dennisthiessenandClaude Opus 5.5
4876f9ffdd
The comparison plotted both books in dollars and SPY on a hidden rescaled axis, stretched so its biggest move matched the books' biggest move. SPY's height therefore said nothing about which was ahead, and the stat cells showed R next to SPY's percent, which do not compare. Everything is now percent of the shadow book's equity: - shadow: actual P&L over equity at window start (start equity plus P&L realized before a pinned performance_start_date) - your picks: re-sized at the shadow book's rule (risk_pct, 20% notional cap), so the gap between books is selection only - SPY: buy & hold on the same axis A strategy-vs-SPY verdict measures SPY from the shadow book's first trade, so manual trades opening earlier no longer charge the strategy for SPY's run-up before it existed. R stats stay underneath for book-vs-book reads. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
1055 lines
39 KiB
Python
1055 lines
39 KiB
Python
"""Paper-trading service: take, mark-to-market, and close simulated trades."""
|
||
|
||
from __future__ import annotations
|
||
|
||
import bisect
|
||
import logging
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from datetime import date, datetime, timezone
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from sqlalchemy import and_, func, select
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||
from sqlalchemy.ext.asyncio import AsyncSession
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from app.exceptions import NotFoundError, ValidationError
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from app.models.ohlcv import OHLCVRecord
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from app.models.paper_trade import PaperTrade
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from app.models.ticker import Ticker
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from app.services import benchmark_service, settings_store
|
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from app.services.outcome_service import (
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OUTCOME_AMBIGUOUS,
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OUTCOME_STOP_HIT,
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OUTCOME_TARGET_HIT,
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Bar,
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evaluate_setup_against_bars,
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)
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from app.services.trade_policy import MANUAL_BOOK, SHADOW_BOOK, get_reentry_gate_locks
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logger = logging.getLogger(__name__)
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# Exit policy for OPEN paper trades (auto-close). Production defaults to the
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# July 2026 promoted strategy: initial stop + 3x ATR trailing stop, with a max
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# 30-trading-day hold. The older percent trail and target/stop modes remain
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# selectable for comparison. Stored in SystemSetting so it's tunable and visible.
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KEY_EXIT_MODE = "paper_exit_mode"
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KEY_TRAILING_PCT = "paper_trailing_pct"
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KEY_ATR_MULTIPLIER = "paper_atr_multiplier"
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KEY_HOLD_DAYS = "paper_hold_days"
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DEFAULT_EXIT_MODE = "atr_trailing"
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DEFAULT_TRAILING_PCT = 12.0
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DEFAULT_ATR_MULTIPLIER = 3.0
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DEFAULT_HOLD_DAYS = 30
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_VALID_EXIT_MODES = ("time", "trailing", "atr_trailing", "target")
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async def get_exit_policy(db: AsyncSession) -> dict:
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"""Active auto-exit policy:
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{'mode': 'time'|'trailing'|'atr_trailing'|'target', ...}."""
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mode = (await settings_store.get_value(db, KEY_EXIT_MODE, DEFAULT_EXIT_MODE)).strip().lower()
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if mode not in _VALID_EXIT_MODES:
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mode = DEFAULT_EXIT_MODE
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raw = await settings_store.get_value(db, KEY_TRAILING_PCT, str(DEFAULT_TRAILING_PCT))
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try:
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pct = float(raw)
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except (TypeError, ValueError):
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pct = DEFAULT_TRAILING_PCT
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pct = max(0.5, min(90.0, pct))
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raw_atr = await settings_store.get_value(db, KEY_ATR_MULTIPLIER, str(DEFAULT_ATR_MULTIPLIER))
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try:
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atr_multiplier = float(raw_atr)
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except (TypeError, ValueError):
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atr_multiplier = DEFAULT_ATR_MULTIPLIER
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atr_multiplier = max(0.5, min(10.0, atr_multiplier))
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raw_days = await settings_store.get_value(db, KEY_HOLD_DAYS, str(DEFAULT_HOLD_DAYS))
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try:
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hold_days = int(float(raw_days))
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except (TypeError, ValueError):
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hold_days = DEFAULT_HOLD_DAYS
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hold_days = max(2, min(250, hold_days))
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return {
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"mode": mode,
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"trailing_pct": pct,
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"atr_multiplier": atr_multiplier,
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"hold_days": hold_days,
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}
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async def set_exit_policy(
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db: AsyncSession,
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*,
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mode: str | None = None,
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trailing_pct: float | None = None,
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atr_multiplier: float | None = None,
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hold_days: int | None = None,
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) -> dict:
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"""Persist the auto-exit policy (admin). Validates inputs."""
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if mode is not None:
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mode = mode.strip().lower()
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if mode not in _VALID_EXIT_MODES:
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raise ValidationError("mode must be 'time', 'trailing', 'atr_trailing' or 'target'")
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await settings_store.upsert_setting(db, KEY_EXIT_MODE, mode)
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if trailing_pct is not None:
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if not 0.5 <= float(trailing_pct) <= 90.0:
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raise ValidationError("trailing_pct must be between 0.5 and 90")
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await settings_store.upsert_setting(db, KEY_TRAILING_PCT, str(float(trailing_pct)))
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if atr_multiplier is not None:
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if not 0.5 <= float(atr_multiplier) <= 10.0:
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raise ValidationError("atr_multiplier must be between 0.5 and 10")
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await settings_store.upsert_setting(db, KEY_ATR_MULTIPLIER, str(float(atr_multiplier)))
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if hold_days is not None:
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if not 2 <= int(hold_days) <= 250:
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raise ValidationError("hold_days must be between 2 and 250")
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await settings_store.upsert_setting(db, KEY_HOLD_DAYS, str(int(hold_days)))
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await db.commit()
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return await get_exit_policy(db)
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|
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async def _get_ticker(db: AsyncSession, symbol: str) -> Ticker:
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normalised = symbol.strip().upper()
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result = await db.execute(select(Ticker).where(Ticker.symbol == normalised))
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ticker = result.scalar_one_or_none()
|
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if ticker is None:
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raise NotFoundError(f"Ticker not found: {normalised}")
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return ticker
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async def _latest_closes(db: AsyncSession, ticker_ids: set[int]) -> dict[int, float]:
|
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"""Latest stored close per ticker."""
