使用pandas在Python中对移动平均线交叉策略进行回测

# ma_cross.pyclass MovingAverageCrossStrategy(Strategy):    """        Requires:    symbol - A stock symbol on which to form a strategy on.    bars - A DataFrame of bars for the above symbol.    short_window - Lookback period for short moving average.    long_window - Lookback period for long moving average."""    def __init__(self, symbol, bars, short_window=100, long_window=400):        self.symbol = symbol        self.bars = bars        self.short_window = short_window        self.long_window = long_window    def generate_signals(self):        """Returns the DataFrame of symbols containing the signals        to go long, short or hold (1, -1 or 0)."""        signals = pd.DataFrame(index=self.bars.index)        signals['signal'] = 0.0        # Create the set of short and long simple moving averages over the         # respective periods        signals['short_mavg'] = pd.rolling_mean(bars['Close'], self.short_window, min_periods=1)        signals['long_mavg'] = pd.rolling_mean(bars['Close'], self.long_window, min_periods=1)        # Create a 'signal' (invested or not invested) when the short moving average crosses the long        # moving average, but only for the period greater than the shortest moving average window        signals['signal'][self.short_window:] = np.where(signals['short_mavg'][self.short_window:]             > signals['long_mavg'][self.short_window:], 1.0, 0.0)           # Take the difference of the signals in order to generate actual trading orders        signals['positions'] = signals['signal'].diff()           return signals

# ma_cross.pyclass MarketOnClosePortfolio(Portfolio):    """Encapsulates the notion of a portfolio of positions based    on a set of signals as provided by a Strategy.    Requires:    symbol - A stock symbol which forms the basis of the portfolio.    bars - A DataFrame of bars for a symbol set.    signals - A pandas DataFrame of signals (1, 0, -1) for each symbol.    initial_capital - The amount in cash at the start of the portfolio."""    def __init__(self, symbol, bars, signals, initial_capital=100000.0):        self.symbol = symbol                self.bars = bars        self.signals = signals        self.initial_capital = float(initial_capital)        self.positions = self.generate_positions()    def generate_positions(self):        positions = pd.DataFrame(index=signals.index).fillna(0.0)        positions[self.symbol] = 100*signals['signal']   # This strategy buys 100 shares        return positions    def backtest_portfolio(self):        portfolio = self.positions*self.bars['Close']        pos_diff = self.positions.diff()        portfolio['holdings'] = (self.positions*self.bars['Close']).sum(axis=1)        portfolio['cash'] = self.initial_capital - (pos_diff*self.bars['Close']).sum(axis=1).cumsum()        portfolio['total'] = portfolio['cash'] + portfolio['holdings']        portfolio['returns'] = portfolio['total'].pct_change()        return portfolio

# ma_cross.pyif __name__ == "__main__":    # Obtain daily bars of AAPL from Yahoo Finance for the period    # 1st Jan 1990 to 1st Jan 2002 - This is an example from ZipLine    symbol = 'AAPL'    bars = DataReader(symbol, "yahoo", datetime.datetime(1990,1,1), datetime.datetime(2002,1,1))    # Create a Moving Average Cross Strategy instance with a short moving    # average window of 100 days and a long window of 400 days    mac = MovingAverageCrossStrategy(symbol, bars, short_window=100, long_window=400)    signals = mac.generate_signals()    # Create a portfolio of AAPL, with $100,000 initial capital    portfolio = MarketOnClosePortfolio(symbol, bars, signals, initial_capital=100000.0)    returns = portfolio.backtest_portfolio()    # Plot two charts to assess trades and equity curve    fig = plt.figure()    fig.patch.set_facecolor('white')     # Set the outer colour to white    ax1 = fig.add_subplot(211,  ylabel='Price in $')    # Plot the AAPL closing price overlaid with the moving averages    bars['Close'].plot(ax=ax1, color='r', lw=2.)    signals[['short_mavg', 'long_mavg']].plot(ax=ax1, lw=2.)    # Plot the "buy" trades against AAPL    ax1.plot(signals.ix[signals.positions == 1.0].index,              signals.short_mavg[signals.positions == 1.0],             '^', markersize=10, color='m')    # Plot the "sell" trades against AAPL    ax1.plot(signals.ix[signals.positions == -1.0].index,              signals.short_mavg[signals.positions == -1.0],             'v', markersize=10, color='k')    # Plot the equity curve in dollars    ax2 = fig.add_subplot(212, ylabel='Portfolio value in $')    returns['total'].plot(ax=ax2, lw=2.)    # Plot the "buy" and "sell" trades against the equity curve    ax2.plot(returns.ix[signals.positions == 1.0].index,              returns.total[signals.positions == 1.0],             '^', markersize=10, color='m')    ax2.plot(returns.ix[signals.positions == -1.0].index,              returns.total[signals.positions == -1.0],             'v', markersize=10, color='k')    # Plot the figure    fig.show()