时间序列模型预测股价，未完成。

Author: zwdnet

Diff for: output/PSO_svm.png

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+# coding:utf-8
+# 时间序列模型ARMA预测股价
+
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import MinMaxScaler
+import tools
+import run
+from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
+from statsmodels.tsa.arima_model import ARMA
+from statsmodels.tsa.arima_model import _arma_predict_out_of_sample as out_predict
+import warnings
+warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA', FutureWarning)
+
+
+# 准备数据
+def readData(code="sh000300", start="2018-01-01", end="2018-12-31", refresh = False):
+    # 加载数据
+    tools.initOutput()
+    data = tools.loadData(code=code, start=start, end=end, refresh=refresh)
+    # 筛选特征
+    feature_cols = ["close"]
+    features_data = data.loc[:, feature_cols]
+    scaler = MinMaxScaler(feature_range=(1, 2))
+    features = scaler.fit_transform(features_data.values)
+    x = []
+    for i in range(len(features)):
+        x.append(features[i][0])
+    new_data = pd.DataFrame({"close":x})
+    new_data["date"] = data.index
+    new_data.set_index("date", inplace = True)
+    print(new_data.head())
+    return new_data
+    
+    
+# 画数据
+@run.change_dir
+def draw(data):
+    plt.figure()
+    plt.plot(data.close)
+    plt.savefig("./output/rawdata.png")
+    # 画自相关函数
+    plot_acf(data.close, lags=25, title="acf")
+    plt.savefig("./output/acf.png")
+    # 画自相关函数
+    plot_pacf(data.close, lags=25, title="pacf")
+    plt.savefig("./output/pacf.png")
+    plt.close()
+    
+    
+# 将数据差分
+def diff_data(data):
+    diff = data.diff(1).dropna()
+    return diff
+    
+    
+# 寻找参数p和q
+def findPQ(data):
+    # N = len(data)
+    best = float("inf")
+    bestp, bestq = -1, -1
+    N = 20
+    for p in range(1, N):
+        for q in range(1, N):
+            try:
+                arma = ARMA(data, (p, q)).fit(disp = -1)
+                aic = arma.aic
+                print(p, q, aic)
+                if aic < best:
+                    best = aic
+                    bestp = p
+                    bestq = q
+            except:
+                print("出现异常")
+    return p, q
+    
+    
+# 用ARMA模型建模并预测
+@run.change_dir
+@run.timethis
+def arma_model(train_data, p, q):
+    data = readData(start = "2019-01-01", end = "2019-12-31")
+    data = diff_data(data)
+    model = ARMA(train_data, (p, q)).fit()
+    print(model.summary)
+    print(model.conf_int())
+    history_p = train_data
+    pred_p = []
+    print("测试", history_p)
+    for i in range(len(data)):
+        model_p = ARMA(train_data, p, q)
+        model_fit_p = model_p.fit(disp = -1)
+        yhat_p = model_fit_p.predict(start = len(history_p), end = len(history_p))[0]
+        pred_p.append(yhat_p)
+        history_p.append(yhat_p)
+        print("测试", yhat_p)
+    results = pd.DataFrame()
+    results["pred"] = pred_p
+    results["real"] = data
+    results["date"] = data.index
+    results.set_index("date", inplace = True)
+    print("预测结果")
+    print(results.head())
+#    # predict = model.forecast()
+#    print("预测")
+#    plt.figure()
+#    model.plot_predict(start = 10, end = 200)
+#    plt.savefig("./output/predict_arma.png")
+#    plt.close()
+#    # print(model.forecast(5))
+#    pred = model.predict()
+#    # 提取参数
+#    params = model.params
+#    residuals = model.resid
+#    p = model.k_ar
+#    q = model.k_ma
+#    k_exog = model.k_exog
+#    k_trend = model.k_trend
+#    steps = 1
+
+#    # 样本内数据验证
+#    in_data = train_data.iloc[:11, :]
+#    in_resid = residuals.iloc[0:11]
+#    a = out_predict(params, steps, in_resid, p, q, k_trend, k_exog, endog = in_data["close"], exog = None, start = len(in_data))
+#    test_pred = pred[8:13]
+#    print(test_pred, a[0])
+#    # 样本外预测
+#    new_resid =  residuals.tail(9).copy()
+#    new_data = train_data.tail(9).copy()
+#    for  i in range(len(data)):
+#        print(i)
+#        a = out_predict(params, steps, in_resid, p, q, k_trend, k_exog, endog = new_data["close"], exog = None, start = len(new_data))
+#        tomorrow_index = data.index[i]
+#        temp_resid = data.loc[tomorrow_index,'close'] - a[0]
+#        new_resid[tomorrow_index] = temp_resid
+#        new_resid.drop(new_resid.index[0],inplace=True)
+#        new_data = new_data.append(data.iloc[i,:])
+#        # new_data.drop(data.index[0], inplace=True)
+#        pred[tomorrow_index] = a[0]
+
+
+if __name__ == "__main__":
+    data = readData()
+    draw(data)
+    data = diff_data(data)
+    draw(data)
+    # p, q = findPQ(data)
+    arma_model(data, 9, 9)
+