feat: simulate trading fees/slippage in backtest, compute real PnL for real trades
Backtest/walk-forward priced every fill at the exact candle close with zero cost, making reported win rate/profit factor systematically more optimistic than live trading. Added configurable taker-fee + slippage simulation (defaults 0.1%/0.05% per fill) applied to every entry/exit, threaded through walk-forward's grid search and both API endpoints. sync_real_trades() hardcoded pnl=0 for every real trade needing it, silently reporting break-even for real-money trades regardless of actual outcome. Replaced with FIFO lot matching per (user, symbol, exchange), and fixed orders.py to persist the exchange's actual average fill price instead of the (always-None-for-market-orders) requested price, so there's real price data to match against. Also verified (and locked in with regression tests) that Divergence/SMC's pivot-confirmation delay is already causally consistent between live and backtest — no repaint, no look-ahead leak. 187 backend tests pass (+17). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
This commit is contained in:
Le
@@ -11,7 +11,12 @@ from app.database import get_db
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from app.models.candle import Candle
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from app.models.symbol import Symbol
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from app.models.exchange import Exchange
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from app.services.backtest_engine import run_backtest as _run_backtest, MIN_CANDLES
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from app.services.backtest_engine import (
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run_backtest as _run_backtest,
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MIN_CANDLES,
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DEFAULT_TAKER_FEE_PCT,
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DEFAULT_SLIPPAGE_PCT,
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)
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/backtest", tags=["backtest"])
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@@ -76,11 +81,13 @@ async def run_backtest(
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timeframe: str = Query("30m"),
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days: int = Query(7),
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trade_size: float = Query(10.0),
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fee_pct: float = Query(DEFAULT_TAKER_FEE_PCT, ge=0, description="Round-trip-per-fill taker fee, e.g. 0.001 = 0.1%"),
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slippage_pct: float = Query(DEFAULT_SLIPPAGE_PCT, ge=0, description="Adverse slippage per fill, e.g. 0.0005 = 0.05%"),
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db: AsyncSession = Depends(get_db),
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):
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"""Run backtest and return JSON results."""
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result = await _run_backtest(
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db, symbol, exchange, timeframe, days, Decimal(str(trade_size))
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db, symbol, exchange, timeframe, days, Decimal(str(trade_size)), fee_pct, slippage_pct,
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)
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if "error" in result:
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raise HTTPException(status_code=400, detail=result["error"])
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@@ -94,7 +101,9 @@ async def run_backtest_post(
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timeframe: str = Query("30m"),
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days: int = Query(7),
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trade_size: float = Query(10.0),
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fee_pct: float = Query(DEFAULT_TAKER_FEE_PCT, ge=0),
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slippage_pct: float = Query(DEFAULT_SLIPPAGE_PCT, ge=0),
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db: AsyncSession = Depends(get_db),
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):
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"""Alias for GET /backtest/run — supports POST method."""
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return await run_backtest(symbol, exchange, timeframe, days, trade_size, db)
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return await run_backtest(symbol, exchange, timeframe, days, trade_size, fee_pct, slippage_pct, db)
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@@ -96,7 +96,11 @@ async def place_order(
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current_user.username, exchange_name, req.symbol, req.side, req.amount,
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)
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# Persist to real_trades
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# Persist to real_trades. For market orders `req.price` is None (no
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# limit price was ever set) — the actual execution price only comes
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# back from the exchange as `order.average`/`order.price`. Without
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# it, this row would have no price at all and PnL could never be
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# computed for it later (see sync_real_trades' FIFO PnL matching).
