Optimize with LetTrade Grid Search¶

Grid Search¶

Sample Strategy¶

In [1]:

from lettrade import DataFeed, Strategy
from lettrade.exchange.backtest import ForexBackTestAccount, let_backtest


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

    def indicators(self, df: DataFeed):
        df["ema1"] = df.i.ema(window=self.ema1_window)
        df["ema2"] = df.i.ema(window=self.ema2_window)

        df["signal_ema_crossover"] = df.i.crossover(df.ema1, df.ema2)
        df["signal_ema_crossunder"] = df.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 - 0.001, tp=price + 0.001)
        elif df.l.signal_ema_crossunder[-1]:
            price = df.l.close[-1]
            self.sell(size=0.1, sl=price + 0.001, tp=price - 0.001)


lt = let_backtest(
    strategy=SmaCross,
    datas="example/data/data/EURUSD_5m-0_10000.csv",
    account=ForexBackTestAccount,
    # plotter=None,
)

from lettrade import DataFeed, Strategy from lettrade.exchange.backtest import ForexBackTestAccount, let_backtest class SmaCross(Strategy): ema1_window = 9 ema2_window = 21 def indicators(self, df: DataFeed): df["ema1"] = df.i.ema(window=self.ema1_window) df["ema2"] = df.i.ema(window=self.ema2_window) df["signal_ema_crossover"] = df.i.crossover(df.ema1, df.ema2) df["signal_ema_crossunder"] = df.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 - 0.001, tp=price + 0.001) elif df.l.signal_ema_crossunder[-1]: price = df.l.close[-1] self.sell(size=0.1, sl=price + 0.001, tp=price - 0.001) lt = let_backtest( strategy=SmaCross, datas="example/data/data/EURUSD_5m-0_10000.csv", account=ForexBackTestAccount, # plotter=None, )

Optimize¶

LetTrade will auto cache optimize result

In [2]:

lt.optimize(ema1_window=[5, 6, 7, 8, 9, 10], ema2_window=range(10, 50, 1))

lt.optimize(ema1_window=[5, 6, 7, 8, 9, 10], ema2_window=range(10, 50, 1))

  Optimizing ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% [240/240] 0:00:00 0:00:05m 0:00:0500:04

Rerun Optimize will reuse optimize result cached

In [3]:

lt.optimize(ema1_window=range(5, 25), ema2_window=range(10, 50, 1))

lt.optimize(ema1_window=range(5, 25), ema2_window=range(10, 50, 1))

  Optimizing ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% [800/800] 0:00:00 0:00:10m 0:00:1000:10

Plot¶

In [4]:

lt.plotter.heatmap(x="ema1_window", y="ema2_window", z="equity")

lt.plotter.heatmap(x="ema1_window", y="ema2_window", z="equity")

In [5]:

lt.plotter.contour(x="ema1_window", y="ema2_window", z="equity")

lt.plotter.contour(x="ema1_window", y="ema2_window", z="equity")

Optimize from cache¶

Load optimize result from cache

In [6]:

lt.optimize_cache()

lt.optimize_cache()

Plot¶

In [7]:

lt.plotter.heatmap()

lt.plotter.heatmap()

In [8]:

lt.plotter.contour()

lt.plotter.contour()