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import Ind
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import pandas as pd
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from sklearn.preprocessing import StandardScaler
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from sklearn.linear_model import LogisticRegression as LR
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# 获取投资组合所有股票交易数据
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stkdata = pd.read_excel('stkdata.xlsx')
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# 确保数据中没有空值
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stkdata.dropna(inplace=True)
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# 获取投资组合所有股票代码列表
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codelist = stkdata['ts_code'].unique().tolist()
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r_total = 0 # 预定义投资组合收益率
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# 对每一只股票交易数据计算技术分析指标（自变量）和涨跌趋势指标（因变量），并
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# 划分训练和测试样本，利用逻辑回归模型预测及计算收益率
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for code in codelist:
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data = stkdata[stkdata['ts_code'] == code]
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# 检查数据长度是否足够
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if len(data) < 50: # 例如，至少需要50天的数据
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print(f"Skipping {code} due to insufficient data length")
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continue
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try:
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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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if kdj is None:
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print(f"Skipping {code} due to insufficient data for KDJ calculation")
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continue
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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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except KeyError as e:
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print(f"Error calculating indicators for {code}: {e}")
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continue
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# 交易日期、技术指标、涨跌趋势指标合并为一个数据Data
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tdate = {'交易日期': data['trade_date'].values}
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tdate = pd.DataFrame(tdate)
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Indicator = {
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'MA5': MA[0], 'MA10': MA[1], 'MA20': MA[2], 'MACD': macd,
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'K': kdj[0], 'D': kdj[1], 'J': kdj[2], 'RSI6': rsi6, 'RSI12': rsi12,
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'RSI24': rsi24, 'BIAS5': bias5, 'BIAS10': bias10, 'BIAS20': bias20, 'OBV': obv
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}
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Indicator = pd.DataFrame(Indicator)
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tempdata = tdate.join(Indicator)
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Y = {'涨跌趋势': y}
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Y = pd.DataFrame(Y)
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Data = tempdata.join(Y)
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Data = Data.dropna() # 去掉空值
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Data = Data[Data.iloc[:, 6].values != 0] # 去掉第6列为0的数据
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# 训练和预测数据划分
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x1 = Data['交易日期'].values >= 20170101
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x2 = Data['交易日期'].values <= 20171031
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index = x1 & x2
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x_train = Data.iloc[index, 1:15]
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y_train = Data.iloc[index, [15]]
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x_test = Data.iloc[~index, 1:15]
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y_test = Data.iloc[~index, [15]]
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# 检查训练数据是否为空
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if x_train.empty or y_train.empty:
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print(f"Skipping {code} due to empty training data")
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continue
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# 数据标准化
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scaler = StandardScaler()
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scaler.fit(x_train)
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x_train = scaler.transform(x_train)
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x_test = scaler.transform(x_test)
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# 逻辑回归模型
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clf = LR()
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clf.fit(x_train, y_train.values.ravel()) # 使用 ravel() 将 y_train 转换为 1D 数组
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result = clf.predict(x_test) # 预测结果
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sc = clf.score(x_train, y_train) # 模型准确率
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result = pd.DataFrame(result) # 预测结果转换为数据框
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ff = Data.iloc[~index, 0] # 提取预测样本的交易日期
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# 将预测结果与实际结果整合在一起，进行比较
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pm1 = {'交易日期': ff.values, '预测结果': result.iloc[:, 0].values, '实际结果': y_test.iloc[:, 0].values}
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result1 = pd.DataFrame(pm1)
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z = result1['预测结果'].values - result1['实际结果'].values
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R = len(z[z == 0]) / len(z) # 预测准确率
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if sc > 0.7:
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r_list = []
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for t in range(len(result1) - 1):
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if result1['预测结果'].values[t] == 1:
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p2 = data.loc[data['trade_date'].values == result1['交易日期'].values[t + 1], 'close'].values
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p1 = data.loc[data['trade_date'].values == result1['交易日期'].values[t + 1], 'open'].values
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r = (p2 - p1) / p1
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r_list.append(r)
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r_stk = sum(r_list)
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r_total = r_total + r_stk
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print(f"{code} : {r_stk}")
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print(f"投资组合收益率： {r_total}")
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# 沪深300指数收益率计算
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hs300 = pd.read_excel('hs300.xlsx')
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x1 = hs300['trade_date'].values >= 20171101
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x2 = hs300['trade_date'].values <= 20171231
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index = x1 & x2
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p = hs300.iloc[index, 2].values
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r_hs300 = (p[-1] - p[0]) / p[0]
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print(f"沪深300同期收益率： {r_hs300}")
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