我们从Python开源项目中，提取了以下38个代码示例，用于说明如何使用talib.RSI。
def onInit(self): """????????????????""" self.writeCtaLog(u'%s?????' %self.name) # ???RSI???? self.rsiBuy = 50 + self.rsiEntry self.rsiSell = 50 - self.rsiEntry # ???????????????????????? initData = self.loadBar(self.initDays) for bar in initData: self.onBar(bar) self.putEvent() #----------------------------------------------------------------------
def isBuy(context, analysis): # Bullish SMA Crossover if (getLast(analysis, 'sma_test') == 1): # Bullish MACD if (getLast(analysis, 'macd_test') == 1): return True # # Bullish Stochastics # if(getLast(analysis, 'stoch_over_sold') == 1): # return True # # Bullish RSI # if(getLast(analysis, 'rsi_over_sold') == 1): # return True return False
def isSell(context, analysis): # Bearish SMA Crossover if (getLast(analysis, 'sma_test') == 0): # Bearish MACD if (getLast(analysis, 'macd_test') == 0): return True # # Bearish Stochastics # if(getLast(analysis, 'stoch_over_bought') == 0): # return True # # Bearish RSI # if(getLast(analysis, 'rsi_over_bought') == 0): # return True return False
def getStrategy_RSI(start_trading_day,end_trading_day,_time,_close): point = [] iday = _time.index(start_trading_day) eday = _time.index(end_trading_day) rsi = ta.RSI(np.array(_close,dtype='f8'),timeperiod=14) rsi = rsi[iday:eday] point.append(0) for i in range(1,len(rsi)): if (rsi[i] <= 30) and (rsi[i - 1] > 30): point.append(1) elif (rsi[i] >= 50) and (rsi[i - 1] < 50): point.append(-1) else: point.append(0) return point
def KDJ_COM(high, low, close, fastk_period=9, slowk_period=3, slowd_period=3, fixpre=True) : len1 = len(high) len2 = len(low) len3 = len(close) if len1 != len2 or len1 != len3: print (("KDJ_COM input invalid for len:%s %s %s " %(str(len1),str(len2),str(len3)))) return np.array([np.nan]),np.array([np.nan]),np.array([np.nan]) hValue = SecurityDataSrcBase.HHV_COM(high, fastk_period, fixpre) lValue = SecurityDataSrcBase.LLV_COM(low, fastk_period, fixpre) rsValue = (close - lValue) / (hValue - lValue) * 100 kValue = SecurityDataSrcBase.SMA_COM(rsValue, slowk_period) dValue = SecurityDataSrcBase.SMA_COM(kValue, slowd_period) jValue = 3 * kValue - 2 * dValue return kValue, dValue, jValue # RSI COMMON
def test_JiShuZhiBiao(): """??????""" pl = publish.Publish() code = '002440' code = '999999' #?????????rsi df = FenshiEx(code, '2014-10-8', '2014-10-8').df #df = FENSHI_MA(df) df = FENSHI_BIAS(df) df['rsi'] = RSI(df['p']) df1 = df.drop(['v','b','bias','rsi'], axis=1) ui.drawDf(pl, df1) df2 = df.drop(['v','b','p','avg', 'rsi'], axis=1) ui.drawDf(pl, df2) df2 = df.drop(['v','b','p','avg', 'bias'], axis=1) ui.drawDf(pl, df2)
def __init__(self, selector): self.selector = selector self.supported = {"ROCP", "OROCP", "HROCP", "LROCP", "MACD", "RSI", "VROCP", "BOLL", "MA", "VMA", "PRICE_VOLUME"} self.feature = []
