QTPyLib

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Example 1

Call QTPyLib indicator

from lettrade import DataFeed, Strategy
from lettrade import indicator as i
from lettrade.exchange.backtest import ForexBackTestAccount, let_backtest
from lettrade.indicator.vendor.qtpylib import ema


class SmaCross(Strategy):
    ema1_window = 9
    ema2_window = 21

    def indicators(self, df: DataFeed):
        # QTPyLib function call
        df["ema1"] = ema(df.close, window=self.ema1_window)
        df["ema2"] = ema(df.close, window=self.ema2_window)

        df["signal_ema_crossover"] = i.crossover(df.ema1, df.ema2)
        df["signal_ema_crossunder"] = i.crossunder(df.ema1, df.ema2)

    def next(self, df: DataFeed):
        if len(self.orders) > 0 or len(self.positions) > 0:
            return

        if df.l.signal_ema_crossover[-1]:
            price = df.l.close[-1]
            self.buy(size=0.1, sl=price - 0.01, tp=price + 0.01)
        elif df.l.signal_ema_crossunder[-1]:
            price = df.l.close[-1]
            self.sell(size=0.1, sl=price + 0.01, tp=price - 0.01)

    def plot(self, df: DataFeed):
        from lettrade.plot.plotly import PlotColor, plot_line, plot_merge

        plot_ema1 = plot_line(df["ema1"], color=PlotColor.AMBER)
        plot_ema2 = plot_line(df["ema2"], color=PlotColor.CYAN)
        return plot_merge(plot_ema1, plot_ema2)


if __name__ == "__main__":
    lt = let_backtest(
        strategy=SmaCross,
        datas="example/data/data/EURUSD_5m-0_1000.csv",
        account=ForexBackTestAccount,
    )

    lt.run()
    lt.plot()

Example 2

Inject QTPyLib to Pandas.DataFrame

from lettrade import DataFeed, Strategy
from lettrade import indicator as i
from lettrade.exchange.backtest import ForexBackTestAccount, let_backtest
from lettrade.indicator.vendor.qtpylib import inject_indicators

# Inject QTPyLib
inject_indicators()


class SmaCross(Strategy):
    ema1_window = 9
    ema2_window = 21

    def indicators(self, df: DataFeed):
        # QTPyLib injected function call
        df["ema1"] = df.close.ema(window=self.ema1_window)
        df["ema2"] = df.close.ema(window=self.ema2_window)

        df["signal_ema_crossover"] = i.crossover(df.ema1, df.ema2)
        df["signal_ema_crossunder"] = i.crossunder(df.ema1, df.ema2)

    def next(self, df: DataFeed):
        if len(self.orders) > 0 or len(self.positions) > 0:
            return

        if df.l.signal_ema_crossover[-1]:
            price = df.l.close[-1]
            self.buy(size=0.1, sl=price - 0.01, tp=price + 0.01)
        elif df.l.signal_ema_crossunder[-1]:
            price = df.l.close[-1]
            self.sell(size=0.1, sl=price + 0.01, tp=price - 0.01)

    def plot(self, df: DataFeed):
        from lettrade.plot.plotly import PlotColor, plot_line, plot_merge

        plot_ema1 = plot_line(df["ema1"], color=PlotColor.AMBER)
        plot_ema2 = plot_line(df["ema2"], color=PlotColor.CYAN)
        return plot_merge(plot_ema1, plot_ema2)


if __name__ == "__main__":
    lt = let_backtest(
        strategy=SmaCross,
        datas="example/data/data/EURUSD_5m-0_1000.csv",
        account=ForexBackTestAccount,
    )

    lt.run()
    lt.plot()