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if not ticker_ids:
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return {}
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latest = (
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select(OHLCVRecord.ticker_id, func.max(OHLCVRecord.date).label("md"))
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.where(OHLCVRecord.ticker_id.in_(ticker_ids))
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.group_by(OHLCVRecord.ticker_id)
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.subquery()
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)
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stmt = select(OHLCVRecord.ticker_id, OHLCVRecord.close).join(
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latest,
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and_(
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OHLCVRecord.ticker_id == latest.c.ticker_id,
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OHLCVRecord.date == latest.c.md,
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),
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)
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result = await db.execute(stmt)
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return {tid: float(close) for tid, close in result.all()}
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async def _max_high_after(db: AsyncSession, ticker_id: int, since: date) -> float | None:
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"""Highest high strictly after ``since`` — the running peak for a trailing stop."""
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result = await db.execute(
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select(func.max(OHLCVRecord.high)).where(
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OHLCVRecord.ticker_id == ticker_id, OHLCVRecord.date > since
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)
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)
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v = result.scalar()
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return float(v) if v is not None else None
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def _time_close(
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direction: str, init_stop: float, hold_days: int, rows: list[tuple]
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) -> tuple[float, date, str] | None:
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"""Walk post-entry ``rows`` of (date, open, high, low, close); close at the
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initial stop if hit (a gap through it fills at the open, matching the
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backtest's fill model), else at the ``hold_days``-th bar's close ('time').
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None while neither has happened."""
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long = direction == "long"
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for i, (d, open_, high, low, close) in enumerate(rows):
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if (low <= init_stop) if long else (high >= init_stop):
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fill = min(init_stop, open_) if long else max(init_stop, open_)
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return float(fill), d, "stop"
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if i + 1 >= hold_days:
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return float(close), d, "time"
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return None
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def _trailing_close(
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direction: str, entry: float, init_stop: float, trail_frac: float, bars: list[Bar]
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) -> tuple[float, date, str] | None:
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"""Walk post-entry bars; return (price, date, reason) when the trailing or initial
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stop is hit, else None. The stop only ratchets up: max(init_stop, peak*(1-trail))
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for a long. reason = 'trailing' once it's above the initial stop, else 'stop'."""
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long = direction == "long"
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peak = entry
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for b in bars:
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if long:
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level = max(init_stop, peak * (1 - trail_frac))
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if b.low <= level:
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return level, b.date, ("trailing" if level > init_stop else "stop")
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if b.high > peak:
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peak = b.high
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else:
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level = min(init_stop, peak * (1 + trail_frac))
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if b.high >= level:
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return level, b.date, ("trailing" if level < init_stop else "stop")
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||
if b.low < peak:
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peak = b.low
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return None
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||
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||
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||
def _atr_series_from_rows(rows: list[tuple], period: int = 14) -> list[float | None]:
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||
"""ATR at each index i, equal to ``compute_atr(rows[: i + 1])["atr"]`` but
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computed in a single O(n) Wilder pass instead of re-smoothing the whole
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prefix per bar. None where there are fewer than ``period + 1`` bars or the
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rounded ATR is non-positive. ``period`` mirrors ``compute_atr``'s default;
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keep them in sync.
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Exactness: ``compute_atr`` keeps its running ATR unrounded through the
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recurrence and rounds only at return, so storing ``round(running, 4)`` at
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each index reproduces its per-prefix value bit-for-bit.
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"""
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n = len(rows)
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out: list[float | None] = [None] * n
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if n < period + 1:
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return out
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tr = [0.0] * n
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for i in range(1, n):
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high, low, prev_close = float(rows[i][2]), float(rows[i][3]), float(rows[i - 1][4])
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tr[i] = max(high - low, abs(high - prev_close), abs(low - prev_close))
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running = sum(tr[1 : period + 1]) / period
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rounded = round(running, 4)
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out[period] = rounded if rounded > 0 else None
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for j in range(period + 1, n):
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running = (running * (period - 1) + tr[j]) / period
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rounded = round(running, 4)
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out[j] = rounded if rounded > 0 else None
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return out
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||
|
||
|
||
def _atr_trailing_level(
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direction: str,
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entry: float,
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||
init_stop: float,
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atr_multiplier: float,
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rows: list[tuple],
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||
opened_on: date,
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||
) -> float:
|
||
"""Current ATR trailing stop level after all available post-entry closes."""
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long = direction == "long"
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stop = float(init_stop)
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anchor = float(entry)
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atr_by_idx = _atr_series_from_rows(rows)
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for idx, (d, _, _, _, close) in enumerate(rows):
|
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if d <= opened_on:
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continue
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close = float(close)
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atr = atr_by_idx[idx]
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if long:
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||
anchor = max(anchor, close)
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if atr is not None:
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next_stop = anchor - atr_multiplier * atr
|
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if next_stop < close:
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||
stop = max(stop, next_stop)
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else:
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anchor = min(anchor, close)
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if atr is not None:
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next_stop = anchor + atr_multiplier * atr
|
||
if next_stop > close:
|
||
stop = min(stop, next_stop)
|
||
return stop
|
||
|
||
|
||
def _atr_trailing_close(
|
||
direction: str,
|
||
entry: float,
|
||
init_stop: float,
|
||
atr_multiplier: float,
|
||
hold_days: int,
|
||
rows: list[tuple],
|
||
opened_on: date,
|
||
) -> tuple[float, date, str] | None:
|
||
"""Initial stop + ATR trailing stop + max hold, matching the portfolio sim.