def handle_bar(context, bar_dict): # ????????????? # bar_dict[order_book_id] ?????????bar?? # context.portfolio ??????????????? # ??order_shares(id_or_ins, amount)?????? # TODO: ?????????? # ????????????loop?????????RSI?? for stock in context.stocks: # ?????? prices = history_bars(stock, context.TIME_PERIOD+1, '1d', 'close') # ?Talib??RSI? rsi_data = talib.RSI(prices, timeperiod=context.TIME_PERIOD)[-1] cur_position = context.portfolio.positions[stock].quantity # ??????30%??????? target_available_cash = context.portfolio.cash * context.ORDER_PERCENT # ?RSI??????????????? if rsi_data > context.HIGH_RSI and cur_position > 0: order_target_value(stock, 0) # ?RSI?????????????cash????????? if rsi_data < context.LOW_RSI: logger.info("target available cash caled: " + str(target_available_cash)) # ??????????? - 100shares?????ricequant ?order management system reject? order_value(stock, target_available_cash)
def handle_data(context, data): print('handling bar: {}'.format(data.current_dt)) price = data.current(context.asset, 'close') print('got price {price}'.format(price=price)) prices = data.history( context.asset, fields='price', bar_count=20, frequency='30T' ) last_traded = prices.index[-1] print('last candle date: {}'.format(last_traded)) rsi = talib.RSI(prices.values, timeperiod=14)[-1] print('got rsi: {}'.format(rsi)) # If base_price is not set, we use the current value. This is the # price at the first bar which we reference to calculate price_change. if context.base_price is None: context.base_price = price price_change = (price - context.base_price) / context.base_price cash = context.portfolio.cash # Now that we've collected all current data for this frame, we use # the record() method to save it. This data will be available as # a parameter of the analyze() function for further analysis. record( price=price, price_change=price_change, cash=cash )
def RSI(df, n, field='close'): return pd.Series(talib.RSI(df[field].values, n), index = df.index, name='RSI%s' % str(n))
def rsi(df, n, field = 'close'): RSI_key = 'RSI%s' % str(n) df[RSI_key][-1] = talib.RSI(df[field][(-n-1):], n)[-1]
def RSI_F(df, n, field='close'): UpMove = df[field] - df[field].shift(1) DoMove = df[field].shift(1) - df[field] UpD = pd.Series(UpMove) DoD = pd.Series(DoMove) UpD[(UpMove <= 0)] = 0 DoD[(DoMove <= 0)] = 0 PosDI = pd.Series(pd.ewma(UpD, com = n-1), name = "RSI"+str(n)+'_UP') NegDI = pd.Series(pd.ewma(DoD, com = n-1), name = "RSI"+str(n)+'_DN') RSI = pd.Series(PosDI / (PosDI + NegDI) * 100, name = 'RSI' + str(n)) return pd.concat([RSI, PosDI, NegDI], join='outer', axis=1)
def rsi_f(df, n, field = 'close'): RSI_key = 'RSI%s' % str(n) dx = df[field][-1] - df[field][-2] alpha = 1.0/n if dx > 0: upx = dx dnx = 0 else: upx = 0 dnx = -dx udi = df[RSI_key + '_UP'][-1] = df[RSI_key + '_UP'][-2] * (1 - alpha) + upx * alpha ddi = df[RSI_key + '_DN'][-1] = df[RSI_key + '_DN'][-2] * (1 - alpha) + dnx * alpha df[RSI_key][-1] = udi/(udi + ddi) * 100.0 #True Strength Index
def add_RSI(self, timeperiod=14, type='line', color='secondary', **kwargs): """Relative Strength Index.""" if not self.has_close: raise Exception() utils.kwargs_check(kwargs, VALID_TA_KWARGS) if 'kind' in kwargs: type = kwargs['kind'] name = 'RSI({})'.format(str(timeperiod)) self.sec[name] = dict(type=type, color=color) self.ind[name] = talib.RSI(self.df[self.cl].values, timeperiod)
def RSI_CN(close, timeperiod=6): return tl.RSI(close, timeperiod) # MA
def rsi(self, n, array=False): """RSI??""" result = talib.RSI(self.close, n) if array: return result return result[-1] # ----------------------------------------------------------------------
def RSI(self): """??????? 30????? 70????""" closes = self.getCloses() return talib.RSI(closes)
