] = ta.MFI(df.high,
df.low,
df.close,
df.volume,
timeperiod=n)
#MINUS_DI
df['MINUS_DI' + str(n)] = ta.MINUS_DI(df.high,
df.low,
df.close,
timeperiod=n)
#MINUS_DM
df['MINUS_DM' + str(n)] = ta.MINUS_DM(df.high, df.low, timeperiod=n)
#MOM
df['MOM' + str(n)] = ta.MOM(df.close, timeperiod=n)
#PLUS_DI
df['PLUS_DI' + str(n)] = ta.PLUS_DI(df.high,
df.low,
df.close,
timeperiod=n)
#PLUS_DM
df['PLUS_DM' + str(n)] = ta.PLUS_DM(df.high, df.low, timeperiod=n)
#ROC
df['ROC' + str(n)] = ta.ROC(df.close, timeperiod=n)
#ROCP
df['ROCP' + str(n)] = ta.ROCP(df.close, timeperiod=n)
#ROCR
df['ROCR' + str(n)] = ta.ROCR(df.close, timeperiod=n)
#ROCR100
df['ROCR100_' + str(n)] = ta.ROCR100(df.close, timeperiod=n)
#RSI
df['RSI' + str(n)] = ta.RSI(df.close, timeperiod=n)
#TRIX
df['TRIX' + str(n)] = ta.TRIX(df.close, timeperiod=n)
#WILLR
df['WILLR' + str(n)] = ta.WILLR(df.high,
df.low,
df.close,
timeperiod=n)
#Volatility Indicator Functions
#ATR
df['ATR' + str(n)] = ta.ATR(df.high, df.low, df.close, timeperiod=n)
#NATR
df['NATR' + str(n)] = ta.NATR(df.high, df.low, df.close, timeperiod=n)
# Statistic Functions
#BETA
df['BETA' + str(n)] = ta.BETA(df.high, df.low, timeperiod=n)
#CORREL
df['CORREL' + str(n)] = ta.CORREL(df.high, df.low, timeperiod=n)
#LINEARREG
df['LINEARREG' + str(n)] = ta.LINEARREG(df.close, timeperiod=n)
#LINEARREG_ANGLE
df['LINEARREG_ANGLE' + str(n)] = ta.LINEARREG_ANGLE(df.close,
timeperiod=n)
#LINEARREG_INTERCEPT
df['LINEARREG_INTERCEPT' + str(n)] = ta.LINEARREG_INTERCEPT(
df.close, timeperiod=n)
#LINEARREG_SLOPE
df['LINEARREG_SLOPE' + str(n)] = ta.LINEARREG_SLOPE(df.close,
timeperiod=n)
#STDDEV
df['STDDEV' + str(n)] = ta.STDDEV(df.close, timeperiod=n, nbdev=1)
#TSF
df['TSF' + str(n)] = ta.TSF(df.close, timeperiod=n)
#VAR
df['VAR' + str(n)] = ta.VAR(df.close, timeperiod=n, nbdev=1)
# Math Operator Functions
#MAX
df['MAX' + str(n)] = ta.MAX(df.close, timeperiod=n)
#MAXINDEX
#df['MAXINDEX' + str(n)] = ta.MAXINDEX(df.close, timeperiod=n)
#MIN
df['MIN' + str(n)] = ta.MIN(df.close, timeperiod=n)
#MININDEX
#df['MININDEX' + str(n)] = ta.MININDEX(df.close, timeperiod=n)
#SUM
#df['SUM' + str(n)] = ta.SUM(df.close, timeperiod=n)
# HT_TRENDLINE
df['HT_TRENDLINE'] = ta.HT_TRENDLINE(df.close)
# BOP
df['BOP'] = ta.BOP(df.open, df.high, df.low, df.close)
# MACD
df['MACD'], df['MACD_SIGNAL'], df['MACD_HIST'] = ta.MACD(df.close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
# STOCH
df['slowk'], df['slowd'] = ta.STOCH(df.high,
df.low,
df.close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
# STOCHF
df['fsatk'], df['fastd'] = ta.STOCHF(df.high,
df.low,
df.close,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
# STOCHRSI
df['fsatk_RSI'], df['fastd_RSI'] = ta.STOCHRSI(df.close,
timeperiod=n,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
