def Math_Operators(dataframe):
#Math Operator Functions
#ADD - Vector Arithmetic Add
df[f'{ratio}_ADD'] = talib.ADD(High, Low)
#c - Vector Arithmetic Div
df[f'{ratio}_ADD'] = talib.DIV(High, Low)
#MAX - Highest value over a specified period
df[f'{ratio}_MAX'] = talib.MAX(Close, timeperiod=30)
#MAXINDEX - Index of Highest value over a specified period
#integer = MAXINDEX(Close, timeperiod=30)
#MIN - Lowest value over a specified period
df[f'{ratio}_MIN'] = talib.MIN(Close, timeperiod=30)
#MININDEX - Index of Lowest value over a specified period
integer = talib.MININDEX(Close, timeperiod=30)
#MINMAX - Lowest and Highest values over a specified period
min, max = talib.MINMAX(Close, timeperiod=30)
#MINMAXINDEX - Indexes of Lowest and Highest values over a specified period
minidx, maxidx = talib.MINMAXINDEX(Close, timeperiod=30)
#MULT - Vector Arithmetic Mult
df[f'{ratio}_MULT'] = talib.MULT(High, Low)
#SUB - Vector Arithmetic Substraction
df[f'{ratio}_SUB'] = talib.SUB(High, Low)
#SUM - Summation
df[f'{ratio}_SUM'] = talib.SUM(Close, timeperiod=30)
return
def RSV(self, stk_no, start='2008/01/01', end='2016/12/31', timeperiod=9):
realstart = day_back(start, timeperiod)
high = self.get_stock_data(stk_no, 'HighestPrice', realstart, end)
low = self.get_stock_data(stk_no, 'LowestPrice', realstart, end)
close = self.get_stock_data(stk_no, 'ClosePrice', realstart, end)
Max = talib.MAX(high, timeperiod=timeperiod)
Min = talib.MIN(low, timeperiod=timeperiod)
return talib.DIV(close - Min, Max - Min)[timeperiod:]
def calExitFilter(self, am, kind, period, threshold, cmiMAPeriod, gene4):
if kind == 0:
self.exitFilter = 1
elif kind == 1:
self.exitFilter = 1 if (ta.ADX(am.high, am.low, am.close,
period)[-1] > threshold) else 0
elif kind == 2:
diff = np.insert(abs(am.close[period:] - am.close[:-period]), 0,
[0 for _ in range(period)])
de = np.insert(ta.SUM(abs(am.close[1:] - am.close[:-1]), period),
0, 0)
ER = ta.DIV(diff, de) * 100
self.exitFilter = 1 if (ER[-1] > threshold) else 0
elif kind == 3:
diff = np.insert(abs(am.close[period:] - am.close[:-period]), 0,
[0 for _ in range(period)])
de = ta.MAX(am.close, period) - ta.MIN(am.close, period)
cmi = ta.MA(ta.DIV(diff, de) * 100, cmiMAPeriod)
self.exitFilter = 1 if (cmi[-1] > threshold) else 0
def DIV(high, low):
''' Vector Arithmetic Div 向量除法运算
分组: Math Operator 数学运算符
简介:
real = DIV(high, low)
'''
return talib.DIV(high, low)
def div(client, symbol, timeframe="6m", col1="open", col2="close"):
"""This will return a dataframe of
Vector Arithmetic Div
for the given symbol across the given timeframe
Args:
client (pyEX.Client); Client
symbol (string); Ticker
timeframe (string); timeframe to use, for pyEX.chart
col1 (string); column to use to calculate
col2 (string); column to use to calculate
Returns:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
x = t.DIV(df[col1].values, df[col2].values)
return pd.DataFrame({col1: df[col1].values, col2: df[col2].values, "div": x})
def DIV(data, **kwargs):
_check_talib_presence()
popen, phigh, plow, pclose, pvolume = _extract_ohlc(data)
return talib.DIV(phigh, plow, **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 DIV(High, Low):
real = pd.DataFrame()
for i in High.columns:
real[i] = ta.DIV(High[i], Low[i])
return real
def indicator(filename):
database = pd.read_csv(filename)
close = np.array(database.close)
high = np.array(database.high)
low = np.array(database.low)
volume = np.array(database.volume)
o = np.array(database.open)
