advanced-algorithmic-trading

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It also changes the FixedPositionSizer to the NaivePositionSizer. The latter is used
to "naively" accept the suggestions of absolute quantities of ETF units to trade as determined
in the KalmanPairsTradingStrategy class. In a production environment it would be necessary
to adjust this depending upon the risk management goals of the portfolio.
Here is the full code for the kalman_qstrader_backtest.py:
import datetime
import click
from qstrader import settings
from qstrader.compat import queue
from qstrader.price_parser import PriceParser
from qstrader.price_handler.yahoo_daily_csv_bar import \
YahooDailyCsvBarPriceHandler
from qstrader.strategy import Strategies, DisplayStrategy
from qstrader.position_sizer.naive import NaivePositionSizer
from qstrader.risk_manager.example import ExampleRiskManager
from qstrader.portfolio_handler import PortfolioHandler
from qstrader.compliance.example import ExampleCompliance
from qstrader.execution_handler.ib_simulated import \
IBSimulatedExecutionHandler
from qstrader.statistics.tearsheet import TearsheetStatistics
from qstrader.trading_session.backtest import Backtest
from kalman_qstrader_strategy import KalmanPairsTradingStrategy
def run(config, testing, tickers, filename):
# Set up variables needed for backtest
events_queue = queue.Queue()
csv_dir = config.CSV_DATA_DIR
initial_equity = PriceParser.parse(100000.00)
# Use Yahoo Daily Price Handler
start_date = datetime.datetime(2009, 8, 3)
end_date = datetime.datetime(2016, 8, 1)
price_handler = YahooDailyCsvBarPriceHandler(
csv_dir, events_queue, tickers,
start_date=start_date, end_date=end_date
)
# Use the KalmanPairsTrading Strategy
strategy = KalmanPairsTradingStrategy(tickers, events_queue)
strategy = Strategies(strategy, DisplayStrategy())