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real_trade = RealTrade(
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user_id=current_user.id,
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exchange=exchange_name,
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@@ -104,7 +108,7 @@ async def place_order(
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side=req.side,
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order_type=req.order_type,
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amount=req.amount,
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price=req.price,
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price=order.average or order.price or req.price,
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filled_amount=order.filled,
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status=order.status,
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order_id=order.order_id,
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@@ -21,6 +21,7 @@ from app.database import get_db
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from app.core.deps import get_current_user
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from app.models.user import User as UserModel
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from app.services.walk_forward import run_walk_forward
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from app.services.backtest_engine import DEFAULT_TAKER_FEE_PCT, DEFAULT_SLIPPAGE_PCT
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/walk-forward", tags=["walk_forward"])
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@@ -35,6 +36,8 @@ async def run(
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train_days: int = Query(270, ge=30, description="Train window size per fold, in days"),
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test_days: int = Query(90, ge=14, description="Held-out test window size per fold, in days"),
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trade_size: float = Query(10.0),
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fee_pct: float = Query(DEFAULT_TAKER_FEE_PCT, ge=0, description="Round-trip-per-fill taker fee, e.g. 0.001 = 0.1%"),
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slippage_pct: float = Query(DEFAULT_SLIPPAGE_PCT, ge=0, description="Adverse slippage per fill, e.g. 0.0005 = 0.05%"),
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db: AsyncSession = Depends(get_db),
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current_user: UserModel = Depends(get_current_user),
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):
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@@ -43,6 +46,7 @@ async def run(
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db, symbol, exchange, timeframe,
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total_days=total_days, train_days=train_days, test_days=test_days,
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trade_size=Decimal(str(trade_size)),
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fee_pct=fee_pct, slippage_pct=slippage_pct,
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)
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if "error" in result:
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raise HTTPException(status_code=400, detail=result["error"])
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@@ -37,6 +37,77 @@ from app.services.signal_scoring import (
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# Min candles for warmup: BB(20) + RSI(14) + some room = 30
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MIN_CANDLES = 30
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# Trading-cost defaults — previously the simulator priced every fill at the
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# exact candle close with zero cost, which made every backtest/walk-forward
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# report systematically more profitable than live trading could ever be.
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# Taker fee: 0.1% per fill (0.2% round-trip) matches typical crypto spot
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# taker fees without VIP/token discounts (Binance, MEXC). Slippage: 0.05%
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# per fill is a conservative estimate for liquid majors on market orders —
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# thinner symbols would see more in reality, but this at least stops
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# results from assuming frictionless fills.
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DEFAULT_TAKER_FEE_PCT = 0.001
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DEFAULT_SLIPPAGE_PCT = 0.0005
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def _fill_price(mark_price: float, direction: str, is_entry: bool, slippage_pct: float) -> float:
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"""Simulate a realistic market-order fill price — slippage always moves
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the price against the trader, never in their favor.
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LONG entry / SHORT exit both buy (fill above mark price).
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SHORT entry / LONG exit both sell (fill below mark price).
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"""
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buying = (direction == "LONG") == is_entry
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return mark_price * (1 + slippage_pct) if buying else mark_price * (1 - slippage_pct)
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def _open_position(direction: str, mark_price: float, timestamp, index: int,
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trade_size: Decimal, entry_signal: str,
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fee_pct: float, slippage_pct: float) -> dict:
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"""Build a new open position dict, applying entry slippage/fees.
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`quantity` is sized off the slipped fill price (not the raw mark price)
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so that `entry_price * quantity == trade_size`, matching how a real
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market order spends a fixed quote-currency amount and receives fewer
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units when the fill is worse than the observed price.
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"""
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entry_price = _fill_price(mark_price, direction, True, slippage_pct)
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quantity = float(trade_size) / entry_price
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entry_fee = entry_price * quantity * fee_pct
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return {
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"direction": direction, "entry_price": entry_price,
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"entry_time": timestamp, "quantity": quantity,
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"entry_signal": entry_signal, "entry_index": index, "status": "OPEN",
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"entry_fee": entry_fee,
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}
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def _close_position(position: dict, mark_price: float, timestamp, exit_reason: str,
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fee_pct: float, slippage_pct: float) -> dict:
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"""Close an open position in-place, applying exit slippage/fees.
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`pnl` is the NET result (gross price movement minus round-trip fees) —
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every caller downstream (_compute_stats, walk_forward's fold scoring)
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reads `pnl` directly, so this is the only place cost needs to be
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subtracted for it to flow through the whole system.
