MB-Ndhlovu
ffa95a9f51
Adds benchmark.py with yfinance-backed ticker resolution (SPY, CSI 300, etc.), injects external benchmark via config['benchmark'] in engines/base.py, and renders a benchmark panel (ticker / benchmark return / excess return / information ratio) in cli.py. Closes #48
622 lines
22 KiB
Python
622 lines
22 KiB
Python
"""Base backtest engine with shared bar-by-bar execution loop.
|
|
|
|
All market engines inherit from BaseEngine and override market-rule methods.
|
|
The shared run_backtest() handles: data loading → signal generation →
|
|
pre-compute target weights (with optimizer) → bar-by-bar execution with
|
|
market rule enforcement → metrics → artifacts.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import importlib
|
|
import json
|
|
import logging
|
|
import sys
|
|
from abc import ABC, abstractmethod
|
|
from pathlib import Path
|
|
from typing import Any, Callable, Dict, List, Optional
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
from backtest.metrics import (
|
|
by_exit_reason_stats,
|
|
by_symbol_stats,
|
|
calc_metrics,
|
|
)
|
|
from backtest.models import EquitySnapshot, Position, TradeRecord
|
|
|
|
|
|
# ─── Market detection (lightweight, for signal alignment only) ───
|
|
|
|
import re as _re
|
|
|
|
_CRYPTO_RE = _re.compile(r"^[A-Z]+-USDT$|^[A-Z]+/USDT$", _re.I)
|
|
_FOREX_RE = _re.compile(r"^[A-Z]{3}/[A-Z]{3}$|^[A-Z]{6}\.FX$")
|
|
|
|
|
|
def _detect_market_for_align(code: str) -> str:
|
|
"""Lightweight market detection for ffill_limit calculation."""
|
|
if _CRYPTO_RE.match(code):
|
|
return "crypto"
|
|
if _FOREX_RE.match(code):
|
|
return "forex"
|
|
return "equity"
|
|
|
|
|
|
# ─── Signal alignment (reused from daily_portfolio logic) ───
|
|
|
|
|
|
def _align(
|
|
data_map: Dict[str, pd.DataFrame],
|
|
signal_map: Dict[str, pd.Series],
|
|
codes: List[str],
|
|
optimizer: Optional[Callable] = None,
|
|
) -> tuple:
|
|
"""Build aligned date index, close matrix, target-position matrix, return matrix.
|
|
|
|
Signal is shifted by 1 bar (next-bar-open semantics) then normalised so
|
|
``sum(abs(weights)) <= 1.0``.
|
|
|
|
Args:
|
|
data_map: code -> OHLCV DataFrame.
|
|
signal_map: code -> signal Series.
|
|
codes: Valid instrument codes.
|
|
optimizer: Optional weight optimiser ``(ret, pos, dates) -> pos``.
|
|
|
|
Returns:
|
|
(dates, close_df, positions_df, returns_df)
|
|
"""
|
|
all_dates: set = set()
|
|
for c in codes:
|
|
all_dates.update(data_map[c].index)
|
|
dates = pd.DatetimeIndex(sorted(all_dates))
|
|
|
|
close = pd.DataFrame(index=dates, columns=codes, dtype=float)
|
|
for c in codes:
|
|
close[c] = data_map[c]["close"].reindex(dates)
|
|
|
|
# ffill with limit to avoid masking long suspensions (e.g. 3-week halt)
|
|
# Cross-market needs larger limit (Chinese New Year can be 9-10 bars)
|
|
ffill_limit = 10 if len({_detect_market_for_align(c) for c in codes}) > 1 else 5
|
|
close = close.ffill(limit=ffill_limit)
|
|
|
|
# Drop symbols that are entirely NaN (no data overlap with date range)
|
|
all_nan_cols = [c for c in codes if close[c].isna().all()]
|
|
if all_nan_cols:
|
|
logger.warning("Symbols dropped (no usable price data): %s", all_nan_cols)
|
|
codes = [c for c in codes if c not in all_nan_cols]
|
|
if not codes:
|
|
raise ValueError(f"All symbols have no data in the requested date range")
|
|
close = close[codes]
|
|
|
|
pos = pd.DataFrame(0.0, index=dates, columns=codes)
|
|
for c in codes:
|
|
# Shift on each symbol's OWN trading calendar, then ffill to unified
|
|
own_dates = data_map[c].index
|
|
raw = signal_map[c].reindex(own_dates).fillna(0.0).clip(-1.0, 1.0)
|
|
shifted = raw.shift(1).fillna(0.0)
|
|
pos[c] = shifted.reindex(dates).ffill(limit=ffill_limit).fillna(0.0)
|
|
|
|
ret = close.pct_change().fillna(0.0)
|
|
|
|
if optimizer is not None:
|
|
pos = optimizer(ret, pos, dates)
|
|
|
|
scale = pos.abs().sum(axis=1).clip(lower=1.0)
|
|
pos = pos.div(scale, axis=0)
|
|
|
|
return dates, close, pos, ret
|
|
|
|
|
|
def _load_optimizer(config: Dict[str, Any]) -> Optional[Callable]:
|
|
"""Dynamically load an optimizer function from config.