|
||
|
||
Stop checks happen before the same day's trailing update, so a newly ratcheted
|
||
stop becomes active on the next bar. Gaps through the stop fill at the open.
|
||
"""
|
||
long = direction == "long"
|
||
stop = float(init_stop)
|
||
anchor = float(entry)
|
||
bars_held = 0
|
||
atr_by_idx = _atr_series_from_rows(rows)
|
||
for idx, (d, open_, high, low, close) in enumerate(rows):
|
||
if d <= opened_on:
|
||
continue
|
||
open_ = float(open_)
|
||
high = float(high)
|
||
low = float(low)
|
||
close = float(close)
|
||
bars_held += 1
|
||
|
||
if long:
|
||
if low <= stop:
|
||
reason = "trailing" if stop > init_stop + 1e-9 else "stop"
|
||
return min(stop, open_), d, reason
|
||
else:
|
||
if high >= stop:
|
||
reason = "trailing" if stop < init_stop - 1e-9 else "stop"
|
||
return max(stop, open_), d, reason
|
||
|
||
if bars_held >= hold_days:
|
||
return close, d, "time"
|
||
|
||
atr = atr_by_idx[idx]
|
||
if long:
|
||
anchor = max(anchor, close)
|
||
if atr is not None:
|
||
next_stop = anchor - atr_multiplier * atr
|
||
if next_stop < close:
|
||
stop = max(stop, next_stop)
|
||
else:
|
||
anchor = min(anchor, close)
|
||
if atr is not None:
|
||
next_stop = anchor + atr_multiplier * atr
|
||
if next_stop > close:
|
||
stop = min(stop, next_stop)
|
||
|
||
return None
|
||
|
||
|
||
async def create_trade(
|
||
db: AsyncSession,
|
||
user_id: int,
|
||
*,
|
||
symbol: str,
|
||
direction: str,
|
||
entry_price: float,
|
||
shares: float,
|
||
stop_loss: float,
|
||
target: float,
|
||
) -> PaperTrade:
|
||
direction = direction.strip().lower()
|
||
if direction not in ("long", "short"):
|
||
raise ValidationError("direction must be 'long' or 'short'")
|
||
if shares <= 0 or entry_price <= 0:
|
||
raise ValidationError("shares and entry_price must be positive")
|
||
|
||
ticker = await _get_ticker(db, symbol)
|
||
if ticker.id in await get_reentry_gate_locks(db):
|
||
raise ValidationError(
|
||
f"{ticker.symbol} requires a post-stop gate reset before re-entry"
|
||
)
|
||
trade = PaperTrade(
|
||
user_id=user_id,
|
||
ticker_id=ticker.id,
|
||
direction=direction,
|
||
entry_price=entry_price,
|
||
shares=shares,
|
||
stop_loss=stop_loss,
|
||
target=target,
|
||
status="open",
|
||
opened_at=datetime.now(timezone.utc),
|
||
# Near-close cutover era — Track Record must not mix with morning-scan
|
||
# fills or future broker-routed fills when comparing to backtests.
|
||
fill_mode="near_close",
|
||
)
|
||
db.add(trade)
|
||
await db.commit()
|
||
await db.refresh(trade)
|
||
return trade
|
||
|
||
|
||
def _to_dict(
|
||
trade: PaperTrade,
|
||
symbol: str,
|
||
current_price: float | None,
|
||
benchmark_closes: dict[date, float] | None = None,
|
||
trailing: tuple[float, float | None] | None = None,
|
||
holding_sessions: tuple[int, int] | None = None,
|
||
) -> dict:
|
||
# For open trades, mark to market; for closed, the realized exit price.
|
||
ref = current_price if trade.status == "open" else trade.close_price
|
||
|
||
# Alpha = trade return − benchmark (SPY) return over the same holding period.
|
||
benchmark_return = None
|
||
alpha_pct = None
|
||
alpha_usd = None
|
||
if ref is not None and trade.entry_price and benchmark_closes:
|
||
sign = 1.0 if trade.direction == "long" else -1.0
|
||
trade_return = (ref - trade.entry_price) / trade.entry_price * 100.0 * sign
|
||
as_of = (
|
||
trade.closed_at.date()
|
||
if trade.status == "closed" and trade.closed_at is not None
|
||
else date.today()
|
||
)
|
||
benchmark_return = benchmark_service.benchmark_return_pct(
|
||
benchmark_closes, trade.opened_at.date(), as_of
|
||
)
|
||
if benchmark_return is not None:
|
||
alpha_pct = trade_return - benchmark_return
|
||
alpha_usd = alpha_pct / 100.0 * trade.entry_price * trade.shares
|
||
|
||
return {
|
||
"id": trade.id,
|
||
"symbol": symbol,
|
||
"direction": trade.direction,
|
||
"entry_price": trade.entry_price,
|
||
"shares": trade.shares,
|
||
"stop_loss": trade.stop_loss,
|
||
"target": trade.target,
|
||
"status": trade.status,
|
||
"opened_at": trade.opened_at,
|
||
"close_price": trade.close_price,
|
||
"closed_at": trade.closed_at,
|
||
"current_price": ref,
|
||
"benchmark_return_pct": benchmark_return,
|
||
"alpha_pct": alpha_pct,
|
||
"alpha_usd": alpha_usd,
|
||
"close_reason": trade.close_reason,
|
||
"fill_mode": trade.fill_mode,
|
||
"trailing_stop": trailing[0] if trailing else None,
|
||
"trailing_distance_pct": trailing[1] if trailing else None,
|
||
"sessions_held": holding_sessions[0] if holding_sessions else None,
|
||
"sessions_remaining": holding_sessions[1] if holding_sessions else None,
|
||
}
|
||
|
||
|
||
async def list_trades(
|
||
db: AsyncSession,
|
||
user_id: int | None = None,
|
||
status: str | None = None,
|
||
book: str | None = MANUAL_BOOK,
|
||
) -> list[dict]:
|
||
"""Trades for the UI. Defaults to the discretionary book.
