title: how to normalize always changing data?
body: <p>I am training a model using tensorflow and i am using stock price information.  the thing i am not sure about is that i query for one stocks data and then fit with my test and validation sets using StandardScaler() but then for the next stock the data might be a different fit so im not sure how i should be addressing normalization in this issue if the training, test sequences are always going to have different scales all of the time</p>
  <pre><code> data, indexes, retdat = get_data(ticker)
      scaler = StandardScaler()
      train_data = data
      env = StockTradingEnv(train_data, initial_investment)
      testx, testy = [], []
      for e in range(env.n_step):
          x, y = env._get_obs(e)
          testx.append(x)
          testy.append(y)
  
  
      scaler.fit(testx)
      testx = scaler.transform(testx)
      tesx, valx = splitter(testx)
      tesy, valy = splitter(testy)
  </code></pre>
  <p>so this code above is kind of in a loop calling different stock tickers.  Im just not sure about the use of standard scaler here and if i should save it to a file or if using a new scaler every time is right or what.</p>
  <p>any more information i can provide thank you for your time</p>