import pandas as pd import akshare as ak import numpy as np import talib from datetime import datetime, timedelta import time

獲取股票數據

from_date = '2010-01-01' from_date = datetime.strptime(from_date,"%Y-%m-%d") day_nums = 1 current_dt = time.strftime("%Y-%m-%d", time.localtime()) current_dt = datetime.strptime(current_dt, '%Y-%m-%d') previous_date = current_dt - timedelta(days=day_nums) data = ak.stock_zh_a_daily(symbol='sz000001',start_date = from_date,end_date = previous_date) df = data df.index=pd.to_datetime(df.date) cols=['Open', 'High', 'Low', 'Close'] dataset=df[['open','high','low','close']] dataset=dataset.rename(columns=dict(zip(dataset.columns,cols))) dataset.head()

01 數據預處理

dataset['H-L'] = dataset['High'] - dataset['Low'] dataset['O-C'] = dataset['Close'] - dataset['Open'] dataset['3day MA'] = dataset['Close'].shift(1).rolling(window = 3).mean() dataset['10day MA'] = dataset['Close'].shift(1).rolling(window = 10).mean() dataset['30day MA'] = dataset['Close'].shift(1).rolling(window = 30).mean() dataset['Std_dev']= dataset['Close'].rolling(5).std() dataset['RSI'] = talib.RSI(dataset['Close'].values, timeperiod = 9) dataset['Williams %R'] = talib.WILLR(dataset['High'].values, dataset['Low'].values, dataset['Close'].values, 7)dataset['Price_Rise'] = np.where(dataset['Close'].shift(-1) > dataset['Close'], 1, 0) #刪除缺失值 dataset = dataset.dropna() #查看最后五列數據 dataset.tail()X = dataset.iloc[:, 4:-1] y = dataset.iloc[:, -1]split = int(len(dataset)*0.8) X_train, X_test, y_train, y_test = X[:split], X[split:], y[:split], y[split:]from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test)

02 構建神經網絡

from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout classifier = Sequential()classifier.add(Dense(units = 128, kernel_initializer = 'uniform', activation = 'relu', input_dim = X.shape[1]))classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))classifier.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics = ['accuracy'])classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)

03 預測股票走勢

y_pred = classifier.predict(X_test) y_pred = (y_pred > 0.5)dataset['y_pred'] = np.NaN dataset.iloc[(len(dataset) - len(y_pred)):,-1:] = y_pred trade_dataset = dataset.dropna()

04 計算策略收益

import warnings warnings.filterwarnings('ignore') trade_dataset['Tomorrows Returns'] = 0. trade_dataset['Tomorrows Returns'] = np.log(trade_dataset['Close']/trade_dataset['Close'].shift(1)) trade_dataset['Tomorrows Returns'] = trade_dataset['Tomorrows Returns'].shift(-1)trade_dataset['Strategy Returns'] = 0. trade_dataset['Strategy Returns'] = np.where(trade_dataset['y_pred'] == True, trade_dataset['Tomorrows Returns'], - trade_dataset['Tomorrows Returns'])trade_dataset['Strategy Returns'] = 0. trade_dataset['Strategy Returns'] = np.where(trade_dataset['y_pred'] == True, trade_dataset['Tomorrows Returns'], - trade_dataset['Tomorrows Returns'])trade_dataset['Cumulative Market Returns'] = np.cumsum(trade_dataset['Tomorrows Returns']) trade_dataset['Cumulative Strategy Returns'] = np.cumsum(trade_dataset['Strategy Returns'])

05 收益可視化

import matplotlib.pyplot as plt trade_dataset[['Cumulative Market Returns','Cumulative Strategy Returns']].plot(figsize=(14,8));

總結

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