def RSI(closes, timeperiod=12): """??????? 30????? 70???? return: np.darray""" closes = np.array(closes) closes = closes[np.isnan(closes) == False] return talib.RSI(closes, timeperiod)
def unittest_ma(): closes = Guider('600100').getCloses() print(MA(closes)) fours = FOUR(closes) print( len(closes), len(fours)) print(fours) rsi = RSI(closes) #rsi = rsi/100 - 0.5 rsi -= 50 fours *= 100 pl.figure #pl.plot(closes) pl.plot(fours,'r') pl.plot(rsi) pl.show()
def test_fenshi_rsi(): """???????rsi??""" code = '300059' fenshi = CreateFenshiPd(code, '2015-8-6').resample('1min').mean().dropna() print( fenshi) closes = fenshi['p'] print(closes) rsi = RSI(closes, 6) print(rsi) ui.DrawTs(pl, rsi)
def get_rsi_info(df): """ calculate rsi quotation. :param df: :return: rsi """ close = get_close_data(df) rsi = ta.RSI(close, timeperiod=12) return pd.DataFrame({u'rsi': rsi[-view_days:]})
def Get_TA(df_Code,Dist): operate_array1=[] operate_array2=[] operate_array3=[] count = 0 for code in df_Code.index: # index,0 - 6 date??? open???? high???? close???? low???? volume???? price_change????? p_change???? # 7-12 ma5?5??? ma10?10??? ma20:20??? v_ma5:5???v_ma10:10??? v_ma20:20??? df = ts.get_hist_data(code,start='2014-11-20') dflen = df.shape[0] count = count + 1 if dflen>35: try: (df,operate1) = Get_MACD(df) (df,operate2) = Get_KDJ(df) (df,operate3) = Get_RSI(df) except Exception, e: Write_Blog(e,Dist) pass operate_array1.append(operate1) #round(df.iat[(dflen-1),16],2) operate_array2.append(operate2) operate_array3.append(operate3) if count == 0: Write_Blog(str(count),Dist) df_Code['MACD']=pd.Series(operate_array1,index=df_Code.index) df_Code['KDJ']=pd.Series(operate_array2,index=df_Code.index) df_Code['RSI']=pd.Series(operate_array3,index=df_Code.index) return df_Code #??MACD??????
def __recountRsi(self): """??K??RSI""" if self.inputRsiLen <= 0: return # 1?lineBar????????? if len(self.lineBar) < self.inputRsiLen+2: self.debugCtaLog(u'?????,??Bar?????{0}???RSI???{1}'. format(len(self.lineBar), self.inputRsiLen+2)) return # 3?inputRsiLen(???????????? listClose=[x.close for x in self.lineBar[-self.inputRsiLen - 2:]] barRsi = ta.RSI(numpy.array(listClose, dtype=float), self.inputRsiLen)[-1] barRsi = round(float(barRsi), 3) l = len(self.lineRsi) if l > self.inputRsiLen*8: del self.lineRsi[0] self.lineRsi.append(barRsi) if l > 3: # ? if self.lineRsi[-1] < self.lineRsi[-2] and self.lineRsi[-3] < self.lineRsi[-2]: t={} t["Type"] = u'T' t["RSI"] = self.lineRsi[-2] t["Close"] = self.lineBar[-2].close if len(self.lineRsiTop) > self.inputRsiLen: del self.lineRsiTop[0] self.lineRsiTop.append( t ) self.lastRsiTopButtom = self.lineRsiTop[-1] # ? elif self.lineRsi[-1] > self.lineRsi[-2] and self.lineRsi[-3] > self.lineRsi[-2]: b={} b["Type"] = u'B' b["RSI"] = self.lineRsi[-2] b["Close"] = self.lineBar[-2].close if len(self.lineRsiButtom) > self.inputRsiLen: del self.lineRsiButtom[0] self.lineRsiButtom.append(b) self.lastRsiTopButtom = self.lineRsiButtom[-1]