# PPO
df['PPO'] = ta.PPO(df.close, fastperiod=12, slowperiod=26, matype=0)
# ULTOSC
df['ULTOSC'] = ta.ULTOSC(df.high,
df.low,
df.close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
# Volume Indicator Functions
# AD
#df['AD'] = ta.AD(df.high, df.low, df.close, df.volume)
# ADOSC
#df['ADOSC'] = ta.ADOSC(df.high, df.low, df.close, df.volume, fastperiod=3, slowperiod=10)
# OBV
# OBV is too large
#df['OBV'] = ta.OBV(df.close, df.volume)
# TRANGE
df['TRANGE'] = ta.TRANGE(df.high, df.low, df.close)
# Price Indicator Functions
# AVGPRICE
df['AVGPRICE'] = ta.AVGPRICE(df.open, df.high, df.low, df.close)
# MEDPRICE
df['MEDPRICE'] = ta.MEDPRICE(df.high, df.low)
# TYPPRICE
df['TYPPRICE'] = ta.TYPPRICE(df.high, df.low, df.close)
# WCLPRICE
df['WCLPRICE'] = ta.WCLPRICE(df.high, df.low, df.close)
# Cycle Indicator Functions
# HT_DCPERIOD
df['HT_DCPERIOD'] = ta.HT_DCPERIOD(df.close)
# HT_DCPHASE
df['HT_DCPHASE'] = ta.HT_DCPHASE(df.close)
# HT_PHASOR
df['HT_PHASOR_inphase'], df['HT_PHASOR_quadrature'] = ta.HT_PHASOR(
df.close)
# HT_SINE
df['HT_SINE_sine'], df['HT_SINE_leadsine'] = ta.HT_SINE(df.close)
# HT_TRENDMODE
df['HT_TRENDMODE'] = ta.HT_TRENDMODE(df.close)
#Indicators regardless of time_period
#Pattern Recognition Functions
#CDL2CROWS
'''df['CDL2CROWS'] = ta.CDL2CROWS(df.open, df.high, df.low, df.close)
# CDL3BLACKCROWS
df['CDL3BLACKCROWS'] = ta.CDL3BLACKCROWS(df.open, df.high, df.low, df.close)
# CDL3INSIDE
df['CDL3INSIDE'] = ta.CDL3INSIDE(df.open, df.high, df.low, df.close)
# CDL3LINESTRIKE
df['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(df.open, df.high, df.low, df.close)
# CDL3OUTSIDE
df['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(df.open, df.high, df.low, df.close)
# CDL3STARSINSOUTH
df['CDL3STARSINSOUTH'] = ta.CDL3STARSINSOUTH(df.open, df.high, df.low, df.close)
# CDL3WHITESOLDIERS
df['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(df.open, df.high, df.low, df.close)
# CDLABANDONEDBABY
df['CDLABANDONEDBABY'] = ta.CDLABANDONEDBABY(df.open, df.high, df.low, df.close, penetration=0)
# CDLADVANCEBLOCK
df['CDLADVANCEBLOCK'] = ta.CDLADVANCEBLOCK(df.open, df.high, df.low, df.close)
# CDLBELTHOLD
df['CDLBELTHOLD'] = ta.CDLBELTHOLD(df.open, df.high, df.low, df.close)
# CDLBREAKAWAY
df['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(df.open, df.high, df.low, df.close)
# CDLCLOSINGMARUBOZU
df['CDLCLOSINGMARUBOZU'] = ta.CDLCLOSINGMARUBOZU(df.open, df.high, df.low, df.close)
# CDLCONCEALBABYSWALL
df['CDLCONCEALBABYSWALL'] = ta.CDLCONCEALBABYSWALL(df.open, df.high, df.low, df.close)
# CDLCOUNTERATTACK
df['CDLCOUNTERATTACK'] = ta.CDLCOUNTERATTACK(df.open, df.high, df.low, df.close)
# CDLDARKCLOUDCOVER
df['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(df.open, df.high, df.low, df.close, penetration=0)
# CDLDOJI
df['CDLDOJI'] = ta.CDLDOJI(df.open, df.high, df.low, df.close)
