LSTM机器学习预测沪深300指数涨跌来交易股指期货

1. 从tushare获取沪深300数据

指标计算模块下载

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import tushare as ts
import tool.MyTT as zb
import copy
from sklearn.preprocessing import MinMaxScaler

# ###################数据获取
pro = ts.pro_api('自己的key')
df = pro.index_daily(ts_code='000300.SH', start_date='20060101')  # end_date='20220711'
df = df.iloc[::-1]

2. 特征工程

说明:用沪深300当日收益率、5日收益率和5日均线乖离作为输入特征,标签用第二天的收益率。将三个输入特征和标签转换为1和-1,如收益率大于零为1代表张,小于零为-1代表跌。

# 特征工程
df['cma_5'] = zb.MA(df.close.values, 5)
df['atr_5'] = (zb.ATR(df.close.values, df.high.values, df.low.values, 5))/df.close*100

df['syl_1'] = ((df['close']-df['close'].shift(1))/df['close'].shift(1)*100) > 0
df['syl_1'] = df['syl_1'] = (df['syl_1'].astype(int)).replace(0, -1)  # 将bool变量转换为1 0数值, 并将0替换为-1
df['syl_5'] = ((df['close']-df['close'].shift(5))/df['close'].shift(5)*100) > 0
df['syl_5'] = df['syl_5'] = (df['syl_5'].astype(int)).replace(0, -1)
df['jxqs_5'] = ((df['close'] > df['cma_5']).astype(int)).replace(0, -1)
df = df.fillna(method='bfill')  # 向上填充

sel_col = ['syl_1', 'syl_5', 'jxqs_5']
df_main = copy.deepcopy(df[sel_col])


# 数据归一化,这里本来就是用的1和-1也可以不归一化
scaler = MinMaxScaler(feature_range=(-1, 1))
for col in sel_col:                           # 这里不能进行统一进行缩放,因为fit_transform返回值是numpy类型
    df_main[col] = scaler.fit_transform(df_main[col].values.reshape(-1, 1))
# 将下一日的收盘价作为本日的标签
df_main['target'] = df_main['syl_1'].shift(-1)
# df_main = df_main.dropna()                      # 使用了shift函数,在最后必然是有缺失值的,这里去掉缺失值所在行
df_main['target'].iloc[-1] = df_main['target'].iloc[-2]  # 替换最后一个nan为上一个值
df_main = df_main.astype(np.float32)  # 修改数据类型

LSTM机器学习预测沪深300指数涨跌来交易股指期货_第1张图片
LSTM机器学习预测沪深300指数涨跌来交易股指期货_第2张图片

3. 建立LSTM模型

import torch.nn as nn

input_dim = 3      # 数据的特征数
hidden_dim = 32    # 隐藏层的神经元个数
num_layers = 2     # LSTM的层数
output_dim = 1     # 预测值的特征数(这是预测股票价格,所以这里特征数是1,如果预测一个单词,那么这里是one-hot向量的编码长度)


class LSTM(nn.Module):
    def __init__(self, input_dim, hidden_dim, num_layers, output_dim):
        super(LSTM, self).__init__()
        # Hidden dimensions
        self.hidden_dim = hidden_dim

        # Number of hidden layers
        self.num_layers = num_layers

        # Building your LSTM
        # batch_first=True causes input/output tensors to be of shape (batch_dim, seq_dim, feature_dim)
        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)

        # Readout layer 在LSTM后再加一个全连接层,因为是回归问题,所以不能在线性层后加激活函数
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        # Initialize hidden state with zeros
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()
        # 这里x.size(0)就是batch_size

        # Initialize cell state
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()

        # One time step
        # We need to detach as we are doing truncated backpropagation through time (BPTT)
        # If we don't, we'll backprop all the way to the start even after going through another batch
        out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))

4. 按照模型接口组织训练数据

# 创建两个列表,用来存储数据的特征和标签
data_feat, data_target = [], []
# 设每条数据序列有10组数据
seq = 10

for index in range(len(df_main) - seq):
    # 构建特征集
    data_feat.append(df_main[sel_col][index: index + seq].values)
    # 构建target集
    data_target.append(df_main['target'][index:index + seq])

