matitos
9dc00eba95
Trailing stops: strategies can pass trailing_stop_pct to enter_long/enter_short. advance_trailing_stop in app/engine/portfolio.py ratchets the stop with the price (up for longs, down for shorts) and never loosens; the engine persists the ratcheted level and the backtester applies the same logic. Position gains a trailing_stop_pct column (additive migration). momentum exposes it as a param. Parameter sweeps: app/backtest/sweep.py runs a cartesian grid through the same backtester and ranks by return minus max drawdown. POST /api/sweep plus a dashboard Parameter sweep panel.
61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
from __future__ import annotations
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import itertools
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from collections.abc import Sequence
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from dataclasses import dataclass
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from typing import Any
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from app.backtest.backtester import Backtester
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from app.engine.strategies import get_strategy
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from app.engine.strategies.base import Strategy
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from app.schemas import Candle
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@dataclass(slots=True)
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class SweepRow:
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params: dict[str, Any]
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metrics: dict[str, Any]
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def expand_grid(grid: dict[str, Sequence[Any]]) -> list[dict[str, Any]]:
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"""Cartesian product of a param grid into a list of concrete param dicts."""
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if not grid:
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return [{}]
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names = list(grid)
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return [dict(zip(names, values, strict=True)) for values in itertools.product(*grid.values())]
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def _score(metrics: dict[str, Any]) -> float:
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"""Rank by return, penalising drawdown. Simple, transparent, sortable."""
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return float(metrics.get("total_return_pct", 0.0)) - float(metrics.get("max_drawdown_pct", 0.0))
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def run_sweep(
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strategy_key: str,
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base_params: dict[str, Any],
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grid: dict[str, Sequence[Any]],
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candles: Sequence[Candle],
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symbol: str,
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timeframe: str,
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*,
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fee_bps: float = 10.0,
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slippage_bps: float = 5.0,
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starting_balance: float = 10_000.0,
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top_n: int = 20,
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) -> list[SweepRow]:
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"""Backtest every combination in ``grid`` (merged over ``base_params``)."""
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strategy_cls: type[Strategy] = get_strategy(strategy_key)
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backtester = Backtester(
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fee_bps=fee_bps, slippage_bps=slippage_bps, starting_balance=starting_balance
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)
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rows: list[SweepRow] = []
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for params in expand_grid(grid):
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merged = {**base_params, **params}
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strategy = strategy_cls(merged)
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result = backtester.run(candles, strategy, symbol, timeframe)
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rows.append(SweepRow(params=merged, metrics=result.metrics.model_dump()))
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rows.sort(key=lambda row: _score(row.metrics), reverse=True)
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return rows[:top_n]
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