def MAXINDEX(close, timeperiod=30): ''' Index of highest value over a specified period 周期内最大值的索引 分组: Math Operator 数学运算符 简介: integer = MAXINDEX(close, timeperiod=30) ''' return talib.MAXINDEX(close, timeperiod)
def maxindex(client, symbol, timeframe="6m", col="close", period=30): """This will return a dataframe of Highest value over a specified period 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 period (int); period Returns: DataFrame: result """ df = client.chartDF(symbol, timeframe) x = t.MAXINDEX(df[col].values, period) return x, df[col].values[x]
def math_operator_process(event): print(event.widget.get()) math_operator = 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_operator, fontproperties="SimHei") if math_operator == '指定的期间的最大值': real = ta.MAX(close, timeperiod=30) axes[1].plot(real, 'r-') elif math_operator == '指定的期间的最大值的索引': integer = ta.MAXINDEX(close, timeperiod=30) axes[1].plot(integer, 'r-') elif math_operator == '指定的期间的最小值': real = ta.MIN(close, timeperiod=30) axes[1].plot(real, 'r-') elif math_operator == '指定的期间的最小值的索引': integer = ta.MININDEX(close, timeperiod=30) axes[1].plot(integer, 'r-') elif math_operator == '指定的期间的最小和最大值': min, max = ta.MINMAX(close, timeperiod=30) axes[1].plot(min, 'r-') axes[1].plot(max, 'r-') elif math_operator == '指定的期间的最小和最大值的索引': minidx, maxidx = ta.MINMAXINDEX(close, timeperiod=30) axes[1].plot(minidx, 'r-') axes[1].plot(maxidx, 'r-') elif math_operator == '合计': real = ta.SUM(close, timeperiod=30) axes[1].plot(real, 'r-') plt.show()
def TALIB_MAXINDEX(close, timeperiod=30): '''00358,2,1''' return talib.MAXINDEX(close, timeperiod)
def MAXINDEX(data, **kwargs): _check_talib_presence() prices = _extract_series(data) return talib.MAXINDEX(prices, **kwargs)
def appendAllTAData(df=pd.DataFrame([])): resDF = pd.DataFrame([]) # 函数名:AD名称:ChaikinA/DLine累积/派发线(Accumulation/DistributionLine) # 简介:MarcChaikin提出的一种平衡交易量指标,以当日的收盘价位来估算成交流量,用于估定一段时间内该证券累积的资金流量。 # 计算公式:A/D=昨日A/D+多空对比*今日成交量多空对比=[(收盘价-最低价)-(最高价-收盘价)]/(最高价-最低价) # 若最高价等于最低价:多空对比=(收盘价/昨收盘)-1 # 研判:1、A/D测量资金流向,向上的A/D表明买方占优势,而向下的A/D表明卖方占优势 # 2、A/D与价格的背离可视为买卖信号,即底背离考虑买入,顶背离考虑卖出 # 3、应当注意A/D忽略了缺口的影响,事实上,跳空缺口的意义是不能轻易忽略的 # A/D指标无需设置参数,但在应用时,可结合指标的均线进行分析例子:real=AD(high,low,close,volume) resDF['AD'] = ta.AD(df['max_price'].values, df['min_price'].values, df['price'].values, df['vol'].values) # 函数名:ADOSC名称:Chaikin A/D Oscillator Chaikin震荡指标 # 简介:将资金流动情况与价格行为相对比,检测市场中资金流入和流出的情况 # 计算公式:fastperiod A/D - slowperiod A/D # 研判:1、交易信号是背离:看涨背离做多,看跌背离做空 # 2、股价与90天移动平均结合,与其他指标结合 # 3、由正变负卖出,由负变正买进 # 例子:real = ADOSC(high, low, close, volume, fastperiod=3, slowperiod=10) resDF['ADOSC'] = ta.ADOSC(df['max_price'].values, df['min_price'].values, df['price'].values, df['vol'].values, fastperiod=3, slowperiod=10) resDF['ADX'] = ta.ADX(df['max_price'].values, df['min_price'].values, df['price'].values) resDF['ADXR'] = ta.ADXR(df['max_price'].values, df['min_price'].values, df['price'].values, timeperiod=14) resDF['APO'] = ta.APO(df['price'].values, fastperiod=12, slowperiod