|
||
|
||
Shadow trades are attached to a user row for FK reasons only — they are not
|
||
that person's decisions. Listing them alongside manual trades would mix two
|
||
different books in one P&L and let the autonomous record be edited by hand.
|
||
Pass ``book=None`` to deliberately span both.
|
||
"""
|
||
stmt = (
|
||
select(PaperTrade, Ticker.symbol)
|
||
.join(Ticker, PaperTrade.ticker_id == Ticker.id)
|
||
)
|
||
if user_id is not None: # None → all users (single-user app; used by the digest)
|
||
stmt = stmt.where(PaperTrade.user_id == user_id)
|
||
if status is not None:
|
||
stmt = stmt.where(PaperTrade.status == status)
|
||
if book is not None:
|
||
stmt = stmt.where(PaperTrade.book == book)
|
||
stmt = stmt.order_by(PaperTrade.opened_at.desc())
|
||
|
||
rows = (await db.execute(stmt)).all()
|
||
open_ids = {t.ticker_id for t, _ in rows if t.status == "open"}
|
||
prices = await _latest_closes(db, open_ids)
|
||
|
||
# Benchmark closes for alpha — populated by the daily/benchmark job. Empty until
|
||
# that runs once, in which case alpha is simply left unset (a read path never
|
||
# makes a provider call).
|
||
benchmark_closes = await benchmark_service.load_benchmark_closes(db)
|
||
|
||
# Current trailing-stop level + distance for open trades (when a trailing
|
||
# policy is active).
|
||
policy = await get_exit_policy(db)
|
||
holding_sessions: dict[int, tuple[int, int]] = {}
|
||
if policy["mode"] in ("time", "atr_trailing"):
|
||
hold_days = int(policy["hold_days"])
|
||
open_trades = [trade for trade, _ in rows if trade.status == "open"]
|
||
if open_trades:
|
||
ticker_ids = {trade.ticker_id for trade in open_trades}
|
||
earliest_opened = min(trade.opened_at.date() for trade in open_trades)
|
||
session_rows = (
|
||
await db.execute(
|
||
select(OHLCVRecord.ticker_id, OHLCVRecord.date)
|
||
.where(
|
||
OHLCVRecord.ticker_id.in_(ticker_ids),
|
||
OHLCVRecord.date > earliest_opened,
|
||
)
|
||
.order_by(OHLCVRecord.ticker_id, OHLCVRecord.date)
|
||
)
|
||
).all()
|
||
dates_by_ticker: dict[int, list[date]] = {}
|
||
for ticker_id, session_date in session_rows:
|
||
dates_by_ticker.setdefault(int(ticker_id), []).append(session_date)
|
||
for trade in open_trades:
|
||
dates = dates_by_ticker.get(trade.ticker_id, [])
|
||
held = len(dates) - bisect.bisect_right(
|
||
dates, trade.opened_at.date()
|
||
)
|
||
# Do not clamp: a policy shortened below the current holding
|
||
# period must remain visible as overdue until the exit pass runs.
|
||
holding_sessions[trade.id] = (held, hold_days - held)
|
||
|
||
trailing_info: dict[int, tuple[float, float | None]] = {}
|
||
if policy["mode"] == "trailing":
|
||
trail_frac = policy["trailing_pct"] / 100.0
|
||
for t, _ in rows:
|
||
if t.status != "open":
|
||
continue
|
||
max_high = await _max_high_after(db, t.ticker_id, t.opened_at.date())
|
||
peak = max(t.entry_price, max_high) if max_high is not None else t.entry_price
|
||
long = t.direction == "long"
|
||
level = (
|
||
max(t.stop_loss, peak * (1 - trail_frac))
|
||
if long
|
||
else min(t.stop_loss, peak * (1 + trail_frac))
|
||
)
|
||
cur = prices.get(t.ticker_id)
|
||
dist = None
|
||
if cur:
|
||
dist = ((cur - level) / cur * 100.0) if long else ((level - cur) / cur * 100.0)
|
||
trailing_info[t.id] = (level, dist)
|
||
elif policy["mode"] == "atr_trailing":
|
||
atr_multiplier = float(policy["atr_multiplier"])
|
||
for t, _ in rows:
|
||
if t.status != "open":
|
||
continue
|
||
bars_result = await db.execute(
|
||
select(
|
||
OHLCVRecord.date, OHLCVRecord.open, OHLCVRecord.high,
|
||
OHLCVRecord.low, OHLCVRecord.close,
|
||
)
|
||
.where(OHLCVRecord.ticker_id == t.ticker_id)
|
||
.order_by(OHLCVRecord.date.asc())
|
||
)
|
||
level = _atr_trailing_level(
|
||
t.direction,
|
||
t.entry_price,
|
||
t.stop_loss,
|
||
atr_multiplier,
|
||
bars_result.all(),
|
||
t.opened_at.date(),
|
||
)
|
||
cur = prices.get(t.ticker_id)
|
||
dist = None
|
||
if cur:
|
||
long = t.direction == "long"
|
||
dist = ((cur - level) / cur * 100.0) if long else ((level - cur) / cur * 100.0)
|
||
trailing_info[t.id] = (level, dist)
|
||
|
||
return [
|
||
_to_dict(
|
||
t,
|
||
sym,
|
||
prices.get(t.ticker_id),
|
||
benchmark_closes,
|
||
trailing_info.get(t.id),
|
||
holding_sessions.get(t.id),
|
||
)
|
||
for t, sym in rows
|
||
]
|
||
|
||
|
||
async def close_trade(
|
||
db: AsyncSession,
|
||
user_id: int,
|
||
trade_id: int,
|
||
close_price: float | None = None,
|
||
) -> PaperTrade:
|
||
result = await db.execute(
|
||
select(PaperTrade).where(
|
||
PaperTrade.id == trade_id,
|
||
PaperTrade.user_id == user_id,
|
||
)
|
||
)
|
||
trade = result.scalar_one_or_none()
|
||
if trade is None:
|
||
raise NotFoundError(f"Paper trade not found: {trade_id}")
|
||
if trade.book == SHADOW_BOOK:
