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Hi
I understand that the recommended way to obtain history data is using RolllingWindow instead of History. But in my algorithm I need to get the data in some matricial way. That is, in numpy array or in pandas DataFrame. So I supose that the logical path is to transforme the RollingWindow data into a array. But if you know another method, I will be very thanksfull if you can show me how.
I'm using the following code to create the array. But, this is not a very efficient method. In fact, it would be better to simply use History method, because returns the data just in the format needed for the algorithm.
def OnData(self,data):
self.data = []
self.assets = ["AAPL", "IBM"]
for asset in self.assets:
self.closes = []
if self.Data[asset].IsReady():
for i in range(self.Bars.Count - 1):
self.closes.append(self.Data[asset].Bars[i].Close)
self.data.apppend(self.closes)
Douglas Stridsberg
I don't use Python personally, but don't you think the following would help you? Why can you not use History, like you proposed yourself?
Also I'm not sure about your assertion that "RollingWindow is the preferred way to obtain historical data". In fact, RollingWindows are meant to store data for use across different iterations of the algorithm. You can't really use it to "obtain" historical data.
Alexandre Catarino
As an alternative to Lean RollingWindow, you can use python collections.deque.
It is easily convertible to a numpy array:
import numpy as np from collections import deque # In Initilialize self.queue = deque(maxlen=period) # In OnData or elsewhere self.queue.appendleft(value) arr = np.array(self.queue)Christian Weber
While looking for a solution to the same problem, I found this post.
This is how I do it now
closes = np.fromiter([i.Close for i in self.Bars], dtype=float, count=self.Bars.Count)Â
Halldor Andersen
Hi all.
Thanks for sharing. You can also fill a DataFrame with prices and use the tail() and value() methods to create a rolling window numpy array:
### In Initialize() # Create empty DataFrame self.df = pd.DataFrame(columns = self.tickers) ### In OnData() # Create an empty dictionary dict = {} # Fill the dictionary with prices for ticker in self.tickers: price = self.Securities[ticker].Price dict[ticker] = price # Append dictionary to DataFrame self.df = self.df.append(dict,ignore_index=True) # Return if DataFrame is not filled if self.IsWarmingUp: return # Retrieve last values in DataFrame and convert to numpy array self.df.tail(self.period).valuesI've attached a backtest where I use the method I describe above to retrieve a rolling window numpy array filled with prices.
Enzo Garofalo
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