#简单区分其到底处于什么区间内
Add('OPEN', o)
Add('HIGH', high)
Add('LOW', low)
Add('CLOSE', close)
Add('VOLUME', volume)
upperband, middleband, lowerband = talib.BBANDS(close,
timeperiod=20,
nbdevup=2,
nbdevdn=2,
matype=0)
Length = len(upperband)
increase = []
for i in range(0, Length):
if math.isnan(upperband[i]):
increase.append(np.nan)
else:
increase.append(upperband[i] - middleband[i])
Add('BBANDS', np.asarray(increase))
real = talib.DEMA(close, timeperiod=10)
real1 = talib.DEMA(close, timeperiod=20)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('DEMA', real0)
real = talib.EMA(close, timeperiod=5)
real1 = talib.EMA(close, timeperiod=10)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('EMA', real0)
#暂时不会用这个指标
real = talib.HT_TRENDLINE(close)
Add('HT_TRENDLINE', real)
real = talib.KAMA(close, timeperiod=30)
real1 = talib.KAMA(close, timeperiod=60)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('KAMA', real0)
real = talib.MA(close, timeperiod=7, matype=0)
real1 = talib.MA(close, timeperiod=14, matype=0)
real0 = []
for i in range(Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('MA', real0)
#暂时没找到怎么去用
mama, fama = talib.MAMA(close, fastlimit=0.5, slowlimit=0.05)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(mama[i] - fama[i])
else:
real0.append(np.nan)
Add('MAMA', np.asarray(real0))
#没找到
real = talib.MIDPOINT(close, timeperiod=14)
Add('MIDPOINT', real)
#没找到
real = talib.MIDPRICE(high, low, timeperiod=14)
Add('MIDPRICE', real)
real = talib.SAR(high, low, acceleration=0, maximum=0)
real0 = []
for i in range(0, Length):
if not math.isnan(real[i]):
real0.append(close[i] - real[i])
else:
real0.append(np.nan)
Add('SAR', real0)
#暂时不会
real = talib.SAREXT(high,
low,
startvalue=0,
offsetonreverse=0,
accelerationinitlong=0,
accelerationlong=0,
accelerationmaxlong=0,
accelerationinitshort=0,
accelerationshort=0,
accelerationmaxshort=0)
Add('SAREXT', real)
real = talib.SMA(close, timeperiod=3)
real1 = talib.SMA(close, timeperiod=5)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('SMA', real0)
#暂时不懂
real = talib.T3(close, timeperiod=5, vfactor=0)
Add('T3', real)
real = talib.TEMA(close, timeperiod=7)
real1 = talib.TEMA(close, timeperiod=14)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('TEMA', real0)
real = talib.TRIMA(close, timeperiod=7)
real1 = talib.TRIMA(close, timeperiod=14)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('TRIMA', real0)
real = talib.WMA(close, timeperiod=7)
real1 = talib.WMA(close, timeperiod=14)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('WMA', real0)
#ADX与ADXR的关系需要注意一下
real = talib.ADX(high, low, close, timeperiod=14)
Add('ADX', real)
real = talib.ADXR(high, low, close, timeperiod=14)
Add('ADXR', real)
#12个和26个简单移动平均线的差值
real = talib.APO(close, fastperiod=12, slowperiod=26, matype=0)
Add('APO', real)
'''
aroondown, aroonup = talib.AROON(high, low, timeperiod=14)
real0 = []
for i in range(0,Length):
if not(math.isnan(aroondown) or math.isnan(aroonup)):
real0.append(aroonup[i] - aroondown[i])
else:
real0.append(numpy.nan)
Add('AROON',numpy.asarray(real0))
'''
#AROONOSC就是Aroonup-aroondown
real = talib.AROONOSC(high, low, timeperiod=14)
Add('AROONOSC', real)
#不懂
real = talib.BOP(o, high, low, close)
Add('BOP', real)
#
real = talib.CCI(high, low, close, timeperiod=14)
Add('CCI', real)
real = talib.CMO(close, timeperiod=14)
Add('CMO', real)
#需要再考虑一下，因为DX代表的市场的活跃度
real = talib.DX(high, low, close, timeperiod=14)
Add('DX', real)
