股票常用指标
原文
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指标
先把要用指标放上来
| 缩写 | 描述 |
|---|---|
| K | KDJ中的K值 |
| D | KDJ中的D值 |
| J | KDJ中的J值 |
| MACD | 异同移动平均线 |
| MOM | 动量线 |
| BIAS | 乖离率 |
| CMO | 钱德动量摆动指标 |
| TRIX | 三重指数平滑平均线 |
| OBV | 能量潮 |
| ROC | 变动率指标 |
| AMA | 移动平均平行线差指标 |
| VR | 成交量变异率 |
| PSY | 心理线指标 |
| Force Index | 强力指数指标 |
| DPO | 区间震荡线 |
| VHF | 十字过滤线指标 |
| RVI | 相对活力指数 |
实现
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54import pandas as pd import numpy as np import talib import stockstats import pandas_talib import tushare as ts df=ts.get_hist_data('601857') df1=df.copy() ''' 这里虽然没有定义df这个变量，但这很明显就是dateframe格式的某只股票基础数据 包括开盘价、收盘价、最高价、最低价和成交量 建议用tushare来获取数据(当然仅限日数据) ''' stockStat = stockstats.StockDataFrame.retype(df) close = df.close highPrice = df.high lowPrice = df.low volume = df.volume df.rename(columns={'close': 'Close', 'volume': 'Volume'}, inplace=True) sig_k , sig_d = talib.STOCH(np.array(highPrice), np.array(lowPrice), np.array(close), fastk_period=9,slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0) sig_j = sig_k * 3 - sig_d * 2 sig = pd.DataFrame() sig['K']=list(sig_k) sig['D']=list(sig_d) sig['J']=list(sig_j) #sig = pd.DataFrame([list(sig_k), list(sig_d), list(sig_j)], columns=['K', 'D', 'J']) sig['MACD'], MACDsignal, MACDhist = talib.MACD(np.array(close), fastperiod=6, slowperiod=12, signalperiod=9) sig['MOM'] = talib.MOM(np.array(close), timeperiod=5) sig['CMO'] = talib.CMO(np.array(close), timeperiod=10) sig['TRIX'] = talib.TRIX(np.array(close), timeperiod=14) sig['OBV'] = talib.OBV(np.array(close), volume) sig['ROC'] = talib.ROC(np.array(close), timeperiod=10) sig['VR'] = list(stockStat['vr']) sig['Force_Index'] = list(pandas_talib.FORCE(df, 12)['Force_12'])
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57def BIAS(close, timeperiod=20): if isinstance(close,np.ndarray): pass else: close = np.array(close) MA = talib.MA(close,timeperiod=timeperiod) return (close-MA)/MA bias=BIAS(close, timeperiod=20) def AMA(stockStat): return talib.MA(stockStat['dma'], timeperiod=10) ama=AMA(stockStat) def PSY(priceData, period): difference = priceData[1:] - priceData[:-1] difference = np.append(0, difference) difference_dir = np.where(difference > 0, 1, 0) psy = np.zeros((len(priceData),)) psy[:period] *= np.nan for i in range(period, len(priceData)): psy[i] = (difference_dir[i-period+1:i+1].sum()) / period return psy*100 def DPO(close): p = talib.MA(close, timeperiod=11) p.shift() return close-p def VHF(close): LCP = talib.MIN(close, timeperiod=28) HCP = talib.MAX(close, timeperiod=28) NUM = HCP - LCP pre = close.copy() pre = pre.shift() DEN = abs(close-close.shift()) DEN = talib.MA(DEN, timeperiod=28)*28 return NUM.div(DEN) vhf=VHF(close) def RVI(df): close = df.close open = df.open high = df.high low = df.low X = close-open+2*(close.shift()-open.shift())+2*(close.shift(periods=2)-open.shift(periods=2))*(close.shift(periods=3)- open.shift(periods=3))/6 Y = high-low+2*(high.shift()-low.shift())+2*(high.shift(periods=2)-low.shift(periods=2))*(high.shift(periods=3)- low.shift(periods=3))/6 Z = talib.MA(X, timeperiod=10)*10 D = talib.MA(Y, timeperiod=10)*10 return Z/D rvi=RVI(df1)
posted on 2019-02-25 19:13 luoganttcc 阅读(...) 评论(...) 编辑 收藏
最后
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