Hi Chris,
From what you describe I think you are just looking to accumulate prices inside your algorithm from the start of the backtest forward. If that's the case you could so something like the example below.
I've kept this simplest case, but you can extend this logic to work with a universe of many stocks (just need to loop over each stock and add a second index to the defaultdic), you can also set the maximum number of data points you want to accumulate by passing a maxlen parameter to the deque. You can read more about the collections module to see other examples.
from collections import defaultdict, deque
import numpy as np
def initialize(context):
context.stock = sid(24)
context.prices = defaultdict(deque)
# Will be called on every trade event for the securities you specify.
def handle_data(context, data):
price = data[context.stock].price
context.prices[context.stock].append(price)
stock_series = np.array(context.prices[context.stock])
record(number_of_points_stored = len(stock_series))
Note: to pass the maxlen parameter you need to use a partially applied function which requires importing from functools as follows:
from functools import partial
and then replacing the initialization of the prices deque as follows to limit to 20 data points for example:
context.prices = defaultdict(partial(deque, maxlen=20))
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