macd, macdsignal, macdhist = talib.MACD(close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
Add('MACD', macdhist)
macd, macdsignal, macdhist = talib.MACDEXT(close,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
Add('MACDEXT', macdhist)
macd, macdsignal, macdhist = talib.MACDFIX(close, signalperiod=9)
Add('MACDFIX', macdhist)
real = talib.MFI(high, low, close, volume, timeperiod=14)
real1 = talib.MA(real, 7)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('MFI', np.asarray(real0))
real = talib.MINUS_DI(high, low, close, timeperiod=14)
real1 = talib.PLUS_DI(high, low, close, timeperiod=14)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real1[i] - real[i])
else:
real0.append(np.nan)
Add('PLUS_DI', np.asarray(real0))
real = talib.MINUS_DM(high, low, timeperiod=14)
Add('MINUS_DM', real)
#虽然大概了解了规则，但在标普500上怎么用还不是很清楚
real = talib.MOM(close, timeperiod=14)
Add('MOM', real)
real = talib.PLUS_DM(high, low, timeperiod=14)
Add('PLUS_DM', real)
#暂时不用
real = talib.PPO(close, fastperiod=12, slowperiod=26, matype=0)
Add('PPO', real)
real = talib.ROC(close, timeperiod=14)
Add('ROC', real)
real = talib.ROCP(close, timeperiod=14)
Add('ROCP', real)
real = talib.ROCR(close, timeperiod=14)
Add('ROCR', real)
real = talib.ROCR100(close, timeperiod=14)
Add('ROCR100', real)
real = talib.RSI(close, timeperiod=14)
Add('RSI', real)
slowk, slowd = talib.STOCH(high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
slowj = []
for i in range(Length):
if not (math.isnan(slowk[i]) or math.isnan(slowd[i])):
slowj.append(3 * slowk[i] - 2 * slowd[i])
else:
slowj.append(np.nan)
Add('STOCH', np.asarray(slowj))
fastk, fastd = talib.STOCHF(high,
low,
close,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
fastj = []
for i in range(Length):
if not (math.isnan(fastk[i]) or math.isnan(fastd[i])):
fastj.append(3 * fastk[i] - 2 * fastd[i])
else:
fastj.append(np.nan)
Add('STOCHF', np.asarray(fastj))
fastk, fastd = talib.STOCHRSI(close,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
fastj = []
for i in range(Length):
if not (math.isnan(fastk[i]) or math.isnan(fastd[i])):
fastj.append(3 * fastk[i] - 2 * fastd[i])
else:
fastj.append(np.nan)
Add('STOCHRSI', np.asarray(fastj))
real = talib.TRIX(close, timeperiod=30)
real1 = talib.MA(real, 6)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i] or math.isnan(real1[i]))):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('TRIX', real)
real = talib.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
Add('ULTOSC', real)
real = talib.WILLR(high, low, close, timeperiod=14)
real0 = []
for i in range(0, Length):
if not math.isnan(real[i]):
if real[i] > -20:
real0.append(1.0)
elif real[i] < -80:
real0.append(-1.0)
else:
real0.append(0.0)
else:
real0.append(np.nan)
Add('WILLR', np.asarray(real0))
real = talib.AD(high, low, close, volume)
real1 = talib.MA(real, 6)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('AD', np.asarray(real0))
real = talib.ADOSC(high, low, close, volume, fastperiod=3, slowperiod=10)
Add('ADOSC', real)
#对于每个指标的处理还是很有问题的呀
real = talib.OBV(close, volume)
Add('OBV', real)
real = talib.ATR(high, low, close, timeperiod=14)
Add('ATR', real)
real = talib.NATR(high, low, close, timeperiod=14)
Add('NATR', real)
real = talib.TRANGE(high, low, close)
Add('TRANGE', real)
integer = talib.HT_TRENDMODE(close)
Add('HT_TRENDMODE', integer)
real = talib.LINEARREG_SLOPE(close, timeperiod=14)
Add('LINEARREG_SLOPE', real)
real = talib.STDDEV(close, timeperiod=5, nbdev=1)
Add('STDDEV', real)