# CDLDOJISTAR
df['CDLDOJISTAR'] = ta.CDLDOJISTAR(df.open, df.high, df.low, df.close)
# CDLDRAGONFLYDOJI
df['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(df.open, df.high, df.low, df.close)
# CDLENGULFING
df['CDLENGULFING'] = ta.CDLENGULFING(df.open, df.high, df.low, df.close)
# CDLEVENINGDOJISTAR
df['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(df.open, df.high, df.low, df.close, penetration=0)
# CDLEVENINGSTAR
df['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(df.open, df.high, df.low, df.close, penetration=0)
# CDLGAPSIDESIDEWHITE
df['CDLGAPSIDESIDEWHITE'] = ta.CDLGAPSIDESIDEWHITE(df.open, df.high, df.low, df.close)
# CDLGRAVESTONEDOJI
df['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(df.open, df.high, df.low, df.close)
# CDLHAMMER
df['CDLHAMMER'] = ta.CDLHAMMER(df.open, df.high, df.low, df.close)
# CDLHANGINGMAN
df['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(df.open, df.high, df.low, df.close)
# CDLHARAMI
df['CDLHARAMI'] = ta.CDLHARAMI(df.open, df.high, df.low, df.close)
# CDLHARAMICROSS
df['CDLHARAMICROSS'] = ta.CDLHARAMICROSS(df.open, df.high, df.low, df.close)
# CDLHIGHWAVE
df['CDLHIGHWAVE'] = ta.CDLHIGHWAVE(df.open, df.high, df.low, df.close)
# CDLHIKKAKE
df['CDLHIKKAKE'] = ta.CDLHIKKAKE(df.open, df.high, df.low, df.close)
# CDLHIKKAKEMOD
df['CDLHIKKAKEMOD'] = ta.CDLHIKKAKEMOD(df.open, df.high, df.low, df.close)
# CDLHOMINGPIGEON
df['CDLHOMINGPIGEON'] = ta.CDLHOMINGPIGEON(df.open, df.high, df.low, df.close)
# CDLIDENTICAL3CROWS
df['CDLIDENTICAL3CROWS'] = ta.CDLIDENTICAL3CROWS(df.open, df.high, df.low, df.close)
# CDLINNECK
df['CDLINNECK'] = ta.CDLINNECK(df.open, df.high, df.low, df.close)
# CDLINVERTEDHAMMER
df['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(df.open, df.high, df.low, df.close)
# CDLKICKING
df['CDLKICKING'] = ta.CDLKICKING(df.open, df.high, df.low, df.close)
# CDLKICKINGBYLENGTH
df['CDLKICKINGBYLENGTH'] = ta.CDLKICKINGBYLENGTH(df.open, df.high, df.low, df.close)
# CDLLADDERBOTTOM
df['CDLLADDERBOTTOM'] = ta.CDLLADDERBOTTOM(df.open, df.high, df.low, df.close)
# CDLLONGLEGGEDDOJI
df['CDLLONGLEGGEDDOJI'] = ta.CDLLONGLEGGEDDOJI(df.open, df.high, df.low, df.close)
# CDLLONGLINE
df['CDLLONGLINE'] = ta.CDLLONGLINE(df.open, df.high, df.low, df.close)
# CDLMARUBOZU
df['CDLMARUBOZU'] = ta.CDLMARUBOZU(df.open, df.high, df.low, df.close)
# CDLMATCHINGLOW
df['CDLMATCHINGLOW'] = ta.CDLMATCHINGLOW(df.open, df.high, df.low, df.close)
# CDLMATHOLD
df['CDLMATHOLD'] = ta.CDLMATHOLD(df.open, df.high, df.low, df.close, penetration=0)
# CDLMORNINGDOJISTAR
df['CDLMORNINGDOJISTAR'] = ta.CDLMORNINGDOJISTAR(df.open, df.high, df.low, df.close, penetration=0)
# CDLMORNINGSTAR
df['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(df.open, df.high, df.low, df.close, penetration=0)