|
|
|
|
Args:
|
|
config: Backtest configuration.
|
|
|
|
Returns:
|
|
Optimizer callable, or None.
|
|
"""
|
|
opt_name = config.get("optimizer")
|
|
if not opt_name:
|
|
return None
|
|
opt_params = config.get("optimizer_params") or {}
|
|
try:
|
|
mod = importlib.import_module(f"backtest.optimizers.{opt_name}")
|
|
return lambda ret, pos, dates: mod.optimize(ret, pos, dates, **opt_params)
|
|
except (ImportError, AttributeError) as e:
|
|
print(f"[WARN] Failed to load optimizer '{opt_name}': {e}, falling back to equal weight")
|
|
return None
|
|
|
|
|
|
# ─── Base Engine ───
|
|
|
|
|
|
class BaseEngine(ABC):
|
|
"""Abstract base for all market engines.
|
|
|
|
Subclasses override market-rule methods:
|
|
- can_execute: whether a trade is allowed by market rules
|
|
- round_size: lot-size rounding
|
|
- calc_commission: fee structure
|
|
- apply_slippage: slippage model
|
|
- on_bar: per-bar hooks (funding fees, liquidation, etc.)
|
|
"""
|
|
|
|
def __init__(self, config: dict):
|
|
self.config = config
|
|
self.initial_capital: float = config.get("initial_cash", 1_000_000)
|
|
self.default_leverage: float = config.get("leverage", 1.0)
|
|
self.capital: float = self.initial_capital
|
|
self.positions: Dict[str, Position] = {}
|
|
self.trades: List[TradeRecord] = []
|
|
self.equity_snapshots: List[EquitySnapshot] = []
|
|
self._bar_idx: int = 0
|
|
self._active_symbol: str = "" # set by _rebalance/_close_position for subclass use
|
|
|
|
# ── Market rule interface (subclass must implement) ──
|
|
|
|
@abstractmethod
|
|
def can_execute(self, symbol: str, direction: int, bar: pd.Series) -> bool:
|
|
"""Whether market rules allow this trade.
|
|
|
|
Args:
|
|
symbol: Instrument identifier.
|
|
direction: 1 (long), -1 (short), 0 (close).
|
|
bar: Current bar data (OHLCV + extras).
|
|
|
|
Returns:
|
|
True if allowed.
|
|
"""
|
|
|
|
@abstractmethod
|
|
def round_size(self, raw_size: float, price: float) -> float:
|
|
"""Round position size per market lot rules.
|
|
|
|
Args:
|
|
raw_size: Desired size.
|
|
price: Current price.
|
|
|
|
Returns:
|
|
Rounded size.
|
|
"""
|
|
|
|
@abstractmethod
|
|
def calc_commission(self, size: float, price: float, direction: int, is_open: bool) -> float:
|
|
"""Calculate commission for a trade.
|
|
|
|
Args:
|
|
size: Trade size.
|
|
price: Execution price.
|
|
direction: 1 or -1.
|
|
is_open: True for opening, False for closing.
|
|
|
|
Returns:
|
|
Commission amount.
|
|
"""
|
|
|
|
@abstractmethod
|
|
def apply_slippage(self, price: float, direction: int) -> float:
|
|
"""Apply slippage to execution price.
|
|
|
|
Args:
|
|
price: Raw price.
|
|
direction: 1 (buying / covering short) or -1 (selling / shorting).
|
|
|
|
Returns:
|
|
Slipped price.
|
|
"""
|
|
|
|
def on_bar(self, symbol: str, bar: pd.Series, timestamp: pd.Timestamp) -> None:
|
|
"""Per-bar market-rule hook (funding fees, liquidation, etc.).