real = talib.TSF(close, timeperiod=14)
Add('TSF', real)
real = talib.VAR(close, timeperiod=5, nbdev=1)
Add('VAR', real)
real = talib.MEDPRICE(high, low)
Add('MEDPRICE', real)
real = talib.TYPPRICE(high, low, close)
Add('TYPPRICE', real)
real = talib.WCLPRICE(high, low, close)
Add('WCLPRICE', real)
real = talib.DIV(high, low)
Add('DIV', real)
real = talib.MAX(close, timeperiod=30)
Add('MAX', real)
real = talib.MIN(close, timeperiod=30)
Add('MIN', real)
real = talib.SUB(high, low)
Add('SUB', real)
real = talib.SUM(close, timeperiod=30)
Add('SUM', real)
return [dict1, dict2]
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 Operator Functions ######
#########################################
#ADD - Vector Arithmetic Add
add = ta.ADD(high, low)
#DIV - Vector Arithmetic Div
div = ta.DIV(high, low)
#MAX - Highest value over a specified period
maxv = ta.MAX(close, timeperiod=30)
#MAXINDEX - Index of highest value over a specified period
maxindex = ta.MAXINDEX(close, timeperiod=30)
#MIN - Lowest value over a specified period
minv = ta.MIN(close, timeperiod=30)
#MININDEX - Index of lowest value over a specified period
minindex = ta.MININDEX(close, timeperiod=30)
#MINMAX - Lowest and highest values over a specified period
minsp, maxsp = ta.MINMAX(close, timeperiod=30)
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']]
#最高价与最低价之和
df['add']=ta.ADD(df.high,df.low)
#最高价与最低价之差
df['sub']=ta.SUB(df.high,df.low)
#最高价与最低价之乘
df['mult']=ta.MULT(df.high,df.low)
#最高价与最低价之除
df['div']=ta.DIV(df.high,df.low)
#收盘价的每30日移动求和
df['sum']=ta.SUM(df.close, timeperiod=30)
#收盘价的每30日内的最大最小值
df['min'], df['max'] = ta.MINMAX(df.close, timeperiod=30)
#收盘价的每30日内的最大最小值对应的索引值（第N行）
df['minidx'], df['maxidx'] = ta.MINMAXINDEX(df.close, timeperiod=30)
df.tail()
#将上述函数计算得到的结果进行可视化
df[['close','add','sub','mult','div','sum','min','max']].plot(figsize=(12,10),
subplots = True,
layout=(4, 2))
plt.subplots_adjust(wspace=0,hspace=0.2)
plt.show()
def DIV(self, name, **parameters):
data = self.__data[name]
return talib.DIV(data, **parameters)
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 Operator Functions ######
#########################################
#ADD - Vector Arithmetic Add
add = ta.ADD(high, low)
#DIV - Vector Arithmetic Div
div = ta.DIV(high, low)
#MAX - Highest value over a specified period
maxv = ta.MAX(close, timeperiod=30)
#MAXINDEX - Index of highest value over a specified period
maxindex = ta.MAXINDEX(close, timeperiod=30)
#MIN - Lowest value over a specified period
minv = ta.MIN(close, timeperiod=30)
#MININDEX - Index of lowest value over a specified period
minindex = ta.MININDEX(close, timeperiod=30)
#MINMAX - Lowest and highest values over a specified period
minsp, maxsp = ta.MINMAX(close, timeperiod=30)
#MINMAXINDEX - Indexes of lowest and highest values over a specified period
minidx, maxidx = ta.MINMAXINDEX(close, timeperiod=30)
#MULT - Vector Arithmetic Mult
mult = ta.MULT(high, low)
#SUB - Vector Arithmetic Substraction
sub = ta.SUB(high, low)
#SUM - Summation
sum = ta.SUM(close, timeperiod=30)
df_save = pd.DataFrame(
data={
'date': np.array(df['date']),
'add': add,
'div': div,
'max': maxv,
'maxindex': maxindex,
'min': minv,
'minindex': minindex,
'min_spec_period': minsp,
'max_spec_period': maxsp,
'minidx': minidx,
'maxidx': maxidx,
'mult': mult,
'sub': sub,
'sum': sum
})
df_save.to_csv(current_dir + "/" + base_dir + "/" + out_dir + '/' +
stock_symbol + "/" + out_dir + '_ta_math_operator_' +
stock_symbol + '.csv',
index=False)
