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RC2_IMPORT_BLOCKED

import statements are not allowed in RaamCode.

What it means

RaamCode strategies cannot import any module — neither the Python standard library nor third-party packages. The transpiler refuses every import and from ... import statement.

Why

Three reasons:

  1. Determinism. Imported modules can read the environment, make network calls, or have version-specific behaviour. None of those are safe in a strategy that must produce identical results in backtest, paper, and live.
  2. Isolation. Strategies share infrastructure. An import that could pull arbitrary code defeats the entire sandbox.
  3. Reviewability. A strategy that’s a few classes and arithmetic is auditable. One that imports random, requests, or numpy is not.

How to fix it

Every common motivation for an import has a RaamCode replacement:

You wrote Use this instead
import numpy as np for np.mean, np.std sum(series) / len(series) inside a custom indicator, or the built-in StdDev.
import math for math.sqrt, math.log Nothing — math.* is ambient. Write math.sqrt(x) with no import at all.
import datetime to read the date self.trade.timestamp.hour / .minute / .weekday() / .month, and self.bar_count for the bar index.
from typing import List RaamCode is untyped at the source level. Drop the import.
import random Strategies must be deterministic. There is no random API, by design.

import math gets its own message, because it is the one import people write for a library that is already there:

import math is not needed — the math.* standard library is always available. Remove this line.

For numerical work inside an indicator, use Series plus a custom Indicator subclass — see Custom Indicators.

Common patterns

import math


class Bad(Strategy):
    trade = Symbol()
    sizing = FixedCapital()


    def execute(self):
        if self.trade.is_flat:
            self.buy(self.trade)
class Good(Strategy):
    trade = Symbol()
    sizing = FixedCapital()


    def execute(self):
        sma = SMA(20, on=self.trade)
        sd = StdDev(20, on=self.trade)


        # Bollinger bands without numpy — and math.* with no import
        upper = sma.value + 2 * sd.value
        lower = sma.value - 2 * sd.value
        span = math.sqrt(upper - lower)


        if self.trade.close < lower and span > 0 and self.trade.is_flat:
            self.buy(self.trade)