|
|
|
|
Default: no-op. Override in subclass as needed.
|
|
"""
|
|
|
|
# ── PnL / margin calculation hooks ──
|
|
# Override in FuturesBaseEngine to inject contract multiplier.
|
|
|
|
def _calc_pnl(
|
|
self, symbol: str, direction: int, size: float,
|
|
entry_price: float, exit_price: float,
|
|
) -> float:
|
|
"""Realised PnL for a closed position."""
|
|
return direction * size * (exit_price - entry_price)
|
|
|
|
def _calc_margin(
|
|
self, symbol: str, size: float, price: float, leverage: float,
|
|
) -> float:
|
|
"""Margin (collateral) required for a position."""
|
|
return size * price / leverage
|
|
|
|
def _calc_raw_size(
|
|
self, symbol: str, target_notional: float, price: float,
|
|
) -> float:
|
|
"""Convert target notional exposure to number of units/contracts."""
|
|
return target_notional / price
|
|
|
|
# ── Main entry ──
|
|
|
|
def run_backtest(
|
|
self,
|
|
config: Dict[str, Any],
|
|
loader: Any,
|
|
signal_engine: Any,
|
|
run_dir: Path,
|
|
bars_per_year: int = 252,
|
|
) -> Dict[str, Any]:
|
|
"""Full backtest pipeline.
|
|
|
|
Signature matches ``daily_portfolio.run_backtest`` for drop-in replacement.
|
|
|
|
Args:
|
|
config: Backtest configuration dict.
|
|
loader: DataLoader with ``fetch()`` method.
|
|
signal_engine: SignalEngine with ``generate()`` method.
|
|
run_dir: Artifacts output directory.
|
|
bars_per_year: Annualisation factor.
|
|
|
|
Returns:
|
|
Metrics dictionary.
|
|
"""
|
|
codes = config.get("codes", [])
|
|
interval = config.get("interval", "1D")
|
|
extra_fields = config.get("extra_fields") or None
|
|
|
|
# 1. Load data
|
|
data_map = loader.fetch(
|
|
codes,
|
|
config.get("start_date", ""),
|
|
config.get("end_date", ""),
|
|
fields=extra_fields,
|
|
interval=interval,
|
|
)
|
|
if not data_map:
|
|
print(json.dumps({"error": "No data fetched"}))
|
|
sys.exit(1)
|
|
|
|
# 2. Generate signals
|
|
signal_map = signal_engine.generate(data_map)
|
|
valid_codes = sorted(c for c in signal_map if c in data_map)
|
|
if not valid_codes:
|
|
print(json.dumps({"error": "No valid signals generated"}))
|
|
sys.exit(1)
|
|
|
|
# 3. Pre-compute target weights (with optimizer)
|
|
opt_fn = _load_optimizer(config)
|
|
dates, close_df, target_pos, ret_df = _align(
|
|
data_map, signal_map, valid_codes, optimizer=opt_fn,
|
|
)
|
|
|
|
# Sync codes after _align may have dropped all-NaN symbols
|
|
valid_codes = [c for c in valid_codes if c in target_pos.columns]
|
|
|
|
# 4. Bar-by-bar execution
|
|
self._execute_bars(dates, data_map, close_df, target_pos, valid_codes)
|
|
|
|
# 5. Build output series
|
|
equity_series = pd.Series(
|
|
[s.equity for s in self.equity_snapshots],
|
|
index=[s.timestamp for s in self.equity_snapshots],
|
|
)
|
|
bench_ret = ret_df.mean(axis=1) if ret_df.shape[1] > 0 else pd.Series(0.0, index=dates)
|
|
|
|
# ── External benchmark fetch ──────────────────────────────────────────
|
|
bench_ticker = config.get("benchmark")
|
|
if bench_ticker and bench_ticker != "auto":
|
|
from backtest.benchmark import resolve_benchmark
|
|
bench_result = resolve_benchmark(
|
|
strategy_codes=codes,
|
|
source=config.get("source", "yfinance"),
|
|
start_date=config.get("start_date", ""),
|
|
end_date=config.get("end_date", ""),
|
|
interval=interval,
|
|
explicit=bench_ticker,
|
|
)
|
|
if bench_result is not None:
|
|
bench_ret = bench_result.ret_series.reindex(dates).fillna(0.0)
|
|
bench_equity = self.initial_capital * (1 + bench_ret).cumprod()
|
|
m["benchmark_ticker"] = bench_result.ticker
|
|
m["benchmark_return"] = bench_result.total_ret
|
|