def _handle_data_rsi_only(context, data): price = data.current(context.asset, 'close') log.info('got price {price}'.format(price=price)) if price is np.nan: log.warn('no pricing data') return if context.base_price is None: context.base_price = price try: prices = data.history( context.asset, fields='price', bar_count=20, frequency='30T' ) except Exception as e: log.warn('historical data not available: '.format(e)) return rsi = talib.RSI(prices.values, timeperiod=16)[-1] log.info('got rsi {}'.format(rsi)) signal = None if rsi < context.RSI_OVERSOLD: signal = 'long' # Making sure that the price is still current price = data.current(context.asset, 'close') cash = context.portfolio.cash log.info( 'base currency available: {cash}, cap: {cap}'.format( cash=cash, cap=context.MAX_HOLDINGS ) ) volume = data.current(context.asset, 'volume') price_change = (price - context.base_price) / context.base_price record( price=price, price_change=price_change, rsi=rsi, volume=volume, cash=cash, starting_cash=context.portfolio.starting_cash, leverage=context.account.leverage, ) _handle_buy_sell_decision(context, data, signal, price)
def calculate_features(df): """ Method which calculates and generates features """ close = df['close'].values high = df['high'].values low = df['low'].values volume = df['volume'].values last_row = df.tail(1).copy() # ************** Calc EMAs ema_periods = [2, 4, 8, 12, 16, 20] for ema_period in ema_periods: ema = talib.EMA(close[-ema_period:], timeperiod=ema_period)[-1] last_row['ema' + str(ema_period)] = ema # ************** Calc RSIs rsi_periods = [5] for rsi_period in rsi_periods: rsi = talib.RSI(close[-rsi_period:], timeperiod=rsi_period-1)[-1] last_row['rsi' + str(rsi_period)] = rsi last_row['rsi_above_50' + str(rsi_period)] = int(rsi > 50.0) # ************** Calc CCIs cci_periods = [5] for cci_period in cci_periods: cci = talib.CCI(high[-cci_period:], low[-cci_period:], close[-cci_period:], timeperiod=cci_period)[-1] last_row['cci' + str(cci_period)] = cci # ************** Calc MACD 1 macd_periods = [34] for macd_period in macd_periods: macd, macd_signal, _ = talib.MACD(close[-macd_period:], fastperiod=12, slowperiod=26, signalperiod=9) macd = macd[-1] signal_line = macd_signal[-1] last_row['macd_above_signal' + str(macd_period)] = int(macd > signal_line) last_row['macd_above_zero' + str(macd_period)] = int(macd > 0.0) # ************** Calc OBVs obv_periods = [2, 4, 8, 12, 16, 20] for obv_period in obv_periods: obv = talib.OBV(close[-obv_period:], volume[-obv_period:])[-1] last_row['obv' + str(obv_period)] = obv return last_row
def oscillator2(data): float_close = Data.toFloatArray(df['Close']) float_high = Data.toFloatArray(df['High']) float_low = Data.toFloatArray(df['Low']) float_open = Data.toFloatArray(df['Open']) adx_values = tl.ADX(np.array(float_high),np.array(float_low),np.array(float_close), timeperiod = 14) dmi = tl.DX(np.array(float_high),np.array(float_low),np.array(float_close), timeperiod = 14) mdmi = tl.MINUS_DI(np.array(float_high),np.array(float_low),np.array(float_close), timeperiod = 14) rsi = tl.RSI(np.array(float_close),timeperiod = 4 ) signals = [] flag = 0 for i in xrange(40 , len(adx_values) - 2): if flag ==0: if adx_values[i]>20 and dmi[i]>mdmi[i] and df.loc[i+1, 'Open']> (df.loc[i, 'Close']+1.8) and rsi[i]<50: signal = ['HSI', df.loc[i+1, 'Date'], 'Long', df.loc[i+1, 'Close']] flag =1 signals.append(signal) if adx_values[i]>20 and dmi[i]<mdmi[i] and df.loc[i+1, 'Open']< (df.loc[i, 'Close']-1.8) and rsi[i]<50: signal = ['HSI', df.loc[i+1, 