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"""
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direction = position["direction"]
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entry_price = position["entry_price"]
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quantity = position["quantity"]
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exit_price = _fill_price(mark_price, direction, False, slippage_pct)
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exit_fee = exit_price * quantity * fee_pct
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if direction == "LONG":
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gross_pnl = (exit_price - entry_price) * quantity
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else:
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gross_pnl = (entry_price - exit_price) * quantity
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entry_fee = position.get("entry_fee", 0.0)
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fees = entry_fee + exit_fee
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position.update({
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"exit_price": exit_price, "exit_time": timestamp,
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"pnl": gross_pnl - fees, "gross_pnl": gross_pnl, "fees": fees,
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"status": "CLOSED", "exit_reason": exit_reason,
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})
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return position
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# Bounded trailing-window sizes used when replaying indicators per candle.
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# Every consumer in signal_scoring.py only ever reads the last 1-2 elements
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# of these arrays except the BB squeeze check (lookback=10), so windows
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@@ -433,12 +504,20 @@ def _simulate_from_scores(
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strong_threshold: float = 4.0,
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signal_threshold: float = 1.0,
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max_hold_candles: int = 48,
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fee_pct: float = DEFAULT_TAKER_FEE_PCT,
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slippage_pct: float = DEFAULT_SLIPPAGE_PCT,
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) -> tuple[list[dict], list[dict]]:
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"""Cheap half of simulation: turn a precomputed score series into
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signals + trades for one choice of thresholds.
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See `_compute_scores_series` for the expensive half — run once,
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reused across every threshold combination a grid search tries.
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Every fill (entry and exit) goes through `_fill_price`/`_open_position`/
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`_close_position` so simulated trades pay the same round-trip fee and
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adverse slippage a live market order would, instead of pricing fills at
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the exact candle close for free — see DEFAULT_TAKER_FEE_PCT/
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DEFAULT_SLIPPAGE_PCT above.
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"""
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all_signals: list[dict] = []
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trades: list[dict] = []
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@@ -466,77 +545,43 @@ def _simulate_from_scores(
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if signal_type in (STRONG_BUY, BUY):
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if current_position and current_position["direction"] == "SHORT":
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if signal_type == STRONG_BUY:
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entry_price = current_position["entry_price"]
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qty = current_position["quantity"]
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pnl = (entry_price - latest_close) * qty
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current_position.update({
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"exit_price": latest_close, "exit_time": timestamp,
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"pnl": pnl, "status": "CLOSED", "exit_reason": "REVERSAL",
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})
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_close_position(current_position, latest_close, timestamp, "REVERSAL", fee_pct, slippage_pct)
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trades.append(current_position)
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current_position = None
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else:
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continue
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if not current_position:
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qty = float(trade_size) / latest_close
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current_position = {
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"direction": "LONG", "entry_price": latest_close,
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"entry_time": timestamp, "quantity": qty,
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"entry_signal": signal_type, "entry_index": i, "status": "OPEN",
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}
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current_position = _open_position(
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"LONG", latest_close, timestamp, i, trade_size, signal_type, fee_pct, slippage_pct,
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)
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elif signal_type in (STRONG_SELL, SELL):
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if current_position and current_position["direction"] == "LONG":
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if signal_type == STRONG_SELL:
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entry_price = current_position["entry_price"]
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qty = current_position["quantity"]
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pnl = (latest_close - entry_price) * qty
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current_position.update({
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"exit_price": latest_close, "exit_time": timestamp,
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"pnl": pnl, "status": "CLOSED", "exit_reason": "REVERSAL",
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})
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_close_position(current_position, latest_close, timestamp, "REVERSAL", fee_pct, slippage_pct)
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trades.append(current_position)
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current_position = None
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else:
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continue
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if not current_position:
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qty = float(trade_size) / latest_close
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current_position = {
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"direction": "SHORT", "entry_price": latest_close,
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"entry_time": timestamp, "quantity": qty,