# 将特征集和标签集整理成numpy数组
data_feat = np.array(data_feat)
data_target = np.array(data_target)

# ############## 训练集与测试集的划分 todo
# 这里按照9:1的比例划分训练集和测试集
test_set_size = int(np.round(1*df_main.shape[0]))  # np.round(1)是四舍五入,
train_size = data_feat.shape[0] - (test_set_size)
print(test_set_size)  # 输出测试集大小
print(train_size)     # 输出训练集大小

trainX = torch.from_numpy(data_feat[800:train_size].reshape(-1, seq, input_dim)).type(torch.Tensor)
# 这里第一个维度自动确定,我们认为其为batch_size,因为在LSTM类的定义中,设置了batch_first=True
testX = torch.from_numpy(data_feat[train_size:].reshape(-1, seq, input_dim)).type(torch.Tensor)
testX2 = torch.from_numpy(data_feat[2000:].reshape(-1, seq, input_dim)).type(torch.Tensor)

trainY = torch.from_numpy(data_target[800:train_size].reshape(-1, seq, 1)).type(torch.Tensor)
testY = torch.from_numpy(data_target[train_size:].reshape(-1, seq, 1)).type(torch.Tensor)
testY2 = torch.from_numpy(data_target[2000:].reshape(-1, seq, 1)).type(torch.Tensor)

print('x_train.shape = ', trainX.shape)
print('y_train.shape = ', trainY.shape)
print('x_test.shape = ', testX.shape)
print('x_test.shape2 = ', testX2.shape)

print('y_test.shape = ', testY.shape)

5. 训练模型

import torch.utils.data
batch_size = train_size  # 等于训练集大小
train = torch.utils.data.TensorDataset(trainX, trainY)
test = torch.utils.data.TensorDataset(testX, testY)
train_loader = torch.utils.data.DataLoader(dataset=train,
                                           batch_size=batch_size,
                                           shuffle=False)

test_loader = torch.utils.data.DataLoader(dataset=test,
                                          batch_size=batch_size,
                                          shuffle=False)
# 实例化模型
model = LSTM(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim, num_layers=num_layers)

# 定义优化器和损失函数
optimiser = torch.optim.Adam(model.parameters(), lr=0.01)  # 使用Adam优化算法
loss_fn = torch.nn.MSELoss(size_average=True)  # 使用均方差作为损失函数

# 打印模型结构
print(model)
# 打印模型各层的参数尺寸
for i in range(len(list(model.parameters()))):
    print(list(model.parameters())[i].size())

# 设定数据遍历次数
num_epochs = 90
# 训练模型
hist = np.zeros(num_epochs)
for t in range(num_epochs):
    y_train_pred = model(trainX)

    loss = loss_fn(y_train_pred, trainY)
    if t % 10 == 0 and t != 0:  # 每训练十次,打印一次均方差,
        print("训练次数 ", t, "均方差RMSE: ", loss.item())
    hist[t] = loss.item()

    # Zero out gradient, else they will accumulate between epochs 将梯度归零
    optimiser.zero_grad()
    # Backward pass
    loss.backward()
    # Update parameters
    optimiser.step()

LSTM机器学习预测沪深300指数涨跌来交易股指期货_第3张图片

  • 计算训练得到的模型在训练集上的均方差
y_train_pred = model(trainX)
loss_fn(y_train_pred, trainY).item()

在这里插入图片描述

6. 做出预测

y_test_pred = model(testX2)
loss_fn(y_test_pred, testY2).item()
pred_value = y_test_pred.detach().numpy()[:, -1, 0]
true_value = testY2.detach().numpy()[:, -1, 0]

data2 = pd.read_csv('公式读写数据库配置文件 - syl.csv')
data2['块名'] = ['hs300ycsyl' for var in range(len(pred_value))]
data2['键名'] = df['trade_date'][-len(pred_value):].values
data2['值'] = pred_value[:, 0]  # 有归一化时用
# data2['值'] = pred_value

# data2 = pd.DataFrame(pred_value[:, 0], df['trade_date'][-len(pred_value):].values)
data2.to_csv('hs300ycsyl.csv',  index=None, encoding='utf_8_sig')
print('样本外开始日期=', df['trade_date'][-len(testX):].values[0])
plt.plot(pred_value, label="Preds")  # 预测值
plt.plot(true_value, label="Data")  # 真实值
plt.legend()
plt.show()

• 看图拟合的可以
LSTM机器学习预测沪深300指数涨跌来交易股指期货_第4张图片

7. 将拟合的涨跌结果导出tb交易开拓者能识别的数据格式,然后导入tb本地数据库,读取进行回测

data2 = pd.DataFrame({'块名': [], '键名': [], '值': []})
data2['块名'] = ['hs300ycsyl' for var in range(len(pred_value))]
data2['键名'] = df['trade_date'][-len(pred_value):].values
data2['值'] = pred_value[:, 0]  # 有归一化时用