'Date'], 'Short', df.loc[i+1, 'Close']] flag =2 signals.append(signal) elif flag ==1: if df.loc[i, 'Close']>= signal[3]*1.01 or df.loc[i, 'Close']<= signal[3]*0.90 or (df.loc[i, 'Date']-signal[1])>timedelta(days=5): signal = ['HSI', df.loc[i, 'Date'], 'Short', df.loc[i+1, 'Open']] flag = 0 signals.append(signal) elif flag ==2: if df.loc[i, 'Close']<= signal[3]*0.99 or df.loc[i, 'Close']>= signal[3]*1.10 or (df.loc[i, 'Date']-signal[1])>timedelta(days=5): signal = ['HSI', df.loc[i+1, 'Date'], 'Long', df.loc[i+1, 'Close']] flag = 0 signals.append(signal) sig = pd.DataFrame(signals, columns=['Code', 'Time', 'Action', 'Price']) print sig['Time'][10]-sig['Time'][0] profits = [] print sig for k in range(0,len(signals)/2): if sig['Action'][k*2] == "Long": profit = sig['Price'][k*2+1] - sig['Price'][k*2] else: profit = sig['Price'][k*2]- sig['Price'][k*2+1] profits.append(profit) print np.sum(profits) print(profits) ###### PLOT ####### longSignals = sig[sig['Action'] == 'Long'] shortSignals = sig[sig['Action'] == 'Short'] plt.plot(df['Date'], df['Close'], longSignals['Time'], longSignals['Price'], 'r^', shortSignals['Time'], shortSignals['Price'], 'gv', markersize=10) red_patch = mpatches.Patch(color='red', label='Long') green_patch = mpatches.Patch(color='green', label='Short') plt.legend(handles=[red_patch, green_patch]) plt.grid() plt.show() ###### PLOT #######
def Get_KDJ(df): #??9,3,3 slowk, slowd = ta.STOCH(np.array(df['high']), np.array(df['low']), np.array(df['close']), fastk_period=9, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0) slowkMA5 = ta.MA(slowk, timeperiod=5, matype=0) slowkMA10 = ta.MA(slowk, timeperiod=10, matype=0) slowkMA20 = ta.MA(slowk, timeperiod=20, matype=0) slowdMA5 = ta.MA(slowd, timeperiod=5, matype=0) slowdMA10 = ta.MA(slowd, timeperiod=10, matype=0) slowdMA20 = ta.MA(slowd, timeperiod=20, matype=0) #16-17 K,D df['slowk']=pd.Series(slowk,index=df.index) #K df['slowd']=pd.Series(slowd,index=df.index)#D dflen = df.shape[0] MAlen = len(slowkMA5) operate = 0 #1.K???????——???90?????????10??????D??80???????????D??20??????????? if df.iat[(dflen-1),16]>=90: operate = operate + 3 elif df.iat[(dflen-1),16]<=10: operate = operate - 3 if df.iat[(dflen-1),17]>=80: operate = operate + 3 elif df.iat[(dflen-1),17]<=20: operate = operate - 3 #2.??????K???D??K?????D?????????#??? if df.iat[(dflen-1),16]> df.iat[(dflen-1),17] and df.iat[(dflen-2),16]<=df.iat[(dflen-2),17]: operate = operate + 10 #??????K??D?K?????D?????????#??? elif df.iat[(dflen-1),16]< df.iat[(dflen-1),17] and df.iat[(dflen-2),16]>=df.iat[(dflen-2),17]: operate = operate - 10 #3.??????????????????????? if df.iat[(dflen-1),7]>=df.iat[(dflen-1),8] and df.iat[(dflen-1),8]>=df.iat[(dflen-1),9]:#K??? if (slowkMA5[MAlen-1]<=slowkMA10[MAlen-1] and slowkMA10[MAlen-1]<=slowkMA20[MAlen-1]) or \ (slowdMA5[MAlen-1]<=slowdMA10[MAlen-1] and slowdMA10[MAlen-1]<=slowdMA20[MAlen-1]): #K,D?? operate = operate - 1 elif df.iat[(dflen-1),7]<=df.iat[(dflen-1),8] and df.iat[(dflen-1),8]<=df.iat[(dflen-1),9]:#K??? if (slowkMA5[MAlen-1]>=slowkMA10[MAlen-1] and slowkMA10[MAlen-1]>=slowkMA20[MAlen-1]) or \ (slowdMA5[MAlen-1]>=slowdMA10[MAlen-1] and slowdMA10[MAlen-1]>=slowdMA20[MAlen-1]): #K,D?? operate = operate + 1 return (df,operate) #??RSI??????
