fix: unify backtest execution and add forward validation candidates

Share point-in-time signals and a cash-funded order ledger across backtests and trading. Correct eligibility, missing-price fills, quality factors, regime inputs, warmup windows, and cash-injection accounting. Add reproducible registered-strategy comparisons and paired forward candidates with a 20% resize band.

Validation: 125 Python tests, 23 web tests, TypeScript/Vite build, 87 strategy causality checks, and real-data target/order/equity parity for 10 strategies.

bridge.py

@@ -27,7 +27,7 @@ from trader import (
     LIVE_EXCLUDED_STRATEGIES,
     MARKET_FEE_TIERED,
     MONITOR_CAPITAL,
-    STRATEGY_REGISTRY,
+    STRATEGY_REGISTRY, EXECUTION_VARIANTS,
     book_dir,
     book_strategies,
     convert_quote_currency,
@@ -482,6 +482,11 @@ STRATEGY_META = {
 }
 
 
+for _name, (_base, _market) in EXECUTION_VARIANTS.items():
+    STRATEGY_META[_name] = {**STRATEGY_META[_base], "markets": [_market],
+                           "label": STRATEGY_META[_base]["label"] + " · 20%再平衡带（前瞻候选）"}
+
+
 def _log(msg: str) -> None:
     print(msg, file=sys.stderr)
 
@@ -740,7 +745,8 @@ def cmd_backtest(args: argparse.Namespace) -> None:
         raw = run_market(market, known, years, capital, tx_cost, fixed_fee,
                          **MARKET_FEE_TIERED.get(market, {}))
     results = [
-        _build_strategy_result(run["name"], run["equity"], run["trades"], injection_schedule, capital)
+        _build_strategy_result(run["name"], run["equity"], run["trades"], injection_schedule, capital,
+                               execution_plan=run.get("_plan"), fee_params=run.get("_fees"))
         for run in (raw["strategy_runs"] if raw else [])
     ]
     benchmark_label = raw["benchmark_label"] if raw else "SPY"
@@ -749,13 +755,18 @@ def cmd_backtest(args: argparse.Namespace) -> None:
     output = {
         "meta": {
             "market": market,
-            "years": years,
+            "years": raw.get("actual_years", years) if raw else years,
+            "requestedYears": years,
             "initialCapital": capital,
             "startDate": results[0]["equityCurve"][0]["date"] if results else None,
             "endDate": results[0]["equityCurve"][-1]["date"] if results else None,
             "txCost": tx_cost,
             "fixedFee": fixed_fee,
             "injectionSchedule": injection_schedule,
+            "engineVersion": raw.get("engine_version") if raw else None,
+            "warnings": raw.get("warnings", []) if raw else [],
+            "coverage": raw.get("coverage") if raw else None,
+            "errors": raw.get("errors", []) if raw else [],
         },
         "strategies": results,
         "benchmark": {
@@ -773,21 +784,29 @@ def _build_strategy_result(
     trade_events: list[dict],
     injection_schedule: dict | None,
     initial_capital: float,
+    *, execution_plan: dict | None = None, fee_params: dict | None = None,
 ) -> dict:
     """Build a strategy result dict with optional injection applied.
 
     Return and risk metrics, drawdowns and periodic returns are time-weighted
-    (computed on the curve without injections), so injected cash never counts
-    as performance. ``capitalFlows`` reports the money view: total invested,
+    (computed from cash-flow-adjusted NAV), so injected cash never counts
+    as performance. The orders and fees are replayed at the actual capital. ``capitalFlows`` reports the money view: total invested,
     final value and profit of the injected curve.
     """
     injection_events = []
     equity_with_inj = None
 
+    metric_equity = equity
     if injection_schedule:
-        returns = equity.pct_change().fillna(0.0)
-        contributions, injection_events = build_injection_schedule(equity, injection_schedule)
-        equity_with_inj = equity_with_cashflows(returns, contributions, initial_capital)
+        if execution_plan is None or fee_params is None:
+            raise ValueError("Cash injections require an order replay; scaling a return curve is not supported")
+        from backtest_runner import replay_prepared
+        days = execution_plan["prices"].index
+        contributions, injection_events = build_injection_schedule(equity.reindex(days), injection_schedule)
+        replay = replay_prepared(execution_plan, initial_capital, contributions=contributions, **fee_params)
+        equity_with_inj = replay["equity"]
+        metric_equity = replay["unit_equity"]
+        trade_events = replay["trades"]
 
     # Curve shown to the user: the money curve when cash is injected.
     final_equity = equity_with_inj if equity_with_inj is not None else equity
@@ -804,14 +823,10 @@ def _build_strategy_result(
         base_curve = [{"date": d.strftime("%Y-%m-%d"), "value": round(float(v), 2)}
                       for d, v in equity.items()]
 
-    # Time-weighted metrics on the curve without injections
-    strat_metrics = metrics.raw_summary(equity)
-    strat_metrics["rebalanceWinRate"] = compute_rebalance_win_rate(
-        equity, trade_events,
-    )
-
-    drawdowns = compute_drawdown_periods(equity)
-    monthly_ret, yearly_ret = compute_periodic_returns(equity)
+    strat_metrics = metrics.raw_summary(metric_equity)
+    strat_metrics["rebalanceWinRate"] = compute_rebalance_win_rate(metric_equity, trade_events)
+    drawdowns = compute_drawdown_periods(metric_equity)
+    monthly_ret, yearly_ret = compute_periodic_returns(metric_equity)
 
     meta = STRATEGY_META.get(name, {"label": name, "category": "other"})
 
@@ -1060,7 +1075,9 @@ def cmd_books() -> None:
               "strategies": {m: book_strategies(None, m) for m in ("us", "cn")}}]
     for book_id, cfg in LIVE_BOOKS.items():
         books.append({"id": book_id, "label": cfg["label"], "startDate": cfg["start"],
-                      "capital": cfg["capital"], "feePerOrder": cfg["fee_per_order"],
+                      "capital": cfg["capital"],
+                      "feePerOrder": None if "fee_schedule" in cfg else cfg["fee_per_order"],
+                      "feeSchedule": cfg.get("fee_schedule"),
                       "strategies": {m: book_strategies(book_id, m) for m in ("us", "cn")}})
     json.dump(books, sys.stdout)