|
||
# The shadow book's value is that no human touched it. A hand-closed
|
||
# position would make its record something other than what the strategy
|
||
# would have done; it exits only via the automatic exit policy.
|
||
raise ValidationError(
|
||
"Shadow book trades are closed by the exit policy, not by hand"
|
||
)
|
||
if trade.status == "closed":
|
||
raise ValidationError("Trade is already closed")
|
||
|
||
if close_price is None:
|
||
prices = await _latest_closes(db, {trade.ticker_id})
|
||
close_price = prices.get(trade.ticker_id)
|
||
if close_price is None:
|
||
raise ValidationError("No current price available to close at; supply close_price")
|
||
|
||
trade.status = "closed"
|
||
trade.close_price = float(close_price)
|
||
trade.close_reason = "manual"
|
||
trade.closed_at = datetime.now(timezone.utc)
|
||
await db.commit()
|
||
await db.refresh(trade)
|
||
return trade
|
||
|
||
|
||
async def resolve_open_trades(db: AsyncSession) -> int:
|
||
"""Auto-close open trades per the active exit policy, from the daily bars.
|
||
|
||
Walks the bars after each trade's open. 'atr_trailing' closes at the initial
|
||
stop, a 3x-ATR-style trailing stop, or the hold_days-th close; 'time' closes
|
||
at the initial stop or the hold_days-th close; 'trailing' uses the legacy
|
||
percent trail; 'target' uses the setup's target or stop. Trades that have hit
|
||
nothing stay open. Returns the count closed.
|
||
"""
|
||
result = await db.execute(select(PaperTrade).where(PaperTrade.status == "open"))
|
||
open_trades = list(result.scalars().all())
|
||
if not open_trades:
|
||
return 0
|
||
|
||
policy = await get_exit_policy(db)
|
||
mode = policy["mode"]
|
||
trail_frac = policy["trailing_pct"] / 100.0
|
||
atr_multiplier = float(policy["atr_multiplier"])
|
||
hold_days = policy["hold_days"]
|
||
|
||
closed = 0
|
||
for trade in open_trades:
|
||
bars_result = await db.execute(
|
||
select(
|
||
OHLCVRecord.date, OHLCVRecord.open, OHLCVRecord.high,
|
||
OHLCVRecord.low, OHLCVRecord.close,
|
||
)
|
||
.where(
|
||
OHLCVRecord.ticker_id == trade.ticker_id,
|
||
OHLCVRecord.date > trade.opened_at.date(),
|
||
)
|
||
.order_by(OHLCVRecord.date.asc())
|
||
)
|
||
post_rows = bars_result.all()
|
||
bars = [Bar(date=d, high=h, low=lo) for d, _, h, lo, _ in post_rows]
|
||
if not bars:
|
||
continue
|
||
|
||
if mode == "time":
|
||
hit = _time_close(trade.direction, trade.stop_loss, hold_days, post_rows)
|
||
if hit is None:
|
||
continue # neither the stop nor the hold horizon reached yet
|
||
close_price, close_date, reason = hit
|
||
elif mode == "trailing":
|
||
hit = _trailing_close(trade.direction, trade.entry_price, trade.stop_loss, trail_frac, bars)
|
||
if hit is None:
|
||
continue # neither the trailing nor the initial stop reached yet
|
||
close_price, close_date, reason = hit
|
||
elif mode == "atr_trailing":
|
||
all_bars_result = await db.execute(
|
||
select(
|
||
OHLCVRecord.date, OHLCVRecord.open, OHLCVRecord.high,
|
||
OHLCVRecord.low, OHLCVRecord.close,
|
||
)
|
||
.where(OHLCVRecord.ticker_id == trade.ticker_id)
|
||
.order_by(OHLCVRecord.date.asc())
|
||
)
|
||
hit = _atr_trailing_close(
|
||
trade.direction,
|
||
trade.entry_price,
|
||
trade.stop_loss,
|
||
atr_multiplier,
|
||
hold_days,
|
||
all_bars_result.all(),
|
||
trade.opened_at.date(),
|
||
)
|
||
if hit is None:
|
||
continue
|
||
close_price, close_date, reason = hit
|
||
else:
|
||
# max_bars beyond the data so a still-open trade returns undecided (not "expired").
|
||
outcome, outcome_date = evaluate_setup_against_bars(
|
||
trade.direction, trade.stop_loss, trade.target, bars, max_bars=len(bars) + 1
|
||
)
|
||
if outcome == OUTCOME_TARGET_HIT:
|
||
close_price, close_date, reason = trade.target, outcome_date, "target"
|
||
elif outcome in (OUTCOME_STOP_HIT, OUTCOME_AMBIGUOUS):
|
||
close_price, close_date, reason = trade.stop_loss, outcome_date, "stop"
|
||
else:
|
||
continue
|
||
|
||
trade.status = "closed"
|
||
trade.close_price = float(close_price)
|
||
trade.close_reason = reason
|
||
trade.closed_at = datetime.combine(close_date, datetime.min.time(), tzinfo=timezone.utc)
|
||
closed += 1
|
||
|
||
if closed:
|
||
await db.commit()
|
||
return closed
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Equity curve — the paper book's cumulative P&L vs the same dollars in SPY.