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"entry_signal": signal_type, "entry_index": i, "status": "OPEN",
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}
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current_position = _open_position(
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"SHORT", latest_close, timestamp, i, trade_size, signal_type, fee_pct, slippage_pct,
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)
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if current_position and current_position["status"] == "OPEN":
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hold = i - current_position["entry_index"]
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if hold >= max_hold_candles:
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entry_price = current_position["entry_price"]
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qty = current_position["quantity"]
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if current_position["direction"] == "LONG":
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pnl = (latest_close - entry_price) * qty
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else:
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pnl = (entry_price - latest_close) * qty
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current_position.update({
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"exit_price": latest_close, "exit_time": timestamp,
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"pnl": pnl, "status": "CLOSED", "exit_reason": "TIME_LIMIT",
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})
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_close_position(current_position, latest_close, timestamp, "TIME_LIMIT", fee_pct, slippage_pct)
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trades.append(current_position)
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current_position = None
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# Close final position
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if current_position and current_position["status"] == "OPEN":
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last_close = float(candles[-1].close)
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entry_price = current_position["entry_price"]
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qty = current_position["quantity"]
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if current_position["direction"] == "LONG":
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pnl = (last_close - entry_price) * qty
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else:
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pnl = (entry_price - last_close) * qty
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current_position.update({
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"exit_price": last_close,
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"exit_time": candles[-1].timestamp.isoformat(),
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"pnl": pnl, "status": "CLOSED", "exit_reason": "END_OF_DATA",
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})
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_close_position(
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current_position, last_close, candles[-1].timestamp.isoformat(),
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"END_OF_DATA", fee_pct, slippage_pct,
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)
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trades.append(current_position)
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return all_signals, trades
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@@ -550,6 +595,8 @@ def _simulate_trades(
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signal_threshold: float = 1.0,
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max_hold_candles: int = 48,
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active_from_index: int = MIN_CANDLES,
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fee_pct: float = DEFAULT_TAKER_FEE_PCT,
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slippage_pct: float = DEFAULT_SLIPPAGE_PCT,
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) -> tuple[list[dict], list[dict]]:
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"""Convenience wrapper: compute scores then simulate with one set of
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thresholds. Callers trying many threshold combinations against the
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@@ -558,7 +605,10 @@ def _simulate_trades(
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combination instead — see walk_forward.py.
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"""
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scores_series = _compute_scores_series(candles, precomputed, active_from_index)
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return _simulate_from_scores(candles, scores_series, trade_size, strong_threshold, signal_threshold, max_hold_candles)
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return _simulate_from_scores(
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candles, scores_series, trade_size, strong_threshold, signal_threshold,
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max_hold_candles, fee_pct, slippage_pct,
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)
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def _compute_stats(all_signals: list[dict], trades: list[dict]) -> dict:
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@@ -578,6 +628,7 @@ def _compute_stats(all_signals: list[dict], trades: list[dict]) -> dict:
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profit_factor = round(abs(gross_profit / gross_loss), 2) if gross_loss != 0 else None
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avg_win = round(gross_profit / len(winning_trades), 2) if winning_trades else None
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avg_loss = round(gross_loss / len(losing_trades), 2) if losing_trades else None
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total_fees = round(sum(t.get("fees", 0) for t in closed_trades), 2)
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best_trade = max(closed_trades, key=lambda t: t.get("pnl", 0)) if closed_trades else None
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worst_trade = min(closed_trades, key=lambda t: t.get("pnl", 0)) if closed_trades else None
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@@ -595,6 +646,7 @@ def _compute_stats(all_signals: list[dict], trades: list[dict]) -> dict:
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"profit_factor": profit_factor,
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"avg_win": avg_win,
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"avg_loss": avg_loss,
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"total_fees": total_fees,
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"best_trade": {
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"direction": best_trade.get("direction"),
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"entry_price": round(best_trade["entry_price"], 4),
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@@ -622,6 +674,8 @@ async def run_backtest(
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timeframe: str = "30m",
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days: int = 7,
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trade_size: Decimal = Decimal("10"),
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fee_pct: float = DEFAULT_TAKER_FEE_PCT,
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slippage_pct: float = DEFAULT_SLIPPAGE_PCT,
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) -> dict:
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"""Run a single backtest over the last `days` days and return structured results."""
