Jesse Strategy Tests

Writing tests for Jesse's backend

When a feature is tied to strategy behavior, write it the way the rest of the suite does: a tiny test function that runs a backtest against a purpose-built test strategy, where the assertions live inside the strategy's lifecycle hooks — not in the test function.

The pattern (canonical example)

1. The test — a one-liner in tests/test_parent_strategy.py that just runs the strategy:

def test_on_close_position():
    single_route_backtest('TestOnClosePosition')

2. The strategy — jesse/strategies/TestOnClosePosition/__init__.py. The class name, the directory name, and the string passed to single_route_backtest must all match:

from jesse.strategies import Strategy
import jesse.helpers as jh
from jesse import utils


class TestOnClosePosition(Strategy):
    def should_long(self):
        return self.price == 10

    def go_long(self):
        if self.price == 10:
            self.buy = 1, self.price

    def on_open_position(self, order):
        self.take_profit = 1, 12  # close the position at 12

    def on_close_position(self, order, closed_trade) -> None:
        assert closed_trade.exit_price == 12
        assert closed_trade.entry_price == 10
        assert closed_trade.qty == 1
        assert closed_trade.type == "long"
        assert closed_trade.timeframe == self.timeframe
        assert closed_trade.exchange == self.exchange
        assert closed_trade.symbol == self.symbol

The assertions run during the backtest, inside on_close_position. If any fail, the backtest raises and the test fails. The test function itself stays assertion-free.

How the price moves (so triggers like self.price == 10 work)

single_route_backtest('Name') defaults to: futures, leverage 1, fee 0, 1m timeframe, up-trend, 100 candles. The up-trend candles have close prices 1, 2, 3, … , 99 (candles_from_close_prices(range(1, 100))). So self.price walks 1 → 99, one step per candle. That's why:

• should_long / go_long fire when self.price == 10 (the 10th candle),
• a take_profit at 12 fills two candles later as price rises through it.

Use trend='down' for descending prices (100 → 11), e.g. to exercise short trades or stop-losses.

single_route_backtest parameters

single_route_backtest(
    strategy_name,            # 'TestXyz' — matches class & directory name
    is_futures_trading=True,
    leverage=1,
    leverage_mode='cross',    # or 'isolated'
    trend='up',               # 'up' -> 1..99, 'down' -> 100..11
    fee=0,                    # e.g. 0.0004 to test fees
    candles_count=100,
    timeframe='1m',
)

For multi-route scenarios use two_routes_backtest(name1, name2, ...) or two_data_routes_backtest(...) from jesse.testing_utils.

More patterns

Asserting engine/exchange state at a chosen candle (before/after)

You don't have to assert on the closed trade. To check engine state — balances, exchange internals — assert inside before() (or after()) gated on a specific price/candle, and reach into the store for the raw objects. This spot example runs a down-trend (trend='down' → prices 100 → 11), opens at 100 with a TP/SL, then verifies the balance settled correctly on a later candle:

def test_proper_balance_handling_in_spot_after_order_cancellation():
    single_route_backtest(
        'TestProperBalanceHanldingInSpotAfterOrderCancellation',
        is_futures_trading=False, trend='down',
    )

from jesse.strategies import Strategy
from jesse import utils
from jesse.store import store


class TestProperBalanceHanldingInSpotAfterOrderCancellation(Strategy):
    def before(self) -> None:
        # after the first trade has fully resolved
        if self.price == 89:
            assert self.balance == 9900
            e = store.exchanges.get_exchange(self.exchange)
            assert e.assets['USDT'] == 9900
            assert e.assets['BTC'] == 0

    def should_long(self):
        return self.price == 100

    def go_long(self):
        entry = self.price
        qty = utils.size_to_qty(1000, entry)
        self.buy = qty, entry

    def on_open_position(self, order):
        self.take_profit = self.position.qty, 110
        self.stop_loss = self.position.qty, 90

Notes:

• is_futures_trading=False selects spot, where the exchange tracks per-asset balances (e.assets['USDT'], e.assets['BTC']) rather than a single wallet.
• store.exchanges.get_exchange(self.exchange) is the standard way to inspect engine state from inside a strategy. store.positions, store.orders, store.closed_trades work the same way.
• Set exits sized to the live position with self.position.qty.

