Quickstart

תוכן זה אינו זמין עדיין בשפה שלך.

Build, backtest, and read the results of your first RaamCode strategy. Five minutes end to end.

1. The strategy

We’ll write a moving-average crossover — buys when a fast moving average crosses above a slow one, exits when it crosses back below. The classic “golden cross.”

class MACross(Strategy):
    """Golden-cross trend follower."""

    fast = Param(7, min=2, max=50)
    slow = Param(21, min=10, max=200)
    trade = Symbol()
    sizing = FixedCapital()

    def execute(self):
        fast = SMA(self.fast, on=self.trade)
        slow = SMA(self.slow, on=self.trade)

        self.stop_loss(2.0)

        if fast.crossed_above(slow) and self.trade.is_flat:
            self.buy(self.trade, reason="Bullish crossover")

        if fast.crossed_below(slow) and self.trade.is_long:
            self.sell(self.trade, self.trade.position, reason="Bearish crossover")

2. Line by line

Class header

class MACross(Strategy):
    """Golden-cross trend follower."""

Every strategy is a class that inherits from Strategy. The class name is what appears in the strategy catalog. The docstring is the catalog description — keep it short and human.

Parameters

fast = Param(7, min=2, max=50)
slow = Param(21, min=10, max=200)

Each Param() is a tunable value with a default and a valid range. min/max are hard bounds — the platform rejects anything outside them at compile time. These same params drive the optimizer: it sweeps fast from 2–50 and slow from 10–200 to find the configuration with the best fit.

Symbol slot

trade = Symbol()

A Symbol() is a placeholder for “the instrument the subscriber will pick.” When somebody deploys this strategy, they bind a real ticker (SPY, AAPL, TA35) to trade. The strategy itself doesn’t know or care.

Sizing model

sizing = FixedCapital()

How each self.buy() sizes its position. FixedCapital() means “use 100% of starting capital.” Other options: FixedQuantity(n) (always n contracts) or Compounding() (use 100% of current equity, so winners grow your position).

sizing is required — every strategy must declare one.

Per-bar logic

def execute(self):
    fast = SMA(self.fast, on=self.trade)
    slow = SMA(self.slow, on=self.trade)
    ...

execute() runs once per bar after warmup completes. Inside, you declare your indicators at the top — the transpiler hoists them so they receive every bar’s data, not just the ones where your if branch fires.

Exit rule

self.stop_loss(2.0)

A 2% stop loss on every position. Exit rules are function calls, not assignments. They’re checked tick-by-tick, not just on bar boundaries — your stop fires the moment the price touches it.

Entry signal

if fast.crossed_above(slow) and self.trade.is_flat:
    self.buy(self.trade, reason="Bullish crossover")

Two conditions both have to be true: the fast SMA just crossed above the slow one on this bar, and you’re not already in a position. If they are, place a market buy. The first argument is always the symbol slot. The reason string is saved with the order and shows up in your trade log.

Exit signal

if fast.crossed_below(slow) and self.trade.is_long:
    self.sell(self.trade, self.trade.position, reason="Bearish crossover")

Exit verbs (sell, buy_to_cover) require an explicit quantity. self.trade.position is “everything I currently hold.” You can also exit half (self.trade.position / 2) or a fixed number (50).

3. Run it

In Raamtrade:

Open Workspaces → New strategy.
Paste the code above into the editor. Diagnostics light up the moment you stop typing — green means the strategy is ready.
Pick a ticker (try SPY) and a date range (try the last two years).
Click Backtest.

The result appears as an equity curve, a list of every trade, and summary metrics (return, drawdown, Sharpe).

4. Try the optimizer

Once a baseline backtest succeeds, click Optimize. The platform sweeps fast and slow across their min–max ranges and returns the best-performing combinations. Because fast and slow are already Param() declarations, no extra setup is needed.