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75 lines
1.7 KiB
75 lines
1.7 KiB
# -*- coding: utf-8 -*-
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import Ind
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import pandas as pd
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# 读取数据
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data = pd.read_excel('dta.xlsx')
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# 检查并处理原始数据中的 NaN 值
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if data.isnull().values.any():
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print("原始数据中存在 NaN 值，进行填充处理")
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data = data.fillna(method='ffill').fillna(method='bfill')
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# 计算技术指标
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MA = Ind.MA(data, 5, 10, 20)
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macd = Ind.MACD(data)
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kdj = Ind.KDJ(data, 9)
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rsi6 = Ind.RSI(data, 6)
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rsi12 = Ind.RSI(data, 12)
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rsi24 = Ind.RSI(data, 24)
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bias5 = Ind.BIAS(data, 5)
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bias10 = Ind.BIAS(data, 10)
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bias20 = Ind.BIAS(data, 20)
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obv = Ind.OBV(data)
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y = Ind.cla(data)
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# 检查并处理技术指标中的 NaN 值
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MA = [ma.ffill().bfill() for ma in MA]
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macd = macd.ffill().bfill()
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kdj = [pd.Series(k).ffill().bfill() for k in kdj]
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rsi6 = rsi6.ffill().bfill()
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rsi12 = rsi12.ffill().bfill()
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rsi24 = rsi24.ffill().bfill()
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bias5 = bias5.ffill().bfill()
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bias10 = bias10.ffill().bfill()
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bias20 = bias20.ffill().bfill()
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obv = obv.ffill().bfill()
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y = y.ffill().bfill()
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# 将计算出的技术指标与交易日期以及股价的涨跌趋势利用字典整合在一起
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pm = {'交易日期': data['trade_date'].values}
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PM = pd.DataFrame(pm)
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DF = {
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'MA5': MA[0],
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'MA10': MA[1],
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'MA20': MA[2],
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'MACD': macd,
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'K': kdj[0],
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'D': kdj[1],
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'J': kdj[2],
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'RSI6': rsi6,
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'RSI12': rsi12,
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'RSI24': rsi24,
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'BIAS5': bias5,
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'BIAS10': bias10,
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'BIAS20': bias20,
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'OBV': obv
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}
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DF = pd.DataFrame(DF)
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s1 = PM.join(DF)
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y1 = {'涨跌趋势': y}
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ZZ = pd.DataFrame(y1)
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s2 = s1.join(ZZ)
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# 去掉空值
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ss = s2.dropna()
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# 将ss中第6列不为0的值提取出来,存放到Data中
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Data = ss[ss.iloc[:, 6].values != 0]
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# 打印结果
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print(Data)
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