|
||
|
||
|
||
def _value_on_or_before(
|
||
dates_sorted: list[date], closes: dict[date, float], target: date
|
||
) -> float | None:
|
||
"""Close on the nearest trading day at or before ``target`` (None if before history)."""
|
||
idx = bisect.bisect_right(dates_sorted, target) - 1
|
||
return closes[dates_sorted[idx]] if idx >= 0 else None
|
||
|
||
|
||
def build_equity_curve(
|
||
trades: list,
|
||
ticker_closes: dict[int, dict[date, float]],
|
||
benchmark_closes: dict[date, float],
|
||
) -> list[dict]:
|
||
"""Daily cumulative P&L of the paper book vs a benchmark counterfactual.
|
||
|
||
For every benchmark trading day since the first trade opened:
|
||
|
||
book_pnl = Σ realized P&L of trades closed by then
|
||
+ Σ mark-to-market P&L of trades still open (ticker close
|
||
on/before that day)
|
||
benchmark_pnl = Σ per trade: the SAME cost basis (entry x shares) riding
|
||
the benchmark over the SAME window (open → close/now).
|
||
Long-benchmark regardless of trade direction — the
|
||
question is "what if this money had just sat in SPY".
|
||
|
||
Pure function so the math is unit-testable; trades are duck-typed
|
||
(ticker_id, direction, entry_price, shares, status, opened_at, closed_at,
|
||
close_price). Trades opened before the stored benchmark history contribute
|
||
to book_pnl but not to benchmark_pnl (no baseline close to measure from).
|
||
"""
|
||
if not trades or not benchmark_closes:
|
||
return []
|
||
first = min(t.opened_at.date() for t in trades)
|
||
bench_dates = sorted(benchmark_closes)
|
||
days = [d for d in bench_dates if d >= first]
|
||
if not days:
|
||
return []
|
||
ticker_dates_sorted = {tid: sorted(c) for tid, c in ticker_closes.items()}
|
||
|
||
out: list[dict] = []
|
||
for d in days:
|
||
book = 0.0
|
||
bench = 0.0
|
||
any_priced = False
|
||
for t in trades:
|
||
opened = t.opened_at.date()
|
||
if opened > d:
|
||
continue
|
||
closed_on = (
|
||
t.closed_at.date()
|
||
if (t.status == "closed" and t.closed_at is not None)
|
||
else None
|
||
)
|
||
window_end = min(d, closed_on) if closed_on is not None else d
|
||
|
||
if closed_on is not None and closed_on <= d and t.close_price is not None:
|
||
ref = float(t.close_price)
|
||
else:
|
||
closes = ticker_closes.get(t.ticker_id) or {}
|
||
ref_val = _value_on_or_before(
|
||
ticker_dates_sorted.get(t.ticker_id) or [], closes, d
|
||
)
|
||
if ref_val is None:
|
||
continue
|
||
ref = ref_val
|
||
per_share = (
|
||
ref - t.entry_price if t.direction == "long" else t.entry_price - ref
|
||
)
|
||
book += per_share * t.shares
|
||
any_priced = True
|
||
|
||
s0 = _value_on_or_before(bench_dates, benchmark_closes, opened)
|
||
s1 = _value_on_or_before(bench_dates, benchmark_closes, window_end)
|
||
if s0 and s1:
|
||
bench += (t.entry_price * t.shares) * (s1 - s0) / s0
|
||
if any_priced:
|
||
out.append(
|
||
{
|
||
"date": d.isoformat(),
|
||
"book_pnl": round(book, 2),
|
||
"benchmark_pnl": round(bench, 2),
|
||
}
|
||
)
|
||
return out
|
||
|
||
|
||
KEY_PERFORMANCE_START = "performance_start_date"
|
||
|
||
|
||
async def get_performance_start(db: AsyncSession) -> date | None:
|
||
"""Date the performance view starts from, or None for 'all history'.
|
||
|
||
The strategy has been revised repeatedly, so early trades were taken under
|
||
rules that no longer exist. Pinning a start date keeps the comparison inside
|
||
one regime instead of averaging across configurations that were replaced.
|
||
"""
|
||
raw = await settings_store.get_value(db, KEY_PERFORMANCE_START, "")
|
||
if not raw or not str(raw).strip():
|
||
return None
|
||
try:
|
||
return date.fromisoformat(str(raw).strip())
|
||
except ValueError:
|
||
logger.warning("invalid %s: %r", KEY_PERFORMANCE_START, raw)
|
||
return None
|
||
|
||
|
||
def trade_r_multiple(trade, mark: float | None) -> float | None:
|
||
"""Result in R — profit measured in units of the trade's own initial risk.
|
||
|
||
R is the only sizing-independent yardstick available here: the shadow book
|
||
sizes at a fixed 1% of equity while manual trades were sized by hand, so
|
||
currency P&L cannot compare them. Open trades are marked to ``mark``.
|
||
"""
|
||
risk_per_share = abs(trade.entry_price - trade.stop_loss)
|
||
if risk_per_share <= 0:
|
||
return None
|
||
exit_price = trade.close_price if trade.status == "closed" else mark
|
||
if exit_price is None:
|
||
return None
|
||
per_share = (
|
||
exit_price - trade.entry_price
|
||
if trade.direction == "long"
|
||
else trade.entry_price - exit_price
|
||
)
|
||
return per_share / risk_per_share
|
||
|
||
|
||
def book_stats(trades: list, marks: dict[int, float]) -> dict:
|
||
"""Sizing-independent summary of one book: counts, win rate, R-multiples."""
