Strategy Shape

A strategy tells the engine when to enter and exit positions. A strategy document has two parts:

• Condition expressions — boolean strings that reference indicator output columns, for example "rsi < 30".
• Indicator list — which indicators to compute, and with what parameters.

The engine evaluates the expressions against the computed indicators on each bar to produce the signal series. How that signal becomes a fill is a separate concern — see Signal vs execution.

Minimal example

from backtest360 import Strategy

strat = Strategy(
    name="rsi_mean_reversion",
    long_entry="rsi < 30",
    long_exit="rsi > 70",
    indicators=[Strategy.indicator("rsi", period=14)],
)

The expression "rsi < 30" references the output column of the rsi indicator — rsi is the default column name when only one RSI is declared.

Indicator refs

When you need the same indicator with different parameters, give each a unique ref and reference it by that name in your expressions:

strat = Strategy(
    name="rsi_dual",
    long_entry="rsi_fast < 20",
    long_exit="rsi_slow > 80",
    indicators=[
        Strategy.indicator("rsi", ref="rsi_fast", period=5),
        Strategy.indicator("rsi", ref="rsi_slow", period=21),
    ],
)

Crossovers via transform indicators

A transform indicator takes other indicators as input. cross_above fires when upstream[0] crosses above upstream[1]; cross_below is its mirror. Declare the two source indicators, then a transform that consumes them:

strat = Strategy(
    name="sma_crossover",
    long_entry="x_above",
    long_exit="x_below",
    indicators=[
        Strategy.indicator("sma", ref="sma_10", period=10),
        Strategy.indicator("sma", ref="sma_50", period=50),
        Strategy.indicator(
            "cross_above", ref="x_above",
            kind="transform", upstream=["sma_10", "sma_50"],
        ),
        Strategy.indicator(
            "cross_below", ref="x_below",
            kind="transform", upstream=["sma_10", "sma_50"],
        ),
    ],
)

Short selling

Add short_entry and short_exit expressions to trade both directions:

strat = Strategy(
    name="rsi_long_short",
    long_entry="rsi < 30",
    long_exit="rsi > 50",
    short_entry="rsi > 70",
    short_exit="rsi < 50",
    indicators=[Strategy.indicator("rsi", period=14)],
)

Pre-built templates

Each of these class methods returns a ready-to-run Strategy:

Strategy.rsi_threshold_long()   # RSI(14) oversold entry / overbought exit
Strategy.rsi_mean_reversion()   # RSI(14) recovery-zone entry / neutral exit
Strategy.ma_crossover()         # SMA(10) x SMA(50) crossover
Strategy.momentum_6m_long()     # 6-month momentum, long when positive

They are a good starting point and a template for your own strategies.

Browse the indicator library

list_indicators() returns every available indicator and its parameter schema:

for ind in client.list_indicators():
    print(ind["name"], ind.get("params", {}))

Validate a strategy against the registry before running it with client.validate_strategy(strat); see Your first backtest. The full indicator and metric catalogs are also in the API Reference.