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db_symbol = await _fetch_symbol(db, symbol, exchange)
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@@ -642,7 +696,9 @@ async def run_backtest(
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return {"error": f"No candle data found for {symbol} on {timeframe}. The exchange may not support this pair."}
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precomputed = _precompute_indicators(candles, timeframe)
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all_signals, trades = _simulate_trades(candles, precomputed, trade_size)
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all_signals, trades = _simulate_trades(
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candles, precomputed, trade_size, fee_pct=fee_pct, slippage_pct=slippage_pct,
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)
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stats = _compute_stats(all_signals, trades)
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return {
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@@ -8,6 +8,7 @@ from __future__ import annotations
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import json
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import logging
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from collections import defaultdict
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from datetime import datetime, timezone, timedelta
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from decimal import Decimal
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from sqlalchemy import and_, desc, select
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@@ -351,55 +352,143 @@ async def execute_signal_trade(
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# ═══════════════════════════════════════════════════════════
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# Real Trade Sync — closes stale real trades
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# Real Trade Sync — closes stale real trades, computes real PnL
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# ═══════════════════════════════════════════════════════════
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async def sync_real_trades() -> None:
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"""Sync real trades: close stale ones, calculate PnL for closed ones.
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async def _recompute_realized_pnl(db: AsyncSession) -> int:
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"""Recompute realized PnL for every filled real trade via FIFO lot
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matching, grouped by (user_id, symbol, exchange).
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Called periodically (every 5 min) by the scheduler.
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Fixes: real trades were never being closed or having PnL calculated.
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`RealTrade` rows represent individual order fills (one buy or one
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sell), not paired open/close positions like `HypotheticalTrade` — so a
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single row's PnL can't be read off itself. This walks each group's
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fills in chronological order, matching each buy against any open SHORT
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lots first (then opening a LONG lot with anything left over), and each
|
||||
sell against open LONG lots first (then opening a SHORT lot) — the
|
||||
realized PnL from whatever portion closes an existing lot is assigned
|
||||
to that fill's row. A fill that only opens a new position (nothing to
|
||||
close yet) has no realized PnL and is left at pnl=None until a later
|
||||
fill closes it.
|
||||
|
||||
Recomputes from the full history every time rather than accumulating
|
||||
incrementally, which is safe to rerun (idempotent) — the trade volume
|
||||
this endpoint supports (manual/auto real trading through one connected
|
||||
exchange per user, not HFT) makes a full recompute cheap enough to run
|
||||
on every periodic sync.
|
||||
|
||||
Rows with no recorded price (e.g. legacy market-order fills persisted
|
||||
before `orders.py` started storing `order.average`) can't be matched
|
||||
and are skipped — they neither consume nor produce a lot, so a FIFO
|
||||
walk that includes them would silently misattribute PnL to whichever
|
||||
fill happens to match next.
|
||||
"""
|
||||
result = await db.execute(
|
||||
select(RealTrade)
|
||||
.where(RealTrade.filled_amount > 0, RealTrade.price.isnot(None))
|
||||
.order_by(RealTrade.user_id, RealTrade.symbol, RealTrade.exchange, RealTrade.created_at.asc())
|
||||
)
|
||||
trades = result.scalars().all()
|
||||
|
||||
groups: dict[tuple, list[RealTrade]] = defaultdict(list)
|
||||
for t in trades:
|
||||
groups[(t.user_id, t.symbol, t.exchange)].append(t)
|
||||
|
||||
updated = 0
|
||||
for _key, group in groups.items():
|
||||
long_lots: list[list] = [] # each lot: [remaining_qty: Decimal, entry_price: Decimal]
|
||||
short_lots: list[list] = []
|
||||
|
||||
for t in group:
|
||||
side = (t.side or "").lower()
|
||||
qty = Decimal(t.filled_amount)
|
||||
price = Decimal(t.price)
|
||||
if side not in ("buy", "sell") or qty <= 0 or price <= 0:
|
||||
continue
|
||||
|
||||
opposing = short_lots if side == "buy" else long_lots
|
||||
same_side_lots = long_lots if side == "buy" else short_lots
|
||||
|
||||
remaining = qty
|
||||
realized = Decimal("0")
|
||||
matched_cost = Decimal("0")
|
||||
while remaining > 0 and opposing:
|
||||
lot = opposing[0]
|
||||
matched = min(remaining, lot[0])
|
||||
if side == "buy":
|
||||
realized += (lot[1] - price) * matched # closing a short: profit if price fell
|
||||
else:
|
||||
realized += (price - lot[1]) * matched # closing a long: profit if price rose
|
||||
matched_cost += lot[1] * matched
|
||||
lot[0] -= matched
|
||||
remaining -= matched
|
||||
if lot[0] <= 0:
|
||||
opposing.pop(0)
|
||||
|
||||
if remaining > 0:
|
||||
same_side_lots.append([remaining, price])
|
||||
|
||||
if matched_cost > 0:
|
||||
pnl_percent = (realized / matched_cost) * Decimal("100")
|
||||
if t.pnl != realized or t.pnl_percent != pnl_percent:
|
||||
t.pnl = realized
|
||||
t.pnl_percent = pnl_percent
|
||||
updated += 1
|
||||
elif t.pnl is not None:
|
||||
# Pure opening fill — nothing existed yet to realize against.
|
||||
t.pnl = None
|
||||
t.pnl_percent = None
|
||||
updated += 1
|
||||
|
||||
return updated
|
||||
|
||||
|
||||
async def sync_real_trades() -> None:
|
||||
"""Sync real trades: close stale open orders, then (re)compute realized
|
||||
PnL for every filled trade via FIFO matching (see
|
||||
`_recompute_realized_pnl`).
|
||||
|
||||
Called periodically (every 5 min) by the scheduler. Previously this
|
||||
force-set pnl=0 for every stale-closed AND every already-closed-but-
|
||||
unpriced trade regardless of whether it actually filled — silently
|
||||
reporting "break-even" for real-money trades that may have made or
|
||||
lost real money. Now pnl is only ever 0/None for orders that genuinely
|
||||
never filled; any order with a nonzero fill gets a real FIFO-matched
|
||||
PnL once a later trade closes its position.
|
||||
"""
|
||||
async with async_session_factory() as db:
|
||||
# 1. Fetch open real trades
|
||||
# 1. Force-close real orders stuck "open" too long (exchange likely
|
||||
# never filled them). Only the STATUS changes here for anything
|
||||
# with a partial fill — its PnL (if any) comes from the FIFO
|
||||
# recompute below once/if a later trade closes it out.
|
||||
result = await db.execute(
|
||||
select(RealTrade).where(RealTrade.status == "open")
|
||||
)
|
||||
open_trades = result.scalars().all()
|
||||
|
||||
if not open_trades:
|
||||
return
|
||||
|
||||
now = datetime.now(timezone.utc)
|
||||
|
||||
stale_closed = 0
|
||||
for trade in open_trades:
|
||||
hold_duration = now - trade.created_at
|
||||
if hold_duration > timedelta(hours=24):
|
||||
trade.status = "closed"
|
||||
trade.closed_at = now
|
||||
trade.pnl = Decimal("0")
|
||||
trade.pnl_percent = Decimal("0")
|
||||
if not trade.filled_amount or trade.filled_amount <= 0:
|
||||
# Never filled at all — there is genuinely no PnL.
|
||||
trade.pnl = Decimal("0")
|
||||
trade.pnl_percent = Decimal("0")
|
||||
stale_closed += 1
|
||||
logger.info(
|
||||
"🔒 Real trade #%d CLOSED (time limit 24h): %s %s",
|
||||
trade.id, trade.side, trade.symbol,
|
||||
)
|
||||
|
||||
# 2. Calculate PnL for closed trades missing it
|
||||
result2 = await db.execute(
|
||||
select(RealTrade).where(
|
||||
and_(RealTrade.status.in_(["closed", "filled"]),
|
||||
RealTrade.pnl.is_(None))
|
||||
)
|
||||
)
|
||||
closed_no_pnl = result2.scalars().all()
|
||||
|
||||
for trade in closed_no_pnl:
|
||||
trade.pnl = Decimal("0")
|
||||
trade.pnl_percent = Decimal("0")