|
||
rs = [
|
||
r
|
||
for r in (trade_r_multiple(t, marks.get(t.ticker_id)) for t in trades)
|
||
if r is not None
|
||
]
|
||
closed = [t for t in trades if t.status == "closed"]
|
||
wins = [r for r in rs if r > 0]
|
||
return {
|
||
"trades": len(trades),
|
||
"closed": len(closed),
|
||
"open": len(trades) - len(closed),
|
||
"win_rate": round(100.0 * len(wins) / len(rs), 1) if rs else None,
|
||
"total_r": round(sum(rs), 2) if rs else 0.0,
|
||
"avg_r": round(sum(rs) / len(rs), 3) if rs else None,
|
||
}
|
||
|
||
|
||
async def equity_curve(db: AsyncSession, user_id: int) -> list[dict]:
|
||
"""Equity-curve series for a user's paper book (empty without benchmark data)."""
|
||
trades = (
|
||
(await db.execute(select(PaperTrade).where(PaperTrade.user_id == user_id)))
|
||
.scalars()
|
||
.all()
|
||
)
|
||
if not trades:
|
||
return []
|
||
benchmark_closes = await benchmark_service.load_benchmark_closes(db)
|
||
if not benchmark_closes:
|
||
return []
|
||
first = min(t.opened_at.date() for t in trades)
|
||
ticker_ids = {t.ticker_id for t in trades}
|
||
rows = await db.execute(
|
||
select(OHLCVRecord.ticker_id, OHLCVRecord.date, OHLCVRecord.close).where(
|
||
OHLCVRecord.ticker_id.in_(ticker_ids),
|
||
OHLCVRecord.date >= first,
|
||
)
|
||
)
|
||
ticker_closes: dict[int, dict[date, float]] = {}
|
||
for tid, day, close in rows.all():
|
||
ticker_closes.setdefault(tid, {})[day] = float(close)
|
||
return build_equity_curve(list(trades), ticker_closes, benchmark_closes)
|
||
|
||
|
||
def _cumulative_per_share(
|
||
trades: list, ticker_closes: dict, days: list[date], weight
|
||
) -> list[float]:
|
||
"""Σ per-share P&L x ``weight(trade)`` on each day, realized + mark-to-market."""
|
||
sorted_dates = {tid: sorted(c) for tid, c in ticker_closes.items()}
|
||
out: list[float] = []
|
||
for d in days:
|
||
total = 0.0
|
||
for t in trades:
|
||
if t.opened_at.date() > d:
|
||
continue
|
||
closed_on = (
|
||
t.closed_at.date()
|
||
if (t.status == "closed" and t.closed_at is not None)
|
||
else None
|
||
)
|
||
if closed_on is not None and closed_on <= d and t.close_price is not None:
|
||
ref = float(t.close_price)
|
||
else:
|
||
ref = _value_on_or_before(
|
||
sorted_dates.get(t.ticker_id) or [],
|
||
ticker_closes.get(t.ticker_id) or {},
|
||
d,
|
||
)
|
||
if ref is None:
|
||
continue
|
||
per_share = (
|
||
ref - t.entry_price if t.direction == "long" else t.entry_price - ref
|
||
)
|
||
total += per_share * weight(t)
|
||
out.append(total)
|
||
return out
|
||
|
||
|
||
def _cumulative_pnl(trades: list, ticker_closes: dict, days: list[date]) -> list[float]:
|
||
"""Cumulative realized + mark-to-market P&L of one book on each day."""
|
||
return [
|
||
round(v, 2)
|
||
for v in _cumulative_per_share(trades, ticker_closes, days, lambda t: t.shares)
|
||
]
|
||
|
||
|
||
def strategy_sized_fraction(trade, risk_pct: float, notional_cap: float) -> float:
|
||
"""Shares per unit of equity the shadow book's sizing rule would buy.
|
||
|
||
``risk_pct`` of equity at risk to the stop, capped at ``notional_cap`` of
|
||
equity in notional — the same rule as ``shadow_book_service.position_shares``
|
||
minus the cash limit. Re-sizing a book this way leaves only *selection* to
|
||
differ, and expresses its result as a percent of equity like SPY's.
|
||
"""
|
||
risk_per_share = abs(trade.entry_price - trade.stop_loss)
|
||
if risk_per_share <= 0 or trade.entry_price <= 0:
|
||
return 0.0
|
||
return min(risk_pct / 100.0 / risk_per_share, notional_cap / trade.entry_price)
|
||
|
||
|
||
def _cumulative_sized_pct(
|
||
trades: list,
|
||
ticker_closes: dict,
|
||
days: list[date],
|
||
risk_pct: float,
|
||
notional_cap: float,
|
||
) -> list[float]:
|
||
"""Cumulative return of one book, in % of equity, at the shadow book's sizing."""
|
||
series = _cumulative_per_share(
|
||
trades,
|
||
ticker_closes,
|
||
days,
|
||
lambda t: strategy_sized_fraction(t, risk_pct, notional_cap),
|
||
)
|
||
return [round(100.0 * v, 2) for v in series]
|
||
|
||
|
||
async def _shadow_equity_base(db: AsyncSession, start_equity: float, first: date) -> float:
|
||
"""Shadow equity when the window opens: start equity + P&L realized before it.
|
||
|
||
Trades closed before a pinned ``performance_start_date`` are outside the
|
||
window, but they moved the equity the book sizes from — so the window's
|
||
percent return is measured against that equity, not the original stake.
|
||
"""
|
||
rows = await db.execute(
|
||
select(PaperTrade).where(
|
||
PaperTrade.book == SHADOW_BOOK,
|
||
PaperTrade.status == "closed",
|
||
PaperTrade.close_price.is_not(None),
|
||
func.date(PaperTrade.closed_at) < first,
|
||
)
|
||
)
|
||
realized = 0.0
|
||
for t in rows.scalars():
|
||
per_share = (
|
||
t.close_price - t.entry_price
|
||
if t.direction == "long"
|
||
else t.entry_price - t.close_price
|
||
)
|
||
realized += per_share * t.shares
|
||
return start_equity + realized
|
||
|
||
|
||
async def performance_summary(db: AsyncSession, user_id: int | None = None) -> dict:
|
||
"""Shadow book vs discretionary book vs SPY, from the configured start date.