# ── External benchmark fetch ──────────────────────────────────────────
|
|
|
|
bench_equity = self.initial_capital * (1 + bench_ret).cumprod()
|
|
|
|
# 6. Metrics
|
|
m = calc_metrics(equity_series, self.trades, self.initial_capital, bars_per_year, bench_ret)
|
|
m["by_symbol"] = by_symbol_stats(self.trades)
|
|
m["by_exit_reason"] = by_exit_reason_stats(self.trades)
|
|
|
|
# 7. Validation (optional — triggered by config["validation"])
|
|
if config.get("validation"):
|
|
from backtest.validation import run_validation
|
|
v_results = run_validation(
|
|
config, equity_series, self.trades, self.initial_capital, bars_per_year,
|
|
)
|
|
m["validation"] = v_results
|
|
# Write validation.json artifact
|
|
v_path = run_dir / "artifacts" / "validation.json"
|
|
v_path.write_text(json.dumps(v_results, indent=2, ensure_ascii=False), encoding="utf-8")
|
|
|
|
# 8. Artifacts
|
|
self._write_artifacts(
|
|
run_dir, data_map, dates, equity_series, bench_equity, bench_ret,
|
|
target_pos, m, valid_codes,
|
|
)
|
|
|
|
# Print scalar metrics (skip nested dicts for JSON compat)
|
|
print(json.dumps({k: v for k, v in m.items() if not isinstance(v, dict)}, indent=2))
|
|
return m
|
|
|
|
# ── Execution loop ──
|
|
|
|
def _execute_bars(
|
|
self,
|
|
dates: pd.DatetimeIndex,
|
|
data_map: Dict[str, pd.DataFrame],
|
|
close_df: pd.DataFrame,
|
|
target_pos: pd.DataFrame,
|
|
codes: List[str],
|
|
) -> None:
|
|
"""Bar-by-bar execution with market rule enforcement."""
|
|
for i, ts in enumerate(dates):
|
|
self._bar_idx = i
|
|
|
|
# a. Per-bar hooks (funding fees, liquidation checks)
|
|
for c in codes:
|
|
if ts in data_map[c].index:
|
|
self.on_bar(c, data_map[c].loc[ts], ts)
|
|
|
|
# b. Rebalance each symbol to target weight
|
|
equity = self._calc_equity(close_df, ts)
|
|
for c in codes:
|
|
try:
|
|
target_w = float(target_pos.at[ts, c]) if ts in target_pos.index else 0.0
|
|
self._rebalance(c, target_w, data_map.get(c), ts, equity)
|
|
except Exception as exc:
|
|
logger.warning("Rebalance failed for %s at %s: %s", c, ts, exc)
|
|
|
|
# c. Record equity snapshot
|
|
snap_equity = self._calc_equity(close_df, ts)
|
|
total_unrealized = 0.0
|
|
for p in self.positions.values():
|
|
cp = self._safe_price(close_df, ts, p.symbol, p.entry_price)
|
|
total_unrealized += self._calc_pnl(p.symbol, p.direction, p.size, p.entry_price, cp)
|
|
self.equity_snapshots.append(EquitySnapshot(
|
|
timestamp=ts,
|
|
capital=self.capital,
|
|
unrealized=total_unrealized,
|
|
equity=snap_equity,
|
|
positions=len(self.positions),
|
|
))
|
|
|
|
# d. Force close all remaining positions
|
|
if len(dates) > 0:
|
|
last_ts = dates[-1]
|
|
for c in list(self.positions.keys()):
|
|
price = self._safe_price(close_df, last_ts, c, self.positions[c].entry_price)
|
|
self._close_position(c, price, last_ts, "end_of_backtest")
|
|
|
|
def _calc_equity(self, close_df: pd.DataFrame, ts: pd.Timestamp) -> float:
|
|
"""Total equity = free cash + sum(margin + unrealised) per position."""
|
|
equity = self.capital
|
|
for sym, pos in self.positions.items():
|
|
cp = self._safe_price(close_df, ts, sym, pos.entry_price)
|
|
margin = self._calc_margin(sym, pos.size, pos.entry_price, pos.leverage)
|
|
unrealized = self._calc_pnl(sym, pos.direction, pos.size, pos.entry_price, cp)
|
|
equity += margin + unrealized
|
|
return equity
|
|
|
|
def _rebalance(
|
|
self,
|
|
symbol: str,
|
|
target_weight: float,
|
|
df: Optional[pd.DataFrame],
|
|
ts: pd.Timestamp,
|
|
equity: float,
|
|
) -> None:
|
|
"""Adjust position for *symbol* toward *target_weight*."""
