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)