|
||
|
||
Everything is charted in one unit — percent of the shadow book's equity —
|
||
because SPY is a percent and R is not a return. The shadow line is its
|
||
actual P&L over its equity base; the manual book has no capital base, so it
|
||
is re-sized at the shadow book's rule (``strategy_sized_fraction``), which
|
||
also makes the gap between the two books pure selection. SPY is a plain
|
||
buy-and-hold reference over the same window. R-multiple stats stay as the
|
||
sizing-independent book-vs-book read.
|
||
"""
|
||
from app.services import shadow_book_service
|
||
|
||
start = await get_performance_start(db)
|
||
stmt = select(PaperTrade)
|
||
if start is not None:
|
||
stmt = stmt.where(func.date(PaperTrade.opened_at) >= start)
|
||
if user_id is not None:
|
||
# "Your picks" must be *yours*. The shadow book is a single autonomous
|
||
# book with no owner, so it is never scoped to a user.
|
||
stmt = stmt.where(
|
||
(PaperTrade.book == SHADOW_BOOK) | (PaperTrade.user_id == user_id)
|
||
)
|
||
trades = list((await db.execute(stmt)).scalars().all())
|
||
|
||
benchmark_closes = await benchmark_service.load_benchmark_closes(db)
|
||
empty = {
|
||
"start_date": start.isoformat() if start else None,
|
||
"series": [],
|
||
"stats": {},
|
||
}
|
||
if not trades or not benchmark_closes:
|
||
return empty
|
||
|
||
first = min(t.opened_at.date() for t in trades)
|
||
if start is not None:
|
||
first = max(first, start)
|
||
days = [d for d in sorted(benchmark_closes) if d >= first]
|
||
if not days:
|
||
return empty
|
||
|
||
ticker_ids = {t.ticker_id for t in trades}
|
||
rows = await db.execute(
|
||
select(OHLCVRecord.ticker_id, OHLCVRecord.date, OHLCVRecord.close).where(
|
||
OHLCVRecord.ticker_id.in_(ticker_ids), OHLCVRecord.date >= first
|
||
)
|
||
)
|
||
ticker_closes: dict[int, dict[date, float]] = {}
|
||
for tid, day, close in rows.all():
|
||
ticker_closes.setdefault(tid, {})[day] = float(close)
|
||
|
||
books = {
|
||
MANUAL_BOOK: [t for t in trades if (t.book or MANUAL_BOOK) == MANUAL_BOOK],
|
||
SHADOW_BOOK: [t for t in trades if t.book == SHADOW_BOOK],
|
||
}
|
||
pnl = {
|
||
name: _cumulative_pnl(book_trades, ticker_closes, days)
|
||
for name, book_trades in books.items()
|
||
}
|
||
|
||
bench_dates = sorted(benchmark_closes)
|
||
spy0 = _value_on_or_before(bench_dates, benchmark_closes, days[0])
|
||
spy_pct = [
|
||
round(100.0 * (benchmark_closes[d] / spy0 - 1.0), 2) if spy0 else 0.0
|
||
for d in days
|
||
]
|
||
|
||
config = await shadow_book_service.get_config(db)
|
||
equity_base = await _shadow_equity_base(db, config["start_equity"], days[0])
|
||
pct = {
|
||
SHADOW_BOOK: [round(100.0 * v / equity_base, 2) for v in pnl[SHADOW_BOOK]],
|
||
MANUAL_BOOK: _cumulative_sized_pct(
|
||
books[MANUAL_BOOK],
|
||
ticker_closes,
|
||
days,
|
||
config["risk_pct"],
|
||
shadow_book_service.NOTIONAL_CAP,
|
||
),
|
||
}
|
||
|
||
# Latest close per ticker, for marking open positions in the R stats.
|
||
marks = {
|
||
tid: closes[max(closes)] for tid, closes in ticker_closes.items() if closes
|
||
}
|
||
stats = {name: book_stats(bt, marks) for name, bt in books.items()}
|
||
for name in books:
|
||
stats[name]["pnl"] = pnl[name][-1] if pnl[name] else 0.0
|
||
stats[name]["pct"] = pct[name][-1] if pct[name] else 0.0
|
||
stats["spy"] = {"pct": spy_pct[-1] if spy_pct else 0.0}
|
||
|
||
# The series opens at the first trade in *either* book. If manual trades came
|
||
# first, SPY's full-window return includes days the strategy did not exist,
|
||
# so strategy-vs-SPY is judged from the shadow book's own first trade.
|
||
if books[SHADOW_BOOK]:
|
||
since = max(min(t.opened_at.date() for t in books[SHADOW_BOOK]), days[0])
|
||
s0 = _value_on_or_before(bench_dates, benchmark_closes, since)
|
||
s1 = benchmark_closes[days[-1]]
|
||
stats[SHADOW_BOOK]["since"] = since.isoformat()
|
||
stats[SHADOW_BOOK]["spy_pct"] = round(100.0 * (s1 / s0 - 1.0), 2) if s0 else 0.0
|
||
|
||
series = [
|
||
{
|
||
"date": d.isoformat(),
|
||
"manual_pnl": pnl[MANUAL_BOOK][i],
|
||
"shadow_pnl": pnl[SHADOW_BOOK][i],
|
||
"manual_pct": pct[MANUAL_BOOK][i],
|
||
"shadow_pct": pct[SHADOW_BOOK][i],
|
||
"spy_pct": spy_pct[i],
|
||
}
|
||
for i, d in enumerate(days)
|
||
]
|
||
return {
|
||
"start_date": start.isoformat() if start else None,
|
||
"equity_base": round(equity_base, 2),
|
||
"risk_pct": config["risk_pct"],
|
||
"series": series,
|
||
"stats": stats,
|
||
}
|