advanced-algorithmic-trading

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from sklearn.externals import joblib
from qstrader.price_parser import PriceParser
from qstrader.event import (SignalEvent, EventType)
from qstrader.strategy.base import AbstractStrategy
The initialisation of the class is relatively straightforward. It takes the model pickle file path
through model_pickle_file as an initialisation parameter, along with the number of minutely
bar lags to use for predicting the direction of the next bar, via lags.
The strategy stores the current prices in cur_prices, which is one element longer than the
current returns array, cur_returns, since returns require n + 1 price elements to calculate n
returns. The quantity qty is set to 10,000 in this instance, since there is 500,000 USD of equity.
model contains the unpickled predictive model from Scikit-Learn:
class IntradayMachineLearningPredictionStrategy(AbstractStrategy):
"""
Requires:
tickers - The list of ticker symbols
events_queue - A handle to the system events queue
"""
def __init__(
self, tickers, events_queue,
model_pickle_file, lags=5
):
self.tickers = tickers
self.events_queue = events_queue
self.model_pickle_file = model_pickle_file
self.lags = lags
self.invested = False
self.cur_prices = np.zeros(self.lags+1)
self.cur_returns = np.zeros(self.lags)
self.minutes = 0
self.qty = 10000
self.model = joblib.load(model_pickle_file)
The _update_current_returns method is used to generate the rolling lag-length array of
returns of the AREX closing prices. It begins by reverse iterating through the current prices
array and shifts the prices by one, adding in the new most recent price.
The computer scientists among you will recognise this behaviour as well represented by a
double-ended queue (deque). Python contains such an implementation in the collections library.
For simplicity I have used NumPy arrays in this example.
Once the current prices array is generated the returns list is calculated, but only once all
elements have been populated into the list (i.e. after lags+1):
def _update_current_returns(self, event):
"""
Updates the array of current returns "features"