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 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 CEIL(close):
''' Vector Ceil 向上取整数
分组: Math Transform 数学变换
简介:
real = CEIL(close)
'''
return talib.CEIL(close)
def ceil(client, symbol, timeframe="6m", col="close"):
"""This will return a dataframe of
Vector Ceil
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.CEIL(df[col].values)
return pd.DataFrame({col: df[col].values, "ceil": x})
def CEIL(data, **kwargs):
_check_talib_presence()
prices = _extract_series(data)
return talib.CEIL(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)
def CEIL(Close):
return Close.apply(lambda col: ta.CEIL(col), axis=0)
def CEIL(self, name, **parameters):
data = self.__data[name]
return talib.CEIL(data, **parameters)