def Get_RSI(df): #??14,5 slowreal = ta.RSI(np.array(df['close']), timeperiod=14) fastreal = ta.RSI(np.array(df['close']), timeperiod=5) slowrealMA5 = ta.MA(slowreal, timeperiod=5, matype=0) slowrealMA10 = ta.MA(slowreal, timeperiod=10, matype=0) slowrealMA20 = ta.MA(slowreal, timeperiod=20, matype=0) fastrealMA5 = ta.MA(fastreal, timeperiod=5, matype=0) fastrealMA10 = ta.MA(fastreal, timeperiod=10, matype=0) fastrealMA20 = ta.MA(fastreal, timeperiod=20, matype=0) #18-19 ??real???real df['slowreal']=pd.Series(slowreal,index=df.index) #??real 18 df['fastreal']=pd.Series(fastreal,index=df.index)#??real 19 dflen = df.shape[0] MAlen = len(slowrealMA5) operate = 0 #RSI>80?????RSI<20???? if df.iat[(dflen-1),18]>80 or df.iat[(dflen-1),19]>80: operate = operate - 2 elif df.iat[(dflen-1),18]<20 or df.iat[(dflen-1),19]<20: operate = operate + 2 #RSI??50???????????50???????? if (df.iat[(dflen-2),18]<=50 and df.iat[(dflen-1),18]>50) or (df.iat[(dflen-2),19]<=50 and df.iat[(dflen-1),19]>50): operate = operate + 4 elif (df.iat[(dflen-2),18]>=50 and df.iat[(dflen-1),18]<50) or (df.iat[(dflen-2),19]>=50 and df.iat[(dflen-1),19]<50): operate = operate - 4 #RSI??????????RSI????????? if df.iat[(dflen-1),7]>=df.iat[(dflen-1),8] and df.iat[(dflen-1),8]>=df.iat[(dflen-1),9]:#K??? if (slowrealMA5[MAlen-1]<=slowrealMA10[MAlen-1] and slowrealMA10[MAlen-1]<=slowrealMA20[MAlen-1]) or \ (fastrealMA5[MAlen-1]<=fastrealMA10[MAlen-1] and fastrealMA10[MAlen-1]<=fastrealMA20[MAlen-1]): #RSI?? operate = operate - 1 elif df.iat[(dflen-1),7]<=df.iat[(dflen-1),8] and df.iat[(dflen-1),8]<=df.iat[(dflen-1),9]:#K??? if (slowrealMA5[MAlen-1]>=slowrealMA10[MAlen-1] and slowrealMA10[MAlen-1]>=slowrealMA20[MAlen-1]) or \ (fastrealMA5[MAlen-1]>=fastrealMA10[MAlen-1] and fastrealMA10[MAlen-1]>=fastrealMA20[MAlen-1]): #RSI?? operate = operate + 1 #????????????????????????????????????????????????????????????????? if df.iat[(dflen-1),19]> df.iat[(dflen-1),18] and df.iat[(dflen-2),19]<=df.iat[(dflen-2),18]: operate = operate + 10 elif df.iat[(dflen-1),19]< df.iat[(dflen-1),18] and df.iat[(dflen-2),19]>=df.iat[(dflen-2),18]: operate = operate - 10 return (df,operate)
