advanced-algorithmic-trading

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thresholds are set to 1.5 and 0.5 standard deviations, respectively. Since the account equity is
set at 500,000 USD the base_quantity of shares is set to 10,000.
These values can all be tested and optimised, such as with a grid-search procedure if desired.
# coint_bollinger_strategy.py
..
..
# Use the Cointegration Bollinger Bands trading strategy
weights = np.array([1.0, -1.213])
lookback = 15
entry_z = 1.5
exit_z = 0.5
base_quantity = 10000
strategy = CointegrationBollingerBandsStrategy(
tickers, events_queue,
lookback, weights,
entry_z, exit_z, base_quantity
)
strategy = Strategies(strategy, DisplayStrategy())
..
..
To run the backtest a working installation of QSTrader is needed and these two files described
above need to be placed in the same directory. Assuming the availability of the ARNC and UNG
data, the backtest will execute upon typing the following command into the terminal:
$ python coint_bollinger_backtest.py --tickers=ARNC,UNG
You will receive the following (truncated) output:
..
..
Backtest complete.
Sharpe Ratio: 1.22071888063
Max Drawdown: 0.0701967400339
Max Drawdown Pct: 0.0701967400339
27.6 Strategy Results
27.6.1 Transaction Costs
The strategy results presented here are given net of transaction costs. The costs are simulated
using Interactive Brokers US equities fixed pricing for shares in North America. They do not