|
|
self._active_symbol = symbol
|
|
target_dir = 1 if target_weight > 1e-9 else (-1 if target_weight < -1e-9 else 0)
|
|
current_pos = self.positions.get(symbol)
|
|
|
|
# Nothing to do
|
|
if current_pos is None and target_dir == 0:
|
|
return
|
|
if df is None or ts not in df.index:
|
|
return
|
|
|
|
bar = df.loc[ts]
|
|
|
|
# Close if target is flat or direction changed
|
|
if current_pos is not None:
|
|
need_close = target_dir == 0 or target_dir != current_pos.direction
|
|
if need_close:
|
|
if self.can_execute(symbol, 0, bar):
|
|
open_price = float(bar.get("open", bar.get("close", 0)))
|
|
price = self.apply_slippage(open_price, -current_pos.direction)
|
|
self._close_position(symbol, price, ts, "signal")
|
|
else:
|
|
return # blocked (e.g. limit-down can't sell)
|
|
|
|
# Open new if target non-zero and no remaining position
|
|
if target_dir != 0 and symbol not in self.positions:
|
|
if not self.can_execute(symbol, target_dir, bar):
|
|
return # blocked (e.g. A-share no-short)
|
|
|
|
open_price = float(bar.get("open", bar.get("close", 0)))
|
|
if open_price <= 0:
|
|
return
|
|
|
|
slipped = self.apply_slippage(open_price, target_dir)
|
|
leverage = self.default_leverage
|
|
target_notional = abs(target_weight) * equity * leverage
|
|
raw_size = self._calc_raw_size(symbol, target_notional, slipped)
|
|
size = self.round_size(raw_size, slipped)
|
|
if size <= 0:
|
|
return
|
|
|
|
margin = self._calc_margin(symbol, size, slipped, leverage)
|
|
comm = self.calc_commission(size, slipped, target_dir, is_open=True)
|
|
|
|
# Capital check — reduce if insufficient
|
|
if margin + comm > self.capital:
|
|
available = self.capital - comm
|
|
if available <= 0:
|
|
return
|
|
size = self.round_size(
|
|
self._calc_raw_size(symbol, available * leverage, slipped), slipped,
|
|
)
|
|
if size <= 0:
|
|
return
|
|
margin = self._calc_margin(symbol, size, slipped, leverage)
|
|
comm = self.calc_commission(size, slipped, target_dir, is_open=True)
|
|
|
|
self.capital -= (margin + comm)
|
|
self.positions[symbol] = Position(
|
|
symbol=symbol,
|
|
direction=target_dir,
|
|
entry_price=slipped,
|
|
entry_time=ts,
|
|
size=size,
|
|
leverage=leverage,
|
|
entry_bar_idx=self._bar_idx,
|
|
entry_commission=comm,
|
|
)
|
|
|
|
def _close_position(
|
|
self,
|
|
symbol: str,
|
|
exit_price: float,
|
|
exit_time: pd.Timestamp,
|
|
reason: str,
|
|
) -> None:
|
|
"""Close position, record trade, return capital."""
|
|
self._active_symbol = symbol
|
|
pos = self.positions.pop(symbol, None)
|
|
if pos is None:
|
|
return
|
|
|
|
pnl = self._calc_pnl(symbol, pos.direction, pos.size, pos.entry_price, exit_price)
|
|
margin = self._calc_margin(symbol, pos.size, pos.entry_price, pos.leverage)
|
|
pnl_pct = pnl / margin * 100 if margin > 1e-9 else 0.0
|
|
exit_comm = self.calc_commission(pos.size, exit_price, pos.direction, is_open=False)
|
|
|
|
self.capital += margin + pnl - exit_comm
|
|
|
|
holding_bars = max(self._bar_idx - pos.entry_bar_idx, 0)
|
|
|
|
self.trades.append(TradeRecord(
|
|
symbol=symbol,
|
|
direction=pos.direction,
|
|
entry_price=pos.entry_price,
|
|
exit_price=exit_price,
|
|
entry_time=pos.entry_time,
|
|
exit_time=exit_time,
|
|
size=pos.size,
|
|
leverage=pos.leverage,
|
|
pnl=pnl,
|
|
pnl_pct=pnl_pct,
|
|
exit_reason=reason,
|
|
holding_bars=holding_bars,
|
|
commission=pos.entry_commission + exit_comm,
|
|
))
|
|
|
|
# ── Artifacts ──
|
|
|
|
def _write_artifacts(
|
|
self,
|
|
run_dir: Path,
|
|
data_map: Dict[str, pd.DataFrame],
|
|
dates: pd.DatetimeIndex,
|
|
equity_series: pd.Series,
|
|
bench_equity: pd.Series,
|
|
bench_ret: pd.Series,
|
|
target_pos: pd.DataFrame,
|
|
metrics: dict,
|
|
codes: List[str],
|
|
) -> None:
|
|
"""Write CSV artifacts compatible with daily_portfolio format."""