=26, matype=0) resDF['aroondown'], resDF['aroonup'] = ta.AROON(df['max_price'].values, df['min_price'].values, timeperiod=14) resDF['AROONOSC'] = ta.AROONOSC(df['max_price'].values, df['min_price'].values, timeperiod=14) resDF['ATR'] = ta.ATR(df['max_price'].values, df['min_price'].values, df['price'].values, timeperiod=14) resDF['AVGPRICE'] = ta.AVGPRICE(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) resDF['upperband'], resDF['middleband'], resDF['lowerband'] = ta.BBANDS( df['price'].values, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0) resDF['BETA'] = ta.BETA(df['max_price'].values, df['min_price'].values, timeperiod=5) resDF['BOP'] = ta.BOP(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) resDF['CCI'] = ta.CCI(df['max_price'].values, df['min_price'].values, df['price'].values, timeperiod=10)[-1] # 函数名:CDL2CROWS名称:Two Crows 两只乌鸦 # 简介:三日K线模式,第一天长阳,第二天高开收阴,第三天再次高开继续收阴,收盘比前一日收盘价低,预示股价下跌。 # 例子:integer = CDL2CROWS(open, high, low, close) resDF['CDL2CROWS'] = ta.CDL2CROWS(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDL3BLACKCROWS名称:Three Black Crows 三只乌鸦 # 简介:三日K线模式,连续三根阴线,每日收盘价都下跌且接近最低价,每日开盘价都在上根K线实体内,预示股价下跌。 # 例子:integer = CD3BLACKCROWS(open, high, low, close) resDF['CDL3BLACKCROWS'] = ta.CDL3BLACKCROWS(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDL3INSIDE名称: Three Inside Up/Down 三内部上涨和下跌 # 简介:三日K线模式,母子信号+长K线,以三内部上涨为例,K线为阴阳阳,第三天收盘价高于第一天开盘价,第二天K线在第一天K线内部,预示着股价上涨。 # 例子:integer = CDL3INSIDE(open, high, low, close) resDF['CDL3INSIDE'] = ta.CDL3INSIDE(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDL3LINESTRIKE名称: Three-Line Strike 三线打击 # 简介:四日K线模式,前三根阳线,每日收盘价都比前一日高,开盘价在前一日实体内,第四日市场高开,收盘价低于第一日开盘价,预示股价下跌。 # 例子:integer = CDL3LINESTRIKE(open, high, low, close) resDF['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDL3OUTSIDE名称:Three Outside Up/Down 三外部上涨和下跌 # 简介:三日K线模式,与三内部上涨和下跌类似,K线为阴阳阳,但第一日与第二日的K线形态相反,以三外部上涨为例,第一日K线在第二日K线内部,预示着股价上涨。 # 例子:integer = CDL3OUTSIDE(open, high, low, close) resDF['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDL3STARSINSOUTH名称:Three Stars In The South 南方三星 # 简介:三日K线模式,与大敌当前相反,三日K线皆阴,第一日有长下影线,第二日与第一日类似,K线整体小于第一日,第三日无下影线实体信号,成交价格都在第一日振幅之内,预示下跌趋势反转,股价上升。 # 例子:integer = CDL3STARSINSOUTH(open, high, low, close) resDF['CDL3STARSINSOUTH'] = ta.CDL3STARSINSOUTH( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDL3WHITESOLDIERS名称:Three Advancing White Soldiers 三个白兵 # 简介:三日K线模式,三日K线皆阳,每日收盘价变高且接近最高价,开盘价在前一日实体上半部,预示股价上升。 # 例子:integer = CDL3WHITESOLDIERS(open, high, low, close) resDF['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLABANDONEDBABY名称:Abandoned Baby 弃婴 # 简介:三日K线模式,第二日价格跳空且收十字星(开盘价与收盘价接近,最高价最低价相差不大),预示趋势反转,发生在顶部下跌,底部上涨。 # 例子:integer = CDLABANDONEDBABY(open, high, low, close, penetration=0) resDF['CDLABANDONEDBABY'] = ta.CDLABANDONEDBABY( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values, penetration=0) # 函数名:CDLADVANCEBLOCK名称:Advance Block 大敌当前 # 简介:三日K线模式,三日都收阳,每日收盘价都比前一日高,开盘价都在前一日实体以内,实体变短,上影线变长。 # 例子:integer = CDLADVANCEBLOCK(open, high, low, close) resDF['CDLADVANCEBLOCK'] = ta.CDLADVANCEBLOCK( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLBELTHOLD名称:Belt-hold 捉腰带线 # 简介:两日K线模式,下跌趋势中,第一日阴线,第二日开盘价为最低价,阳线,收盘价接近最高价,预示价格上涨。 # 例子:integer = CDLBELTHOLD(open, high, low, close) resDF['CDLBELTHOLD'] = ta.CDLBELTHOLD(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLBREAKAWAY名称:Breakaway 脱离 # 简介:五日K线模式,以看涨脱离为例,下跌趋势中,第一日长阴线,第二日跳空阴线,延续趋势开始震荡,第五日长阳线,收盘价在第一天收盘价与第二天开盘价之间,预示价格上涨。 # 例子:integer = CDLBREAKAWAY(open, high, low, close) resDF['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名: CDLCLOSINGMARUBOZU 名称:Closing Marubozu 收盘缺影线 # 简介:一日K线模式,以阳线为例,最低价低于开盘价,收盘价等于最高价,预示着趋势持续。 # 例子:integer = CDLCLOSINGMARUBOZU(open, high, low, close) resDF['CDLCLOSINGMARUBOZU'] = ta.CDLCLOSINGMARUBOZU( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLCONCEALBABYSWALL名称: Concealing Baby Swallow 藏婴吞没 # 简介:四日K线模式,下跌趋势中,前两日阴线无影线,第二日开盘、收盘价皆低于第二日,第三日倒锤头,第四日开盘价高于前一日最高价,收盘价低于前一日最低价,预示着底部反转。 # 例子:integer = CDLCONCEALBABYSWALL(open, high, low, close) resDF['CDLCONCEALBABYSWALL'] = ta.CDLCONCEALBABYSWALL( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLCOUNTERATTACK # 名称:Counterattack 反击线 # 简介:二日K线模式,与分离线类似。 # 例子:integer = CDLCOUNTERATTACK(open, high, low, close) resDF['CDLCOUNTERATTACK'] = ta.CDLCOUNTERATTACK( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLDARKCLOUDCOVER名称:Dark Cloud Cover 乌云压顶 # 简介:二日K线模式,第一日长阳,第二日开盘价高于前一日最高价,收盘价处于前一日实体中部以下,预示着股价下跌。 # 例子:integer = CDLDARKCLOUDCOVER(open, high, low, close, penetration=0) resDF['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values, penetration=0) # 函数名: CDLDOJI # 名称:Doji 十字 # 简介:一日K线模式,开盘价与收盘价基本相同。 # 例子:integer = CDLDOJI(open, high, low, close) resDF['CDLDOJI'] = ta.CDLDOJI(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名: CDLDOJISTAR # 名称:Doji Star 十字星 # 简介:一日K线模式,开盘价与收盘价基本相同,上下影线不会很长,预示着当前趋势反转。 # 例子:integer = CDLDOJISTAR(open, high, low, close) resDF['CDLDOJISTAR'] = ta.CDLDOJISTAR(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLDRAGONFLYDOJI名称:Dragonfly Doji 蜻蜓十字/T形十字 # 简介:一日K线模式,开盘后价格一路走低,之后收复,收盘价与开盘价相同,预示趋势反转。 # 例子:integer = CDLDRAGONFLYDOJI(open, high, low, close) resDF['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLENGULFING名称:Engulfing Pattern 吞噬模式 # 简介:两日K线模式,分多头吞噬和空头吞噬,以多头吞噬为例,第一日为阴线,第二日阳线,第一日的开盘价和收盘价在第二日开盘价收盘价之内,但不能完全相同。 # 例子:integer = CDLENGULFING(open, high, low, close) resDF['CDLENGULFING'] = ta.CDLENGULFING(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLEVENINGDOJISTAR名称:Evening Doji Star 十字暮星 # 简介:三日K线模式,基本模式为暮星,第二日收盘价和开盘价相同,预示顶部反转。 # 例子:integer = CDLEVENINGDOJISTAR(open, high, low, close, penetration=0) resDF['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values, penetration=0) # 函数名:CDLEVENINGSTAR名称:Evening Star 暮星 # 简介:三日K线模式,与晨星相反,上升趋势中,第一日阳线,第二日价格振幅较小,第三日阴线,预示顶部反转。 # 例子:integer = CDLEVENINGSTAR(open, high, low, close, penetration=0) resDF['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values, penetration=0) # 函数名:CDLGAPSIDESIDEWHITE名称:Up/Down-gap side-by-side white lines 向上/下跳空并列阳线 # 简介:二日K线模式,上升趋势向上跳空,下跌趋势向下跳空,第一日与第二日有相同开盘价,实体长度差不多,则趋势持续。 # 例子:integer = CDLGAPSIDESIDEWHITE(open, high, low, close) resDF['CDLGAPSIDESIDEWHITE'] = ta.CDLGAPSIDESIDEWHITE( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLGRAVESTONEDOJI名称:Gravestone Doji 墓碑十字/倒T十字 # 简介:一日K线模式,开盘价与收盘价相同,上影线长,无下影线,预示底部反转。 # 例子:integer = CDLGRAVESTONEDOJI(open, high, low, close) resDF['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLHAMMER # 名称:Hammer 锤头 # 简介:一日K线模式,实体较短,无上影线,下影线大于实体长度两倍,处于下跌趋势底部,预示反转。 # 例子:integer = CDLHAMMER(open, high, low, close) resDF['CDLHAMMER'] = ta.CDLHAMMER(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLHANGINGMAN # 名称:Hanging Man 上吊线 # 简介:一日K线模式,形状与锤子类似,处于上升趋势的顶部,预示着趋势反转。 # 例子:integer = CDLHANGINGMAN(open, high, low, close) resDF['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLHARAMI名称:Harami Pattern 母子线 # 简介:二日K线模式,分多头母子与空头母子,两者相反,以多头母子为例,在下跌趋势中,第一日K线长阴,第二日开盘价收盘价在第一日价格振幅之内,为阳线,预示趋势反转,股价上升。 # 例子:integer = CDLHARAMI(open, high, low, close) resDF['CDLHARAMI'] = ta.CDLHARAMI(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLHARAMICROSS名称:Harami Cross Pattern 十字孕线 # 简介:二日K线模式,与母子县类似,若第二日K线是十字线,便称为十字孕线,预示着趋势反转。 # 例子:integer = CDLHARAMICROSS(open, high, low, close) resDF['CDLHARAMICROSS'] = ta.CDLHARAMICROSS(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLHIGHWAVE # 名称:High-Wave Candle 风高浪大线 # 简介:三日K线模式,具有极长的上/下影线与短的实体,预示着趋势反转。 # 例子:integer = CDLHIGHWAVE(open, high, low, close) resDF['CDLHIGHWAVE'] = ta.CDLHIGHWAVE(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLHIKKAKE名称:Hikkake Pattern 陷阱 # 简介:三日K线模式,与母子类似,第二日价格在前一日实体范围内,第三日收盘价高于前两日,反转失败,趋势继续。 # 例子:integer = CDLHIKKAKE(open, high, low, close) resDF['CDLHIKKAKE'] = ta.CDLHIKKAKE(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLHIKKAKEMOD名称:Modified Hikkake Pattern 修正陷阱 # 简介:三日K线模式,与陷阱类似,上升趋势中,第三日跳空高开;下跌趋势中,第三日跳空低开,反转失败,趋势继续。 # 例子:integer = CDLHIKKAKEMOD(open, high, low, close) resDF['CDLHIKKAKEMOD'] = ta.CDLHIKKAKEMOD(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLHOMINGPIGEON名称:Homing Pigeon 家鸽 # 简介:二日K线模式,与母子线类似,不同的的是二日K线颜色相同,第二日最高价、最低价都在第一日实体之内,预示着趋势反转。 # 例子:integer = CDLHOMINGPIGEON(open, high, low, close) resDF['CDLHOMINGPIGEON'] = ta.CDLHOMINGPIGEON( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLIDENTICAL3CROWS名称:Identical Three Crows 三胞胎乌鸦 # 简介:三日K线模式,上涨趋势中,三日都为阴线,长度大致相等,每日开盘价等于前一日收盘价,收盘价接近当日最低价,预示价格下跌。 # 例子:integer = CDLIDENTICAL3CROWS(open, high, low, close) resDF['CDLIDENTICAL3CROWS'] = ta.CDLIDENTICAL3CROWS( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLINNECK名称:In-Neck Pattern 颈内线 # 简介:二日K线模式,下跌趋势中,第一日长阴线,第二日开盘价较低,收盘价略高于第一日收盘价,阳线,实体较短,预示着下跌继续。 # 例子:integer = CDLINNECK(open, high, low, close) resDF['CDLINNECK'] = ta.CDLINNECK(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLINVERTEDHAMMER名称:Inverted Hammer 倒锤头 # 简介:一日K线模式,上影线较长,长度为实体2倍以上,无下影线,在下跌趋势底部,预示着趋势反转。 # 例子:integer = CDLINVERTEDHAMMER(open, high, low, close) resDF['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLKICKING # 名称:Kicking 反冲形态 # 简介:二日K线模式,与分离线类似,两日K线为秃线,颜色相反,存在跳空缺口。 # 例子:integer = CDLKICKING(open, high, low, close) resDF['CDLKICKING'] = ta.CDLKICKING(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLKICKINGBYLENGTH名称:Kicking - bull/bear determined by the longer marubozu 由较长缺影线决定的反冲形态 # 简介:二日K线模式,与反冲形态类似,较长缺影线决定价格的涨跌。 # 例子:integer = CDLKICKINGBYLENGTH(open, high, low, close) resDF['CDLKICKINGBYLENGTH'] = ta.CDLKICKINGBYLENGTH( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLLADDERBOTTOM名称:Ladder Bottom 梯底 # 简介:五日K线模式,下跌趋势中,前三日阴线,开盘价与收盘价皆低于前一日开盘、收盘价,第四日倒锤头,第五日开盘价高于前一日开盘价,阳线,收盘价高于前几日价格振幅,预示着底部反转。 # 例子:integer = CDLLADDERBOTTOM(open, high, low, close) resDF['CDLLADDERBOTTOM'] = ta.CDLLADDERBOTTOM( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLLONGLEGGEDDOJI名称:Long Legged Doji 长脚十字 # 简介:一日K线模式,开盘价与收盘价相同居当日价格中部,上下影线长,表达市场不确定性。 # 例子:integer = CDLLONGLEGGEDDOJI(open, high, low, close) resDF['CDLLONGLEGGEDDOJI'] = ta.CDLLONGLEGGEDDOJI( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLLONGLINE # 名称:Long Line Candle 长蜡烛 # 简介:一日K线模式,K线实体长,无上下影线。 # 例子:integer = CDLLONGLINE(open, high, low, close) resDF['CDLLONGLINE'] = ta.CDLLONGLINE(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLMARUBOZU # 名称:Marubozu 光头光脚/缺影线 # 简介:一日K线模式,上下两头都没有影线的实体,阴线预示着熊市持续或者牛市反转,阳线相反。 # 例子:integer = CDLMARUBOZU(open, high, low, close) resDF['CDLMARUBOZU'] = ta.CDLMARUBOZU(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLMATCHINGLOW名称:Matching Low 相同低价 # 简介:二日K线模式,下跌趋势中,第一日长阴线,第二日阴线,收盘价与前一日相同,预示底部确认,该价格为支撑位。 # 例子:integer = CDLMATCHINGLOW(open, high, low, close) resDF['CDLMATCHINGLOW'] = ta.CDLMATCHINGLOW(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLMATHOLD名称:Mat Hold 铺垫 # 简介:五日K线模式,上涨趋势中,第一日阳线,第二日跳空高开影线,第三、四日短实体影线,第五日阳线,收盘价高于前四日,预示趋势持续。 # 例子:integer = CDLMATHOLD(open, high, low, close, penetration=0) resDF['CDLMATHOLD'] = ta.CDLMATHOLD(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values, penetration=0) # 函数名:CDLMORNINGDOJISTAR名称:Morning Doji Star 十字晨星 # 简介:三日K线模式,基本模式为晨星,第二日K线为十字星,预示底部反转。 # 例子:integer = CDLMORNINGDOJISTAR(open, high, low, close, penetration=0) resDF['CDLMORNINGDOJISTAR'] = ta.CDLMORNINGDOJISTAR( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values, penetration=0) # 函数名:CDLMORNINGSTAR名称:Morning Star 晨星 # 简介:三日K线模式,下跌趋势,第一日阴线,第二日价格振幅较小,第三天阳线,预示底部反转。 # 例子:integer = CDLMORNINGSTAR(open, high, low, close, penetration=0) resDF['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values, penetration=0) # 函数名:CDLONNECK名称:On-Neck Pattern 颈上线 # 简介:二日K线模式,下跌趋势中,第一日长阴线,第二日开盘价较低,收盘价与前一日最低价相同,阳线,实体较短,预示着延续下跌趋势。 # 例子:integer = CDLONNECK(open, high, low, close) resDF['CDLONNECK'] = ta.CDLONNECK(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLPIERCING名称:Piercing Pattern 刺透形态 # 简介:两日K线模式,下跌趋势中,第一日阴线,第二日收盘价低于前一日最低价,收盘价处在第一日实体上部,预示着底部反转。 # 例子:integer = CDLPIERCING(open, high, low, close) resDF['CDLPIERCING'] = ta.CDLPIERCING(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLRICKSHAWMAN名称:Rickshaw Man 黄包车夫 # 简介:一日K线模式,与长腿十字线类似,若实体正好处于价格振幅中点,称为黄包车夫。 # 例子:integer = CDLRICKSHAWMAN(open, high, low, close) resDF['CDLRICKSHAWMAN'] = ta.CDLRICKSHAWMAN(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLRISEFALL3METHODS名称:Rising/Falling Three Methods 上升/下降三法 # 简介: 五日K线模式,以上升三法为例,上涨趋势中,第一日长阳线,中间三日价格在第一日范围内小幅震荡,第五日长阳线,收盘价高于第一日收盘价,预示股价上升。 # 例子:integer = CDLRISEFALL3METHODS(open, high, low, close) resDF['CDLRISEFALL3METHODS'] = ta.CDLRISEFALL3METHODS( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLSEPARATINGLINES名称:Separating Lines 分离线 # 简介:二日K线模式,上涨趋势中,第一日阴线,第二日阳线,第二日开盘价与第一日相同且为最低价,预示着趋势继续。 # 例子:integer = CDLSEPARATINGLINES(open, high, low, close) resDF['CDLSEPARATINGLINES'] = ta.CDLSEPARATINGLINES( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLSHOOTINGSTAR名称:Shooting Star 射击之星 # 简介:一日K线模式,上影线至少为实体长度两倍,没有下影线,预示着股价下跌 # 例子:integer = CDLSHOOTINGSTAR(open, high, low, close) resDF['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLSHORTLINE # 名称:Short Line Candle 短蜡烛 # 简介:一日K线模式,实体短,无上下影线。 # 例子:integer = CDLSHORTLINE(open, high, low, close) resDF['CDLSHORTLINE'] = ta.CDLSHORTLINE(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLSPINNINGTOP # 名称:Spinning Top 纺锤 # 简介:一日K线,实体小。 # 例子:integer = CDLSPINNINGTOP(open, high, low, close) resDF['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLSTALLEDPATTERN名称:Stalled Pattern 停顿形态 # 简介:三日K线模式,上涨趋势中,第二日长阳线,第三日开盘于前一日收盘价附近,短阳线,预示着上涨结束。 # 例子:integer = CDLSTALLEDPATTERN(open, high, low, close) resDF['CDLSTALLEDPATTERN'] = ta.CDLSTALLEDPATTERN( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLSTICKSANDWICH名称:Stick Sandwich 条形三明治 # 简介:三日K线模式,第一日长阴线,第二日阳线,开盘价高于前一日收盘价,第三日开盘价高于前两日最高价,收盘价于第一日收盘价相同。 # 例子:integer = CDLSTICKSANDWICH(open, high, low, close) resDF['CDLSTICKSANDWICH'] = ta.CDLSTICKSANDWICH( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLTAKURI名称:Takuri (Dragonfly Doji with very long lower shadow) 探水竿 # 简介:一日K线模式,大致与蜻蜓十字相同,下影线长度长。 # 例子:integer = CDLTAKURI(open, high, low, close) resDF['CDLTAKURI'] = ta.CDLTAKURI(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLTASUKIGAP名称:Tasuki Gap 跳空并列阴阳线 # 简介:三日K线模式,分上涨和下跌,以上升为例,前两日阳线,第二日跳空,第三日阴线,收盘价于缺口中,上升趋势持续。 # 例子:integer = CDLTASUKIGAP(open, high, low, close) resDF['CDLTASUKIGAP'] = ta.CDLTASUKIGAP(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLTHRUSTING名称:Thrusting Pattern 插入 # 简介:二日K线模式,与颈上线类似,下跌趋势中,第一日长阴线,第二日开盘价跳空,收盘价略低于前一日实体中部,与颈上线相比实体较长,预示着趋势持续。 # 例子:integer = CDLTHRUSTING(open, high, low, close) resDF['CDLTHRUSTING'] = ta.CDLTHRUSTING(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLTRISTAR # 名称:Tristar Pattern 三星 # 简介:三日K线模式,由三个十字组成,第二日十字必须高于或者低于第一日和第三日,预示着反转。 # 例子:integer = CDLTRISTAR(open, high, low, close) resDF['CDLTRISTAR'] = ta.CDLTRISTAR(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLUNIQUE3RIVER名称:Unique 3 River 奇特三河床 # 简介:三日K线模式,下跌趋势中,第一日长阴线,第二日为锤头,最低价创新低,第三日开盘价低于第二日收盘价,收阳线,收盘价不高于第二日收盘价,预示着反转,第二日下影线越长可能性越大。 # 例子:integer = CDLUNIQUE3RIVER(open, high, low, close) resDF['CDLUNIQUE3RIVER'] = ta.CDLUNIQUE3RIVER( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLUPSIDEGAP2CROWS名称:Upside Gap Two Crows 向上跳空的两只乌鸦 # 简介:三日K线模式,第一日阳线,第二日跳空以高于第一日最高价开盘,收阴线,第三日开盘价高于第二日,收阴线,与第一日比仍有缺口。 # 例子:integer = CDLUPSIDEGAP2CROWS(open, high, low, close) resDF['CDLUPSIDEGAP2CROWS'] = ta.CDLUPSIDEGAP2CROWS( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) # 函数名:CDLXSIDEGAP3METHODS名称:Upside/Downside Gap Three Methods 上升/下降跳空三法 # 简介:五日K线模式,以上升跳空三法为例,上涨趋势中,第一日长阳线,第二日短阳线,第三日跳空阳线,第四日阴线,开盘价与收盘价于前两日实体内,第五日长阳线,收盘价高于第一日收盘价,预示股价上升。 # 例子:integer = CDLXSIDEGAP3METHODS(open, high, low, close) resDF['CDLXSIDEGAP3METHODS'] = ta.CDLXSIDEGAP3METHODS( df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values) resDF['CMO'] = ta.CMO(df['price'].values, timeperiod=14) resDF['CORREL'] = ta.CORREL(df['max_price'].values, df['min_price'].values, timeperiod=30) resDF['DEMA'] = ta.DEMA(df['price'].values, timeperiod=30) resDF['DX'] = ta.DX(df['max_price'].values, df['min_price'].values, df['price'].values, timeperiod=14) resDF['EMA'] = ta.EMA(df['price'].values, timeperiod=30) resDF['HT_DCPERIOD'] = ta.HT_DCPERIOD(df['price'].values) resDF['HT_DCPHASE'] = ta.HT_DCPHASE(df['price'].values) resDF['inphase'], resDF['quadrature'] = ta.HT_PHASOR(df['price'].values) resDF['sine'], resDF['leadsine'] = ta.HT_SINE(df['price'].values) resDF['HT_TRENDLINE'] = ta.HT_TRENDLINE(df['price'].values) resDF['HT_TRENDMODE'] = ta.HT_TRENDMODE(df['price'].values) resDF['KAMA'] = ta.KAMA(df['price'].values, timeperiod=30) resDF['LINEARREG'] = ta.LINEARREG(df['price'].values, timeperiod=14) resDF['LINEARREG_ANGLE'] = ta.LINEARREG_ANGLE(df['price'].values, timeperiod=14) resDF['LINEARREG_INTERCEPT'] = ta.LINEARREG_INTERCEPT(df['price'].values, timeperiod=14) resDF['LINEARREG_SLOPE'] = ta.LINEARREG_SLOPE(df['price'].values, timeperiod=14) resDF['MA'] = ta.MA(df['price'].values, timeperiod=30, matype=0) resDF['macd'], resDF['macdsignal'], resDF['macdhist'] = ta.MACD( df['price'].values, fastperiod=12, slowperiod=26, signalperiod=9) resDF['macd'], resDF['macdsignal'], resDF['macdhist'] = ta.MACDEXT( df['price'].values, fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0) resDF['macd'], resDF['macdsignal'], resDF['macdhist'] = ta.MACDFIX( df['price'].values, signalperiod=9) #resDF['mama'], resDF['fama'] = ta.MAMA (df['price'].values, fastlimit=0, slowlimit=0) resDF['MAX'] = ta.MAX(df['price'].values, timeperiod=30) resDF['MAXINDEX'] = ta.MAXINDEX(df['price'].values, timeperiod=30) resDF['MEDPRICE'] = ta.MEDPRICE(df['max_price'].values, df['min_price'].values) resDF['MFI'] = ta.MFI(df['price_today_open'].values, df['max_price'].values, df['min_price'].values, df['price'].values, timeperiod=14) resDF['MIDPOINT'] = ta.MIDPOINT(df['price'].values, timeperiod=14) resDF['MIDPRICE'] = ta.MIDPRICE(df['max_price'].values, df['min_price'].values, timeperiod=14) resDF['MIN'] = ta.MIN(df['price'].values, timeperiod=30) resDF['MININDEX'] = ta.MININDEX(df['price'].values, timeperiod=30) resDF['min'], resDF['max'] = ta.MINMAX(df['price'].values, timeperiod=30) resDF['minidx'], resDF['maxidx'] = ta.MINMAXINDEX(df['price'].values, timeperiod=30) resDF['MINUS_DI'] = ta.MINUS_DI(df['max_price'].values, df['min_price'].values, df['price'].values, timeperiod=14) resDF['MINUS_DM'] = ta.MINUS_DM(df['max_price'].values, df['min_price'].values, timeperiod=14) resDF['MOM'] = ta.MOM(df['max_price'].values, timeperiod=10) resDF['NATR'] = ta.NATR(df['max_price'].values, df['min_price'].values, df['price'].values, timeperiod=14) # 