# CDLONNECK
df['CDLONNECK'] = ta.CDLONNECK(df.open, df.high, df.low, df.close)
# CDLPIERCING
df['CDLPIERCING'] = ta.CDLPIERCING(df.open, df.high, df.low, df.close)
# CDLRICKSHAWMAN
df['CDLRICKSHAWMAN'] = ta.CDLRICKSHAWMAN(df.open, df.high, df.low, df.close)
# CDLRISEFALL3METHODS
df['CDLRISEFALL3METHODS'] = ta.CDLRISEFALL3METHODS(df.open, df.high, df.low, df.close)
# CDLSEPARATINGLINES
df['CDLSEPARATINGLINES'] = ta.CDLSEPARATINGLINES(df.open, df.high, df.low, df.close)
# CDLSHOOTINGSTAR
df['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(df.open, df.high, df.low, df.close)
# CDLSHORTLINE
df['CDLSHORTLINE'] = ta.CDLSHORTLINE(df.open, df.high, df.low, df.close)
# CDLSPINNINGTOP
df['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(df.open, df.high, df.low, df.close)
# CDLSTALLEDPATTERN
df['CDLSTALLEDPATTERN'] = ta.CDLSTALLEDPATTERN(df.open, df.high, df.low, df.close)
# CDLSTICKSANDWICH
df['CDLSTICKSANDWICH'] = ta.CDLSTICKSANDWICH(df.open, df.high, df.low, df.close)
# CDLTAKURI
df['CDLTAKURI'] = ta.CDLTAKURI(df.open, df.high, df.low, df.close)
# CDLTASUKIGAP
df['CDLTASUKIGAP'] = ta.CDLTASUKIGAP(df.open, df.high, df.low, df.close)
# CDLTHRUSTING
df['CDLTHRUSTING'] = ta.CDLTHRUSTING(df.open, df.high, df.low, df.close)
# CDLTRISTAR
df['CDLTRISTAR'] = ta.CDLTRISTAR(df.open, df.high, df.low, df.close)
# CDLUNIQUE3RIVER
df['CDLUNIQUE3RIVER'] = ta.CDLUNIQUE3RIVER(df.open, df.high, df.low, df.close)
# CDLUPSIDEGAP2CROWS
df['CDLUPSIDEGAP2CROWS'] = ta.CDLUPSIDEGAP2CROWS(df.open, df.high, df.low, df.close)
# CDLXSIDEGAP3METHODS
df['CDLXSIDEGAP3METHODS'] = ta.CDLXSIDEGAP3METHODS(df.open, df.high, df.low, df.close)'''
# Math Transform Functions
#ACOS
#df['ACOS'] = ta.ACOS(df.close)
#ASIN
#df['ASIN'] = ta.ASIN(df.close)
#ATAN
df['ATAN'] = ta.ATAN(df.close)
#CEIL
#df['CEIL'] = ta.CEIL(df.close)
#COS
df['COS'] = ta.COS(df.close)
#COSH
#df['COSH'] = ta.COSH(df.close)
#EXP
#df['EXP'] = ta.EXP(df.close)
#FLOOR
#df['FLOOR'] = ta.FLOOR(df.close)
#LN
df['LN'] = ta.LN(df.close)
#LOG10
df['LOG10'] = ta.LOG10(df.close)
#SIN
df['SIN'] = ta.SIN(df.close)
#SINH
#df['SINH'] = ta.SINH(df.close)
#SQRT
df['SQRT'] = ta.SQRT(df.close)
#TAN
df['TAN'] = ta.TAN(df.close)
#TANH
df['TANH'] = ta.TANH(df.close)
# Math Operator Functions
# ADD
#df['ADD'] = ta.ADD(df.high, df.low)
# DIV
#df['DIV'] = ta.DIV(df.high, df.low)
# MULT
#df['MULT'] = ta.MULT(df.high, df.low)
# ADD
#df['SUB'] = ta.SUB(df.high, df.low)
if divided_by_close:
bias = 1
df['BBANDS_5_UP'] = df['BBANDS_5_UP'] / df.close - bias
df['BBANDS_5_MID'] = df['BBANDS_5_MID'] / df.close - bias
df['BBANDS_5_LOW'] = df['BBANDS_5_LOW'] / df.close - bias
df['DEMA5'] = df['DEMA5'] / df.close - bias
df['EMA5'] = df['EMA5'] / df.close - bias
df['KAMA5'] = df['KAMA5'] / df.close - bias
df['MA5'] = df['MA5'] / df.close - bias
