stock_train = Stock_Data(train=True) stock_test = Stock_Data(train=False)
时间: 2024-06-07 08:10:26 浏览: 6
这段代码看起来像是在调用一个名为 Stock_Data 的类来生成训练数据和测试数据。
根据常规的 Python 类的写法,如果 Stock_Data 是一个类的话,那么它应该包含一个 __init__ 方法来初始化对象的属性,还可能包括其他方法来处理数据。在这段代码中,train=True 和 train=False 作为参数传递给 Stock_Data,可能是用来控制生成训练数据和测试数据的。
但是,我没有看到完整的代码,所以无法确定 Stock_Data 类的具体实现和用途。
相关问题
param = {'num_leaves': 31, 'min_data_in_leaf': 20, 'objective': 'binary', 'learning_rate': 0.06, "boosting": "gbdt", "metric": 'None', "verbosity": -1} trn_data = lgb.Dataset(trn, trn_label) val_data = lgb.Dataset(val, val_label) num_round = 666 # clf = lgb.train(param, trn_data, num_round, valid_sets=[trn_data, val_data], verbose_eval=100, # early_stopping_rounds=300, feval=win_score_eval) clf = lgb.train(param, trn_data, num_round) # oof_lgb = clf.predict(val, num_iteration=clf.best_iteration) test_lgb = clf.predict(test, num_iteration=clf.best_iteration)thresh_hold = 0.5 oof_test_final = test_lgb >= thresh_hold print(metrics.accuracy_score(test_label, oof_test_final)) print(metrics.confusion_matrix(test_label, oof_test_final)) tp = np.sum(((oof_test_final == 1) & (test_label == 1))) pp = np.sum(oof_test_final == 1) print('accuracy1:%.3f'% (tp/(pp)))test_postive_idx = np.argwhere(oof_test_final == True).reshape(-1) # test_postive_idx = list(range(len(oof_test_final))) test_all_idx = np.argwhere(np.array(test_data_idx)).reshape(-1) stock_info['trade_date_id'] = stock_info['trade_date'].map(date_map) stock_info['trade_date_id'] = stock_info['trade_date_id'] + 1tmp_col = ['ts_code', 'trade_date', 'trade_date_id', 'open', 'high', 'low', 'close', 'ma5', 'ma13', 'ma21', 'label_final', 'name'] stock_info.iloc[test_all_idx[test_postive_idx]] tmp_df = stock_info[tmp_col].iloc[test_all_idx[test_postive_idx]].reset_index() tmp_df['label_prob'] = test_lgb[test_postive_idx] tmp_df['is_limit_up'] = tmp_df['close'] == tmp_df['high'] buy_df = tmp_df[(tmp_df['is_limit_up']==False)].reset_index() buy_df.drop(['index', 'level_0'], axis=1, inplace=True)buy_df['buy_flag'] = 1 stock_info_copy['sell_flag'] = 0tmp_idx = (index_df['trade_date'] == test_date_min+1) close1 = index_df[tmp_idx]['close'].values[0] test_date_max = 20220829 tmp_idx = (index_df['trade_date'] == test_date_max) close2 = index_df[tmp_idx]['close'].values[0]tmp_idx = (stock_info_copy['trade_date'] >= test_date_min) & (stock_info_copy['trade_date'] <= test_date_max) tmp_df = stock_info_copy[tmp_idx].reset_index(drop=True)from imp import reload import Account reload(Account) money_init = 200000 account = Account.Account(money_init, max_hold_period=20, stop_loss_rate=-0.07, stop_profit_rate=0.12) account.BackTest(buy_df, tmp_df, index_df, buy_price='open')tmp_df2 = buy_df[['ts_code', 'trade_date', 'label_prob', 'label_final']] tmp_df2 = tmp_df2.rename(columns={'trade_date':'buy_date'}) tmp_df = account.info tmp_df['buy_date'] = tmp_df['buy_date'].apply(lambda x: int(x)) tmp_df = tmp_df.merge(tmp_df2, on=['ts_code', 'buy_date'], how='left')最终的tmp_df是什么?tmp_df[tmp_df['label_final']==1]又选取了什么股票?
根据代码逐行分析,tmp_df 是一个 DataFrame,包含了股票的信息以及回测结果。其中,选取了 label_final 为 1 的股票,也就是模型预测为涨的股票,并且过滤掉了当天涨停的股票。最终买入的股票信息保存在 buy_df 中,回测时将其与 stock_info_copy、index_df 进行合并,得到了回测的结果,并将买入的股票信息与回测结果合并,生成了最终的 tmp_df。
lstm股票预测模型python源码
LSTM(Long Short-Term Memory)是一种常用的循环神经网络(RNN)模型,用于处理序列数据。在股票预测中,LSTM可以用来学习和预测股票价格的趋势。
以下是一个简单的使用Python编写的LSTM股票预测模型的源码示例:
```python
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import LSTM, Dense
# 读取股票数据
data = pd.read_csv('stock_data.csv')
# 数据预处理
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1, 1))
# 划分训练集和测试集
train_size = int(len(scaled_data) * 0.8)
train_data = scaled_data[:train_size, :]
test_data = scaled_data[train_size:, :]
# 创建训练集和测试集
def create_dataset(dataset, time_step=1):
X, Y = [], []
for i in range(len(dataset) - time_step - 1):
a = dataset[i:(i + time_step), 0]
X.append(a)
Y.append(dataset[i + time_step, 0])
return np.array(X), np.array(Y)
time_step = 10
X_train, y_train = create_dataset(train_data, time_step)
X_test, y_test = create_dataset(test_data, time_step)
# 转换为LSTM所需的输入格式 [样本数,时间步长,特征数]
X_train = X_train.reshape(X_train.shape, X_train.shape, 1)
X_test = X_test.reshape(X_test.shape, X_test.shape, 1)
# 构建LSTM模型
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(time_step, 1)))
model.add(LSTM(50))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
# 模型训练
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, batch_size=64, verbose=1)
# 预测股票价格
train_predict = model.predict(X_train)
test_predict = model.predict(X_test)
# 反归一化
train_predict = scaler.inverse_transform(train_predict)
y_train = scaler.inverse_transform([y_train])
test_predict = scaler.inverse_transform(test_predict)
y_test = scaler.inverse_transform([y_test])
# 计算均方根误差(RMSE)
train_rmse = np.sqrt(np.mean(np.power((y_train - train_predict), 2)))
test_rmse = np.sqrt(np.mean(np.power((y_test - test_predict), 2)))
print("训练集RMSE:", train_rmse)
print("测试集RMSE:", test_rmse)
```
请注意,这只是一个简单的示例,实际的股票预测模型可能需要更多的特征和更复杂的网络结构来提高预测准确性。你可以根据自己的需求进行修改和优化。
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