函数名:OBV 名称:On Balance Volume 能量潮 # 简介:Joe Granville提出,通过统计成交量变动的趋势推测股价趋势计算公式:以某日为基期,逐日累计每日上市股票总成交量,若隔日指数或股票上涨,则基期OBV加上本日成交量为本日OBV。隔日指数或股票下跌,则基期OBV减去本日成交量为本日OBV # 研判:1、以“N”字型为波动单位,一浪高于一浪称“上升潮”,下跌称“跌潮”;上升潮买进,跌潮卖出 # 2、须配合K线图走势 # 3、用多空比率净额法进行修正,但不知TA-Lib采用哪种方法 # 多空比率净额= [(收盘价-最低价)-(最高价-收盘价)] ÷( 最高价-最低价)×成交量 # 例子:real = OBV(close, volume) resDF['OBV'] = ta.OBV(df['price'].values, df['vol'].values) # resDF['PLUS_DI'] = ta.PLUS_DI # resDF['PLUS_DM'] = ta.PLUS_DM resDF['PPO'] = ta.PPO(df['price'].values, fastperiod=12, slowperiod=26, matype=0) resDF['ROC'] = ta.ROC(df['price'].values, timeperiod=10) resDF['ROCP'] = ta.ROCP(df['price'].values, timeperiod=10) resDF['ROCR'] = ta.ROCR(df['price'].values, timeperiod=10) resDF['ROCR100'] = ta.ROCR100(df['price'].values, timeperiod=10) resDF['RSI'] = ta.RSI(df['price'].values, timeperiod=14) resDF['SAR'] = ta.SAR(df['max_price'].values, df['min_price'].values, acceleration=0, maximum=0) resDF['SAREXT'] = ta.SAREXT(df['max_price'].values, df['min_price'].values, startvalue=0, offsetonreverse=0, accelerationinitlong=0, accelerationlong=0, accelerationmaxlong=0, accelerationinitshort=0, accelerationshort=0, accelerationmaxshort=0) resDF['SMA'] = ta.SMA(df['price'].values, timeperiod=30) resDF['STDDEV'] = ta.STDDEV(df['price'].values, timeperiod=5, nbdev=1) # resDF['STOCH'] = ta.STOCH # resDF['STOCHF'] = ta.STOCHF resDF['fastk'], resDF['fastd'] = ta.STOCHRSI(df['price'].values, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0) resDF['SUM'] = ta.SUM(df['price'].values, timeperiod=30) resDF['T3'] = ta.T3(df['price'].values, timeperiod=5, vfactor=0) resDF['TEMA'] = ta.TEMA(df['price'].values, timeperiod=30) resDF['TRANGE'] = ta.TRANGE(df['max_price'].values, df['min_price'].values, df['price'].values) resDF['TRIMA'] = ta.TRIMA(df['price'].values, timeperiod=30) resDF['TRIX'] = ta.TRIX(df['price'].values, timeperiod=30) resDF['TSF'] = ta.TSF(df['price'].values, timeperiod=14) resDF['TYPPRICE'] = ta.TYPPRICE(df['max_price'].values, df['min_price'].values, df['price'].values) # resDF['ULTOSC'] = ta.ULTOSC resDF['VAR'] = ta.VAR(df['price'].values, timeperiod=5, nbdev=1) resDF['WCLPRICE'] = ta.WCLPRICE(df['max_price'].values, df['min_price'].values, df['price'].values) # resDF['WILLR'] = ta.WILLR resDF['WMA'] = ta.WMA(df['price'].values, timeperiod=30) return resDF
######################################### ##### 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)
def MAXINDEX(self, name, **parameters): data = self.__data[name] return talib.MAXINDEX(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)