advanced-algorithmic-trading

397
self.invested = None
def calculate_signals(self, event):
"""
Calculate the signals for the strategy.
"""
if event.type == EventType.BAR:
self._set_correct_time_and_price(event)
# Only trade if we have all prices
if all(self.latest_prices > -1.0):
# Calculate portfolio market value via dot product
# of ETF prices with portfolio weights
self.port_mkt_val.append(
np.dot(self.latest_prices, self.weights)
)
# If there is enough data to form a full lookback
# window, then calculate zscore and carry out
# respective trades if thresholds are exceeded
if self.bars_elapsed > self.lookback:
zscore = (
self.port_mkt_val[-1] - np.mean(self.port_mkt_val)
) / np.std(self.port_mkt_val)
self.zscore_trade(zscore, event)
# coint_bollinger_backtest.py
import datetime
import click
import numpy as np
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