df['MIDPOINT5'] = df['MIDPOINT5'] / df.close - bias
df['MIDPRICE5'] = df['MIDPRICE5'] / df.close - bias
df['SMA5'] = df['SMA5'] / df.close - bias
df['T35'] = df['T35'] / df.close - bias
df['TEMA5'] = df['TEMA5'] / df.close - bias
df['TRIMA5'] = df['TRIMA5'] / df.close - bias
df['WMA5'] = df['WMA5'] / df.close - bias
df['LINEARREG5'] = df['LINEARREG5'] / df.close - bias
df['LINEARREG_INTERCEPT5'] = df[
'LINEARREG_INTERCEPT5'] / df.close - bias
df['TSF5'] = df['TSF5'] / df.close - bias
df['MAX5'] = df['MAX5'] / df.close - bias
df['MIN5'] = df['MIN5'] / df.close - bias
df['BBANDS_15_UP'] = df['BBANDS_15_UP'] / df.close - bias
df['BBANDS_15_MID'] = df['BBANDS_15_MID'] / df.close - bias
df['BBANDS_15_LOW'] = df['BBANDS_15_LOW'] / df.close - bias
df['DEMA15'] = df['DEMA15'] / df.close - bias
df['EMA15'] = df['EMA15'] / df.close - bias
df['KAMA15'] = df['KAMA15'] / df.close - bias
df['MA15'] = df['MA15'] / df.close - bias
df['MIDPOINT15'] = df['MIDPOINT15'] / df.close - bias
df['MIDPRICE15'] = df['MIDPRICE15'] / df.close - bias
df['SMA15'] = df['SMA15'] / df.close - bias
df['T315'] = df['T315'] / df.close - bias
df['TEMA15'] = df['TEMA15'] / df.close - bias
df['TRIMA15'] = df['TRIMA15'] / df.close - bias
df['WMA15'] = df['WMA15'] / df.close - bias
df['LINEARREG15'] = df['LINEARREG15'] / df.close - bias
df['LINEARREG_INTERCEPT15'] = df[
'LINEARREG_INTERCEPT15'] / df.close - bias
df['TSF15'] = df['TSF15'] / df.close - bias
df['MAX15'] = df['MAX15'] / df.close - bias
df['MIN15'] = df['MIN15'] / df.close - bias
df['BBANDS_30_UP'] = df['BBANDS_30_UP'] / df.close - bias
df['BBANDS_30_MID'] = df['BBANDS_30_MID'] / df.close - bias
df['BBANDS_30_LOW'] = df['BBANDS_30_LOW'] / df.close - bias
df['DEMA30'] = df['DEMA30'] / df.close - bias
df['EMA30'] = df['EMA30'] / df.close - bias
df['KAMA30'] = df['KAMA30'] / df.close - bias
df['MA30'] = df['MA30'] / df.close - bias
df['MIDPOINT30'] = df['MIDPOINT30'] / df.close - bias
df['MIDPRICE30'] = df['MIDPRICE30'] / df.close - bias
df['SMA30'] = df['SMA30'] / df.close - bias
df['T330'] = df['T330'] / df.close - bias
df['TEMA30'] = df['TEMA30'] / df.close - bias
df['TRIMA30'] = df['TRIMA30'] / df.close - bias
df['WMA30'] = df['WMA30'] / df.close - bias
df['LINEARREG_INTERCEPT30'] = df[
'LINEARREG_INTERCEPT30'] / df.close - bias
df['TSF30'] = df['TSF30'] / df.close - bias
df['MAX30'] = df['MAX30'] / df.close - bias
df['MIN30'] = df['MIN30'] / df.close - bias
df['HT_TRENDLINE'] = df['HT_TRENDLINE'] / df.close - bias
df['AVGPRICE'] = df['AVGPRICE'] / df.close - bias
df['MEDPRICE'] = df['MEDPRICE'] / df.close - bias
df['TYPPRICE'] = df['TYPPRICE'] / df.close - bias
df['WCLPRICE'] = df['WCLPRICE'] / df.close - bias
df['open'] = df['open'] / df.close - bias
df['high'] = df['high'] / df.close - bias
df['low'] = df['low'] / df.close - bias
