August 2022
Intermediate to advanced
394 pages
12h 32m
Chinese
Content preview from 金融人工智能：用Python实现AI量化交易
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风险管理
｜
253
11.2
向量化回测
向量化回测不能直接应用于交易机器人。第
10
章使用
DNN
说明过该方法。在这种情况
下，首先准备好具有特征和标签子集的数据，然后将其输入
DNN
模型以立即生成所有预
测。在强化学习环境中，数据是通过一步一步地与环境交互来生成和收集的。
为此，下面的
Python
代码定义了
backtest
函数，该函数将
TradingBot
实例和
Finance
实
例作为输入。它在
Finance
环境列的原始
Data Frame
对象中生成了交易机器人所持有的头
寸和最终策略收益。
In [14]: def reshape(s):
return np.reshape(s, [1, learn_env.lags,
learn_env.n_features])
➊
In [15]: def backtest(agent, env):
env.min_accuracy = 0.0
env.min_performance = 0.0
done = False
env.data['p'] = 0
➋
state = env.reset()
while not done:
action = np.argmax(
agent.model.predict(reshape(state))[0, 0])
➌
position = 1 if action == 1 else -1
➍
env.data.loc[:, 'p'].iloc[env.bar] = position
➎
state, reward, ...
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