From 51befd140c69fc5ccda918e4081e6400d70eb700 Mon Sep 17 00:00:00 2001
From: pavrxskmy <3034025383@qq.com>
Date: Sat, 9 Nov 2024 22:10:00 +0800
Subject: [PATCH] ADD file via upload

---
 7.5.2.py | 117 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 117 insertions(+)
 create mode 100644 7.5.2.py

diff --git a/7.5.2.py b/7.5.2.py
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+import Ind
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from sklearn.linear_model import LogisticRegression as LR
+
+# 获取投资组合所有股票交易数据
+stkdata = pd.read_excel('stkdata.xlsx')
+
+# 确保数据中没有空值
+stkdata.dropna(inplace=True)
+
+# 获取投资组合所有股票代码列表
+codelist = stkdata['ts_code'].unique().tolist()
+r_total = 0  # 预定义投资组合收益率
+
+# 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
+# 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
+for code in codelist:
+    data = stkdata[stkdata['ts_code'] == code]
+
+    # 检查数据长度是否足够
+    if len(data) < 50:  # 例如，至少需要50天的数据
+        print(f"Skipping {code} due to insufficient data length")
+        continue
+
+    try:
+        MA = Ind.MA(data, 5, 10, 20)
+        macd = Ind.MACD(data)
+        kdj = Ind.KDJ(data, 9)
+        if kdj is None:
+            print(f"Skipping {code} due to insufficient data for KDJ calculation")
+            continue
+        rsi6 = Ind.RSI(data, 6)
+        rsi12 = Ind.RSI(data, 12)
+        rsi24 = Ind.RSI(data, 24)
+        bias5 = Ind.BIAS(data, 5)
+        bias10 = Ind.BIAS(data, 10)
+        bias20 = Ind.BIAS(data, 20)
+        obv = Ind.OBV(data)
+        y = Ind.cla(data)
+    except KeyError as e:
+        print(f"Error calculating indicators for {code}: {e}")
+        continue
+
+    # 交易日期、技术指标、涨跌趋势指标合并为一个数据Data
+    tdate = {'交易日期': data['trade_date'].values}
+    tdate = pd.DataFrame(tdate)
+    Indicator = {
+        'MA5': MA[0], 'MA10': MA[1], 'MA20': MA[2], 'MACD': macd,
+        'K': kdj[0], 'D': kdj[1], 'J': kdj[2], 'RSI6': rsi6, 'RSI12': rsi12,
+        'RSI24': rsi24, 'BIAS5': bias5, 'BIAS10': bias10, 'BIAS20': bias20, 'OBV': obv
+    }
+    Indicator = pd.DataFrame(Indicator)
+    tempdata = tdate.join(Indicator)
+    Y = {'涨跌趋势': y}
+    Y = pd.DataFrame(Y)
+    Data = tempdata.join(Y)
+    Data = Data.dropna()  # 去掉空值
+    Data = Data[Data.iloc[:, 6].values != 0]  # 去掉第6列为0的数据
+
+    # 训练和预测数据划分
+    x1 = Data['交易日期'].values >= 20170101
+    x2 = Data['交易日期'].values <= 20171031
+    index = x1 & x2
+    x_train = Data.iloc[index, 1:15]
+    y_train = Data.iloc[index, [15]]
+    x_test = Data.iloc[~index, 1:15]
+    y_test = Data.iloc[~index, [15]]
+
+    # 检查训练数据是否为空
+    if x_train.empty or y_train.empty:
+        print(f"Skipping {code} due to empty training data")
+        continue
+
+    # 数据标准化
+    scaler = StandardScaler()
+    scaler.fit(x_train)
+    x_train = scaler.transform(x_train)
+    x_test = scaler.transform(x_test)
+
+    # 逻辑回归模型
+    clf = LR()
+    clf.fit(x_train, y_train.values.ravel())  # 使用 ravel() 将 y_train 转换为 1D 数组
+    result = clf.predict(x_test)  # 预测结果
+    sc = clf.score(x_train, y_train)  # 模型准确率
+
+    result = pd.DataFrame(result)  # 预测结果转换为数据框
+    ff = Data.iloc[~index, 0]  # 提取预测样本的交易日期
+    # 将预测结果与实际结果整合在一起，进行比较
+    pm1 = {'交易日期': ff.values, '预测结果': result.iloc[:, 0].values, '实际结果': y_test.iloc[:, 0].values}
+    result1 = pd.DataFrame(pm1)
+    z = result1['预测结果'].values - result1['实际结果'].values
+    R = len(z[z == 0]) / len(z)  # 预测准确率
+
+    if sc > 0.7:
+        r_list = []
+        for t in range(len(result1) - 1):
+            if result1['预测结果'].values[t] == 1:
+                p2 = data.loc[data['trade_date'].values == result1['交易日期'].values[t + 1], 'close'].values
+                p1 = data.loc[data['trade_date'].values == result1['交易日期'].values[t + 1], 'open'].values
+                r = (p2 - p1) / p1
+                r_list.append(r)
+        r_stk = sum(r_list)
+        r_total = r_total + r_stk
+
+        print(f"{code} : {r_stk}")
+
+print(f"投资组合收益率： {r_total}")
+
+# 沪深300指数收益率计算
+hs300 = pd.read_excel('hs300.xlsx')
+x1 = hs300['trade_date'].values >= 20171101
+x2 = hs300['trade_date'].values <= 20171231
+index = x1 & x2
+p = hs300.iloc[index, 2].values
+r_hs300 = (p[-1] - p[0]) / p[0]
+print(f"沪深300同期收益率： {r_hs300}")
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