• tb交易开拓者策略编写,预测值大于0时做多,小于0时开空,反手策略。

//------------------------------------------------------------------------
// 简称: lstm_hc
// 名称: 机器学习回测
// 类别: 公式应用
// 类型: 用户应用
// 输出: Void
//------------------------------------------------------------------------
Params
  //此处添加参数

Vars
  Series<Numeric> yc_close;
  Series<Numeric> jiaoyiri;

Defs
  //此处添加公式函数
  
Events

  //初始化事件函数,策略运行期间,首先运行且只有一次,应用在订阅数据等操作
  OnInit()
  {
    //与数据源有关
    Range[0:DataCount-1]
    {
      //=========数据源相关设置==============
      //AddDataFlag(Enum_Data_RolloverBackWard());  //设置后复权

      //AddDataFlag(Enum_Data_RolloverRealPrice());  //设置映射真实价格

      AddDataFlag(Enum_Data_AutoSwapPosition());  //设置自动换仓

      AddDataFlag(Enum_Data_IgnoreSwapSignalCalc());  //设置忽略换仓信号计算
      Bool ret = SetSwapPosVolType(2); //设置自动换仓量类型
      
      //AddDataFlag(Enum_Data_OnlyDay());    //设置仅日盘
      
      //AddDataFlag(Enum_Data_OnlyNight());  //设置仅夜盘
      
      //AddDataFlag(Enum_Data_NotGenReport());  //设置数据源不参与生成报告标志
      
      //=========交易相关设置==============
            //MarginRate rate;
            //rate.ratioType = Enum_Rate_ByFillAmount; //设置保证金费率方式为成交金额百分比
            //rate.longMarginRatio = 0.1; //设置保证金率为10%
            //rate.shortMarginRatio = 0.2; //设置保证金率为20%
      //SetMarginRate(rate);  
      
      CommissionRate tCommissionRate;
      tCommissionRate.ratioType = Enum_Rate_ByFillAmount;
      tCommissionRate.openRatio = 1; //设置开仓手续费为成交金额的5%%
      tCommissionRate.closeRatio = 1; //设置平仓手续费为成交金额的2%%
      tCommissionRate.closeTodayRatio = 1; //设置平今手续费为0
      SetCommissionRate(tCommissionRate); //设置手续费率
      
      SetSlippage(Enum_Rate_PointPerHand,2);  //设置滑点为2跳/手
    
    //=========交易相关设置==============
    SetInitCapital(10000000);  //设置初始资金为100万

  }


  //Bar更新事件函数,参数indexs表示变化的数据源图层ID数组
  OnBar(ArrayRef<Integer> indexs)
  {
    jiaoyiri = TrueDate();
    if(jiaoyiri<>jiaoyiri[1]) data1.yc_close = Value(GetTBProfileString("hs300ycsyl",text(data1.Date)));
    Commentary("预测收益率="+Text(data1.yc_close));
    if(MarketPosition != 1 and data1.yc_close != InvalidNumeric and data1.yc_close>0.5)
    {
      Buy(1, Open);
    }
    
    if(MarketPosition != -1 and data1.yc_close != InvalidNumeric and data1.yc_close<-0.5)
    {
      SellShort(1, Open);
    }  
    
  }
//------------------------------------------------------------------------
// 编译版本  2023/02/01 084226
// 版权所有  jinxin168
// 更改声明  TradeBlazer Software保留对TradeBlazer平台
// 每一版本的TradeBlazer公式修改和重写的权利
//------------------------------------------------------------------------

8. 信号与回测结果

• 下面是样本内信号,非常好。
LSTM机器学习预测沪深300指数涨跌来交易股指期货_第5张图片

• 下面是样本外信号,一般。
LSTM机器学习预测沪深300指数涨跌来交易股指期货_第6张图片

• 下面是回测结果。
LSTM机器学习预测沪深300指数涨跌来交易股指期货_第7张图片
LSTM机器学习预测沪深300指数涨跌来交易股指期货_第8张图片

结论:从回测结果看,有点过拟合,给的三个特征也不是很好,可以加入更多相关性强的特征,注意,类似于价格的特征需要做平稳性检验。

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