使用多个股票代码的数据集进行强化学习? [英] Reinforcement Learning Using Multiple Stock Ticker’s Datasets?

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问题描述

这是一个一般性问题,也许有人可以为我指明正确的方向.

Here’s a general question that maybe someone could point me in the right direction.

我正在使用 Python 3.6/Tensorflow 进行强化学习,并且我发现/调整了我自己的模型来训练特定股票的历史数据.我的问题是,是否可以在不止一只股票的数据集上训练这个模型?我读过的关于时间序列预测和 RL 的每篇机器学习文章都使用一个数据集进行训练和测试,但我的目标是在一堆价格不同的股票代码上训练模型,希望该模型能够识别相似的价格模式,无论价格或股票代码如何,我都可以将经过训练的模型应用于新数据集,并且它会起作用.

I’m getting into Reinforcement Learning with Python 3.6/Tensorflow and I have found/tweaked my own model to train on historical data from a particular stock. My question is, is it possible to train this model on more than just one stock’s dataset? Every single machine learning article I’ve read on time series prediction and RL uses one dataset for training and testing, but my goal is to train a model on a bunch of tickers with varying prices in the hopes that the model can recognize similar price patterns, regardless of the price or ticker so that I could apply the trained model to a new dataset and it’ll work.

现在它在一个股票代码和价格上进行训练,但是当我尝试添加一个新的数据集以进行额外的训练时,它的表现很糟糕,因为它不知道新价格,如果这有意义的话.

Right now it trains on one ticker and it’s prices, but when I try to add a new dataset for added training, it performs horribly because it doesn’t know the new prices, if that makes sense.

这是一个基本问题,我不一定期望得到编码答案,只是在某个地方我可以学习如何使用多个数据集训练模型.如果有帮助的话,我正在使用 OpenAI 健身房环境.

This is a basic question and I don’t necessarily expect a coded answer, just somewhere I could learn how to train a model using multiple datasets. I’m using OpenAI gym environment if that helps anything.

谢谢!

推荐答案

我认为将数据集标准化为所有数据集上次关闭的百分比变化可能是一个好的开始.这样,任何价格的任何股票似乎都正常化了.

I think normalizing the dataset with % change from previous close on all datasets could be a good start. in that way, any stock with any price seems normalized.

这篇关于使用多个股票代码的数据集进行强化学习?的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!

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