RC2_LEARNING_SET_OUTSIDE_RANGE

תוכן זה אינו זמין עדיין בשפה שלך.

In <set>, <name>=Range(...) goes outside the strategy’s own optimizer range for it.

What it means

Every value a learning set gives a number must sit inside that Param’s opt_min..opt_max, have the Param’s kind (a whole number for a whole-number Param), and a Range must run forwards with a step above 0. A switch takes True, False or Range(False, True), and a choice one of its own values.

Why it happens

Below the Param’s opt_min of 5:

class TooWide(Strategy):
    trade = Symbol()
    sizing = FixedCapital()
    fast = Param(10, min=2, max=100, opt_min=5, opt_max=30)
    slow = Param(30, min=10, max=200, opt_min=20, opt_max=80, opt_step=5)
    offset = Param(0.01, min=0.0, max=1.0)
    use_filter = Param(False)

    learning_set_2 = LearningSet(fast=Range(2, 15))

    def execute(self):
        f = SMA(self.fast, on=self.trade)
        s = SMA(self.slow, on=self.trade)
        if f.value > s.value and self.trade.is_flat:
            self.buy(self.trade)

A fraction on a whole-number Param:

class Fraction(Strategy):
    trade = Symbol()
    sizing = FixedCapital()
    fast = Param(10, min=2, max=100, opt_min=5, opt_max=30)
    slow = Param(30, min=10, max=200, opt_min=20, opt_max=80, opt_step=5)
    offset = Param(0.01, min=0.0, max=1.0)
    use_filter = Param(False)

    learning_set_2 = LearningSet(fast=Range(5.5, 15))

    def execute(self):
        f = SMA(self.fast, on=self.trade)
        s = SMA(self.slow, on=self.trade)
        if f.value > s.value and self.trade.is_flat:
            self.buy(self.trade)

How to fix it

Keep the set inside the Param’s own optimizer range — or widen opt_min/opt_max on the Param itself.

class InsideRange(Strategy):
    trade = Symbol()
    sizing = FixedCapital()
    fast = Param(10, min=2, max=100, opt_min=5, opt_max=30)
    slow = Param(30, min=10, max=200, opt_min=20, opt_max=80, opt_step=5)
    offset = Param(0.01, min=0.0, max=1.0)
    use_filter = Param(False)

    learning_set_2 = LearningSet(fast=Range(5, 15))

    def execute(self):
        f = SMA(self.fast, on=self.trade)
        s = SMA(self.slow, on=self.trade)
        if f.value > s.value and self.trade.is_flat:
            self.buy(self.trade)

Learning sets
Param() — opt_min, opt_max, opt_step
RC2_LEARNING_SET_NOT_OPTIMIZABLE — the Param has no optimizer range at all.