matitos
9c3bee87ac
FastAPI backend with a single-strategy async engine, paper and Binance brokers, pure-python indicators, a backtester that shares the engine's portfolio math, and a Vue dashboard with a live WebSocket feed. Adds the Gitea Actions CI/CD workflow that deploys to Traefik via docker_default.
143 lines
4.7 KiB
Python
143 lines
4.7 KiB
Python
from __future__ import annotations
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from typing import ClassVar
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import pytest
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from app.backtest import Backtester
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from app.backtest.metrics import max_drawdown_pct, sharpe_ratio
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from app.engine.strategies.base import Strategy, StrategyContext
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from app.schemas import BacktestPointOut, BacktestTradeOut, Candle
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class HoldStrategy(Strategy):
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"""Enter long on the first candle and stay in (closed at end of data)."""
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key: ClassVar[str] = "test_hold"
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name: ClassVar[str] = "Test Hold"
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default_params: ClassVar[dict] = {}
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def on_candle(self, ctx: StrategyContext) -> None:
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if ctx.is_flat:
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ctx.enter_long(size=1.0)
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class NeverStrategy(Strategy):
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key: ClassVar[str] = "test_never"
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name: ClassVar[str] = "Test Never"
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default_params: ClassVar[dict] = {}
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def on_candle(self, ctx: StrategyContext) -> None:
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return None
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class StopStrategy(Strategy):
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key: ClassVar[str] = "test_stop"
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name: ClassVar[str] = "Test Stop"
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default_params: ClassVar[dict] = {}
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def on_candle(self, ctx: StrategyContext) -> None:
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if ctx.is_flat and not getattr(self, "_entered", False):
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self._entered = True
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ctx.enter_long(size=1.0, stop_loss=ctx.price * 0.9)
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def candles_from_closes(closes: list[float]) -> list[Candle]:
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return [Candle(ts=i, open=c, high=c, low=c, close=c, volume=1.0) for i, c in enumerate(closes)]
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def test_hold_backtest_math() -> None:
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backtester = Backtester(fee_bps=0, slippage_bps=0, starting_balance=10_000)
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result = backtester.run(candles_from_closes([100, 110, 120]), HoldStrategy(), "BTC/USDT", "1h")
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assert result.error is None
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assert result.metrics.num_trades == 1
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# 99 units bought at 100 (cash capped at 99%), closed at 120.
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assert result.metrics.final_equity == pytest.approx(11_980.0)
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assert result.trades[0].pnl == pytest.approx(1_980.0)
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assert result.trades[0].return_pct == pytest.approx(20.0)
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assert result.trades[0].reason == "end of data"
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assert len(result.equity_curve) == 3
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def test_no_trades_leaves_balance_untouched() -> None:
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backtester = Backtester(fee_bps=10, slippage_bps=5, starting_balance=5_000)
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result = backtester.run(
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candles_from_closes([100, 101, 102, 103]), NeverStrategy(), "BTC/USDT", "1m"
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)
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assert result.metrics.num_trades == 0
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assert result.metrics.final_equity == pytest.approx(5_000.0)
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assert result.metrics.total_return_pct == pytest.approx(0.0)
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assert result.metrics.max_drawdown_pct == pytest.approx(0.0)
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def test_stop_loss_is_honoured() -> None:
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backtester = Backtester(fee_bps=0, slippage_bps=0, starting_balance=10_000)
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candles = candles_from_closes([100, 100, 80])
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candles[2] = Candle(ts=2, open=100, high=100, low=80, close=80, volume=1.0)
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result = backtester.run(candles, StopStrategy(), "BTC/USDT", "1h")
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assert result.metrics.num_trades == 1
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assert "stop loss" in result.trades[0].reason
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assert result.metrics.final_equity < 10_000
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def test_strategy_error_is_surfaced() -> None:
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class Boom(Strategy):
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key = "test_boom"
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name = "Boom"
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default_params: ClassVar[dict] = {}
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def on_candle(self, ctx: StrategyContext) -> None:
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raise RuntimeError("kaboom")
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result = Backtester().run(candles_from_closes([1, 2, 3]), Boom(), "BTC/USDT", "1m")
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assert result.error is not None and "kaboom" in result.error
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def test_max_drawdown() -> None:
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assert max_drawdown_pct([100, 120, 90, 130]) == pytest.approx(25.0)
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assert max_drawdown_pct([100, 100, 100]) == pytest.approx(0.0)
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def test_sharpe_none_for_unknown_timeframe() -> None:
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assert sharpe_ratio([100, 101, 102, 103], "nonsense") is None
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def _points(equities: list[float]) -> list[BacktestPointOut]:
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return [BacktestPointOut(ts=i, equity=e, buy_hold=e, price=e) for i, e in enumerate(equities)]
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def test_metrics_profit_factor_and_win_rate() -> None:
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from app.backtest.metrics import compute_metrics
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trades = [
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BacktestTradeOut(
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side="long",
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entry_ts=0,
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exit_ts=1,
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entry_price=100,
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exit_price=110,
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qty=1,
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pnl=10,
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return_pct=10,
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reason="x",
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),
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BacktestTradeOut(
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side="long",
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entry_ts=2,
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exit_ts=3,
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entry_price=100,
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exit_price=95,
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qty=1,
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pnl=-5,
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return_pct=-5,
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reason="x",
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),
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]
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metrics = compute_metrics(_points([10_000, 10_010, 10_005]), trades, 10_000, "1h")
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assert metrics.win_rate_pct == pytest.approx(50.0)
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assert metrics.profit_factor == pytest.approx(2.0)
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assert metrics.best_trade_pct == pytest.approx(10.0)
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assert metrics.worst_trade_pct == pytest.approx(-5.0)
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