Multi-route tests (two_routes_backtest)

To test cross-route behavior, run two strategies together — one per route — with two_routes_backtest(name1, name2) (BTC-USDT and ETH-USDT, both 1m). Each strategy asserts about its own route. These observer strategies don't trade (should_long returns False); they exist purely to assert. Here each route checks its current_route_index at specific candles via self.index:

def test_current_route_index():
    two_routes_backtest('TestCurrentRouteIndex1', 'TestCurrentRouteIndex2')

class TestCurrentRouteIndex1(Strategy):
    def before(self) -> None:
        if self.index == 0 or self.index == 10:
            assert self.current_route_index == 0

    def should_long(self):
        return False

    def go_long(self):
        pass

    def should_cancel_entry(self):
        return False


class TestCurrentRouteIndex2(Strategy):
    def before(self) -> None:
        if self.index == 0 or self.index == 10:
            assert self.current_route_index == 1
    # ... same no-op should_long / go_long / should_cancel_entry

Notes:

• self.index is the 0-based candle index (vs. self.price, the close price). Use it to gate assertions when the value you're testing isn't price-derived.
• A strategy that only observes still needs should_long/go_long (and usually should_cancel_entry) defined, even as no-ops.
• Use two_data_routes_backtest(...) when routes need extra (non-trading) data timeframes.

Lifecycle hooks where assertions go

Put assertions in whichever hook observes the thing you're testing:

Setting exits: self.take_profit = qty, price and self.stop_loss = qty, price (use a list of (qty, price) tuples for multi-tier exits).

Discovering the available strategy API

The hooks and properties above are the common ones, not the full set. Whenever you need to know what a test strategy can access or do — properties (self.balance, self.position, self.orders, self.trades, self.metrics, self.average_entry_price, self.current_route_index, self.portfolio_value, …), helper methods (self.liquidate(), self.log(), self.get_candles()), or the exact signature of a lifecycle hook — read the Strategy base class at jesse/strategies/Strategy.py. It is the source of truth; don't guess an API, confirm it there first.

Conventions

• Names match: class name == directory name == string passed to the helper.
• Assert inside the strategy, not in the test function, for strategy-driven tests.
• Float comparisons: use round(value, 8) when values aren't exact.
• Fees: pass fee= to the helper (or set config['env']['exchanges'][exchanges.SANDBOX]['fee'] in manual setups).
• Clean up shared state: a test that leaves a closed trade in the global store must call store.reset() at the end — some test files assert an empty store at their start. The single_route_backtest helpers reset config/store on entry via set_up, so back-to-back helper-based tests are already isolated.
• Accounting invariant: for tests about fees/PNL, assert that per-trade closed_trade.pnl equals the real wallet balance change.

When NOT to use the strategy-driven pattern

For low-level engine accounting that's awkward to express through a strategy (exact filled_qty, fee-on-fill, exit_price VWAP under reduce_only caps), prefer a broker-level unit test in tests/test_broker.py: build state with set_up_with_fee / set_up_without_fee, then drive broker.* and order_service.execute_order(...) directly and assert on the order, position, and exchange.wallet_balance. See test_oversized_reduce_only_order_uses_actual_filled_qty.

Running

# activate env and cd into jesse/
cd-jesse

# run the full strategy-test suite
pytest tests/test_parent_strategy.py -q

# run a single test by name
pytest tests/test_parent_strategy.py::test_on_close_position -q

# run all tests across every test file
pytest

# run another specific test file (e.g. broker-level tests)
pytest tests/test_broker.py -q

Use -v instead of -q for verbose output showing each test name as it runs.