Financial Trading in Python
Chelsea Yang
Data Science Instructor
Question:
Solution: strategy optimization
def signal_strategy(ticker, period, name, start='2018-4-1', end='2020-11-1'):
# Get the data and calculate SMA price_data = bt.get(ticker, start=start, end=end) sma = price_data.rolling(period).mean()
# Define the signal-based strategy bt_strategy = bt.Strategy(name, [bt.algos.SelectWhere(price_data>sma), bt.algos.WeighEqually(), bt.algos.Rebalance()])
# Return the backtest return bt.Backtest(bt_strategy, price_data)
ticker = 'aapl' sma20 = signal_strategy(ticker, period=20, name='SMA20') sma50 = signal_strategy(ticker, period=50, name='SMA50') sma100 = signal_strategy(ticker, period=100, name='SMA100')
# Run backtests and compare results bt_results = bt.run(sma20, sma50, sma100) bt_results.plot(title='Strategy optimization')
A standard or point of reference against which a strategy can be compared or assessed.
def buy_and_hold(ticker, name, start='2018-11-1', end='2020-12-1'):
# Get the data price_data = bt.get(ticker, start=start_date, end=end_date)
# Define the benchmark strategy bt_strategy = bt.Strategy(name, [bt.algos.RunOnce(), bt.algos.SelectAll(), bt.algos.WeighEqually(), bt.algos.Rebalance()])
# Return the backtest return bt.Backtest(bt_strategy, price_data)
benchmark = buy_and_hold(ticker, name='benchmark')
# Run all backtests and plot the resutls bt_results = bt.run(sma20, sma50, sma100, benchmark) bt_results.plot(title='Strategy benchmarking')
Financial Trading in Python