|
|
out = run_dir / "artifacts"
|
|
out.mkdir(parents=True, exist_ok=True)
|
|
|
|
# OHLCV per symbol
|
|
for code, df in data_map.items():
|
|
df.to_csv(out / f"ohlcv_{code}.csv")
|
|
|
|
# Equity curve
|
|
port_ret = equity_series.pct_change().fillna(0.0)
|
|
peak = equity_series.cummax()
|
|
dd = (equity_series - peak) / peak.replace(0, 1)
|
|
eq_df = pd.DataFrame({
|
|
"ret": port_ret,
|
|
"equity": equity_series,
|
|
"drawdown": dd,
|
|
"benchmark_equity": bench_equity.reindex(dates),
|
|
"active_ret": port_ret - bench_ret.reindex(dates).fillna(0.0),
|
|
}, index=dates)
|
|
eq_df.index.name = "timestamp"
|
|
eq_df.to_csv(out / "equity.csv")
|
|
|
|
# Position weights (target, for compatibility)
|
|
target_pos.index.name = "timestamp"
|
|
target_pos.to_csv(out / "positions.csv")
|
|
|
|
# Trades (compatible format)
|
|
trade_rows = []
|
|
for t in self.trades:
|
|
# Entry event
|
|
trade_rows.append({
|
|
"timestamp": str(t.entry_time.date()) if hasattr(t.entry_time, "date") else str(t.entry_time),
|
|
"code": t.symbol,
|
|
"side": "buy" if t.direction == 1 else "sell",
|
|
"price": round(t.entry_price, 4),
|
|
"qty": round(t.size, 6),
|
|
"reason": "signal",
|
|
"pnl": 0.0,
|
|
"holding_days": 0,
|
|
"return_pct": 0.0,
|
|
})
|
|
# Exit event
|
|
try:
|
|
hold_days = (t.exit_time - t.entry_time).days
|
|
except Exception:
|
|
hold_days = 0
|
|
trade_rows.append({
|
|
"timestamp": str(t.exit_time.date()) if hasattr(t.exit_time, "date") else str(t.exit_time),
|
|
"code": t.symbol,
|
|
"side": "sell" if t.direction == 1 else "buy",
|
|
"price": round(t.exit_price, 4),
|
|
"qty": round(t.size, 6),
|
|
"reason": t.exit_reason,
|
|
"pnl": round(t.pnl, 4),
|
|
"holding_days": hold_days,
|
|
"return_pct": round(t.pnl_pct, 2),
|
|
})
|
|
|
|
trade_cols = ["timestamp", "code", "side", "price", "qty", "reason", "pnl", "holding_days", "return_pct"]
|
|
pd.DataFrame(trade_rows or [], columns=trade_cols).to_csv(out / "trades.csv", index=False)
|
|
|
|
# Metrics
|
|
flat_metrics = {k: v for k, v in metrics.items() if not isinstance(v, dict)}
|
|
pd.DataFrame([flat_metrics]).to_csv(out / "metrics.csv", index=False)
|
|
|
|
# ── Helpers ──
|
|
|
|
@staticmethod
|
|
def _safe_price(
|
|
close_df: pd.DataFrame,
|
|
ts: pd.Timestamp,
|
|
symbol: str,
|
|
fallback: float,
|
|
) -> float:
|
|
"""Get close price with fallback."""
|
|
if ts in close_df.index and symbol in close_df.columns:
|
|
val = close_df.at[ts, symbol]
|
|
if pd.notna(val):
|
|
return float(val)
|
|
return fallback
|