In [ ]:%matplotlib inline # Imports from clr import AddReference AddReference("System") AddReference("QuantConnect.Common") AddReference("QuantConnect.Research") AddReference("QuantConnect.Indicators") from System import * from QuantConnect import * from QuantConnect.Data.Custom import * from QuantConnect.Data.Market import TradeBar, QuoteBar from QuantConnect.Research import * from QuantConnect.Indicators import * from datetime import datetime, timedelta import matplotlib.pyplot as plt import pandas as pd # Create an instance qb = QuantBook() # Select asset data spy = qb.AddEquity("SPY")
We can use the QuantConnect API to make Historical Data Requests. The data will be presented as multi-index pandas.DataFrame where the first index is the Symbol.
For more information, please follow the link.
In [ ]:# Gets historical data from the subscribed assets, the last 360 datapoints with daily resolution h1 = qb.History(qb.Securities.Keys, 360, Resolution.Daily) # Plot closing prices from "SPY" h1.loc["SPY"]["close"].plot()
In [ ]:# Example with BB, it is a datapoint indicator # Define the indicator bb = BollingerBands(30, 2) # Gets historical data of indicator bbdf = qb.Indicator(bb, "SPY", 360, Resolution.Daily) # drop undesired fields bbdf = bbdf.drop('standarddeviation', 1) # Plot bbdf.plot()