|
||||
# 2. Recompute realized PnL for every filled trade (not just ones
|
||||
# missing it — a new closing fill can retroactively realize PnL on
|
||||
# an earlier opening fill that already had pnl=None).
|
||||
updated = await _recompute_realized_pnl(db)
|
||||
|
||||
await db.commit()
|
||||
if open_trades or closed_no_pnl:
|
||||
if stale_closed or updated:
|
||||
logger.info(
|
||||
"Real trade sync: %d open checked, %d closed PnL fixed",
|
||||
len(open_trades), len(closed_no_pnl),
|
||||
"Real trade sync: %d stale trades closed, %d PnL rows recomputed",
|
||||
stale_closed, updated,
|
||||
)
|
||||
|
||||
@@ -26,6 +26,8 @@ from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
||||
from app.services.backtest_engine import (
|
||||
MIN_CANDLES,
|
||||
DEFAULT_TAKER_FEE_PCT,
|
||||
DEFAULT_SLIPPAGE_PCT,
|
||||
_fetch_symbol,
|
||||
_fetch_candles,
|
||||
_precompute_indicators,
|
||||
@@ -138,7 +140,7 @@ async def _prepare_window(
|
||||
return candles, precomputed, active_from_index
|
||||
|
||||
|
||||
def _run_combo(candles, scores_series, trade_size, params):
|
||||
def _run_combo(candles, scores_series, trade_size, params, fee_pct, slippage_pct):
|
||||
"""Apply one parameter combination to an already-computed score series
|
||||
(see `_compute_scores_series`) — cheap, no indicator recomputation."""
|
||||
return _simulate_from_scores(
|
||||
@@ -146,6 +148,8 @@ def _run_combo(candles, scores_series, trade_size, params):
|
||||
strong_threshold=params["strong_threshold"],
|
||||
signal_threshold=params["signal_threshold"],
|
||||
max_hold_candles=int(params["max_hold_candles"]),
|
||||
fee_pct=fee_pct,
|
||||
slippage_pct=slippage_pct,
|
||||
)
|
||||
|
||||
|
||||
@@ -154,12 +158,20 @@ def _grid_search(
|
||||
scores_series: list[dict],
|
||||
param_grid: dict[str, list[float]],
|
||||
trade_size: Decimal,
|
||||
fee_pct: float = DEFAULT_TAKER_FEE_PCT,
|
||||
slippage_pct: float = DEFAULT_SLIPPAGE_PCT,
|
||||
) -> tuple[dict[str, float], float, dict]:
|
||||
"""Try every combination in param_grid against one precomputed score
|
||||
series, return the one that scores best on the train window by
|
||||
`_fold_score`. The 13-algorithm scoring itself already happened once
|
||||
to produce `scores_series` — this loop only replays cheap threshold
|
||||
comparisons and trade bookkeeping per combination."""
|
||||
comparisons and trade bookkeeping per combination.
|
||||
|
||||
Fees/slippage are applied here too (not just the final OOS report) so
|
||||
the grid search picks parameters that hold up after trading costs,
|
||||
instead of favoring high-frequency combos that only look good when
|
||||
fills are free.
|
||||
"""
|
||||
keys = list(param_grid.keys())
|
||||
best_params: dict[str, float] | None = None
|
||||
best_score = float("-inf")
|
||||
@@ -167,7 +179,7 @@ def _grid_search(
|
||||
|
||||
for combo in product(*(param_grid[k] for k in keys)):
|
||||
params = dict(zip(keys, combo))
|
||||
all_signals, trades = _run_combo(candles, scores_series, trade_size, params)
|
||||
all_signals, trades = _run_combo(candles, scores_series, trade_size, params, fee_pct, slippage_pct)
|
||||
closed_trades = [t for t in trades if t.get("status") == "CLOSED"]
|
||||
score = _fold_score(closed_trades)
|
||||
if score > best_score:
|
||||
@@ -179,7 +191,7 @@ def _grid_search(
|
||||
# Every combo scored -inf (too few trades) — still report the
|
||||
# grid's first combination so the fold has *something* to show.
|
||||
best_params = {k: param_grid[k][0] for k in keys}
|
||||
all_signals, trades = _run_combo(candles, scores_series, trade_size, best_params)
|
||||
all_signals, trades = _run_combo(candles, scores_series, trade_size, best_params, fee_pct, slippage_pct)
|
||||
best_stats = _compute_stats(all_signals, trades)
|
||||
|
||||
return best_params, best_score, best_stats
|
||||
@@ -209,6 +221,8 @@ async def run_walk_forward(
|
||||
test_days: int = 90,
|
||||
trade_size: Decimal = Decimal("10"),
|
||||
param_grid: dict[str, list[float]] | None = None,
|
||||
fee_pct: float = DEFAULT_TAKER_FEE_PCT,
|
||||
slippage_pct: float = DEFAULT_SLIPPAGE_PCT,
|
||||
) -> dict:
|
||||
"""Run a full walk-forward analysis: optimize params per fold on the
|
||||
train window, evaluate out-of-sample on the test window, then stitch
|
||||
@@ -234,7 +248,7 @@ async def run_walk_forward(
|
||||
train_scores = _compute_scores_series(train_candles, train_precomputed, train_active_from)
|
||||
|
||||
best_params, _train_score, train_stats = _grid_search(
|
||||
train_candles, train_scores, param_grid, trade_size,
|
||||
train_candles, train_scores, param_grid, trade_size, fee_pct, slippage_pct,
|
||||
)
|
||||
|
||||
test_window = await _prepare_window(db, db_symbol.id, timeframe, spec["test_start"], spec["test_end"])
|
||||
@@ -243,7 +257,7 @@ async def run_walk_forward(
|
||||
test_candles, test_precomputed, test_active_from = test_window
|
||||
test_scores = _compute_scores_series(test_candles, test_precomputed, test_active_from)
|
||||
|
||||
test_signals, test_trades = _run_combo(test_candles, test_scores, trade_size, best_params)
|
||||
test_signals, test_trades = _run_combo(test_candles, test_scores, trade_size, best_params, fee_pct, slippage_pct)
|
||||
test_stats = _compute_stats(test_signals, test_trades)
|
||||
stitched_oos_trades.extend(t for t in test_trades if t.get("status") == "CLOSED")
|
||||
|
||||
@@ -265,6 +279,7 @@ async def run_walk_forward(
|
||||
"win_rate": test_stats["trades"]["win_rate"],
|
||||
"total_pnl": test_stats["trades"]["total_pnl"],
|
||||
"profit_factor": test_stats["trades"]["profit_factor"],
|
||||
"total_fees": test_stats["trades"]["total_fees"],
|
||||
},
|
||||
})
|
||||
|
||||
@@ -282,6 +297,7 @@ async def run_walk_forward(
|
||||
oos_total_pnl = sum(t.get("pnl", 0) for t in stitched_oos_trades)
|
||||
oos_win_rate = round(len(oos_wins) / len(stitched_oos_trades) * 100, 1) if stitched_oos_trades else 0.0
|
||||
oos_profit_factor = round(abs(oos_gross_profit / oos_gross_loss), 2) if oos_gross_loss != 0 else None
|
||||
oos_total_fees = round(sum(t.get("fees", 0) for t in stitched_oos_trades), 2)
|
||||
|
||||
equity_curve = [0.0]
|
||||
running = 0.0
|
||||
@@ -297,6 +313,8 @@ async def run_walk_forward(
|
||||
"train_days": train_days,
|
||||
"test_days": test_days,
|
||||
"param_grid": param_grid,
|
||||
"fee_pct": fee_pct,
|
||||
"slippage_pct": slippage_pct,
|
||||
"folds": fold_results,
|
||||
"out_of_sample_summary": {
|
||||
"trades": len(stitched_oos_trades),
|
||||
@@ -305,6 +323,7 @@ async def run_walk_forward(
|
||||
"win_rate": oos_win_rate,
|
||||
"total_pnl": round(oos_total_pnl, 2),
|
||||
"profit_factor": oos_profit_factor,
|
||||
"total_fees": oos_total_fees,
|
||||
"max_drawdown_pct": _max_drawdown_pct(equity_curve),
|
||||
"equity_curve": equity_curve,
|
||||
},
|
||||
|
||||
Reference in New Issue
Block a user