feat: enhance trader with expanded capabilities

trader.py

@@ -44,6 +44,14 @@ from strategies.factor_combo import FactorComboStrategy
 from strategies.inverse_vol import InverseVolatilityStrategy
 from strategies.momentum import MomentumStrategy
 from strategies.momentum_quality import MomentumQualityStrategy
+from strategies.permanent import (
+    ETF_UNIVERSE,
+    GLOBAL_ETF_UNIVERSE,
+    HK_ETF_UNIVERSE,
+    TREND_RIDER_V4_UNIVERSE,
+    TrendRiderV3,
+    TrendRiderV4,
+)
 from strategies.recovery_momentum import RecoveryMomentumStrategy
 from strategies.trend_following import TrendFollowingStrategy
 from universe import UNIVERSES
@@ -107,8 +115,57 @@ STRATEGY_REGISTRY = {
     "fc_up_cap_mom_gap_weekly": lambda **kw: FactorComboStrategy("up_cap+mom_gap", rebal_freq=5),
     "fc_up_cap_mom_gap_biweekly": lambda **kw: FactorComboStrategy("up_cap+mom_gap", rebal_freq=10),
     "fc_up_cap_mom_gap_monthly": lambda **kw: FactorComboStrategy("up_cap+mom_gap", rebal_freq=21),
+    # --- ETF tactical allocation strategies ---
+    "trend_rider_v3_us": lambda **kw: TrendRiderV3(),
+    "trend_rider_v3_global": lambda **kw: TrendRiderV3(
+        risk_on=("TQQQ", "UPRO", "YINN", "CHAU"),
+        risk_off=("GLD", "DBC"),
+    ),
+    "trend_rider_v3_hk": lambda **kw: TrendRiderV3(
+        risk_on=("7200.HK", "7500.HK"),
+        risk_off=("GLD", "DBC"),
+    ),
+    "trend_rider_v4": lambda **kw: TrendRiderV4(),
 }
 
+ETF_STRATEGY_UNIVERSES = {
+    "trend_rider_v3_us": sorted(set(ETF_UNIVERSE)),
+    "trend_rider_v3_global": sorted(set(GLOBAL_ETF_UNIVERSE)),
+    "trend_rider_v3_hk": sorted(set(HK_ETF_UNIVERSE)),
+    "trend_rider_v4": sorted(set(TREND_RIDER_V4_UNIVERSE)),
+}
+
+DEFAULT_MONITOR_STRATEGIES = [
+    name for name in STRATEGY_REGISTRY
+    if name not in ETF_STRATEGY_UNIVERSES
+]
+
+
+def strategy_universe(market: str, strategy_name: str) -> tuple[list[str], str]:
+    """Return tradable tickers and benchmark for a strategy.
+
+    Stock strategies use the market's dynamic universe. TrendRider variants
+    trade fixed USD/HK ETF baskets and use SPY as the regime benchmark.
+    """
+    base_name = strategy_name.removeprefix("sim_")
+    if base_name in ETF_STRATEGY_UNIVERSES:
+        return ETF_STRATEGY_UNIVERSES[base_name], "SPY"
+
+    universe = UNIVERSES[market]
+    tickers = universe["fetch"]()
+    return tickers, universe["benchmark"]
+
+
+def strategy_data_market(market: str, strategy_name: str) -> str:
+    """Return the cache namespace used for a strategy's price data."""
+    base_name = strategy_name.removeprefix("sim_")
+    return "etfs" if base_name in ETF_STRATEGY_UNIVERSES else market
+
+
+def filter_tradable_tickers(price_data: pd.DataFrame, tickers: list[str]) -> list[str]:
+    """Keep requested tickers that are present in a downloaded price panel."""
+    return [t for t in tickers if t in price_data.columns]
+
 
 # ---------------------------------------------------------------------------
 # Persistent state
@@ -383,9 +440,8 @@ def cmd_morning(args):
     """Morning: download open prices, generate today's trade orders."""
     market = args.market
     strategy_name = args.strategy
-    universe = UNIVERSES[market]
+    tickers, benchmark = strategy_universe(market, strategy_name)
-    tickers = universe["fetch"]()
+    data_market = strategy_data_market(market, strategy_name)
-    benchmark = universe["benchmark"]
     all_tickers = sorted(set(tickers + [benchmark]))
 
     # Load or init state
@@ -395,8 +451,8 @@ def cmd_morning(args):
         print(f"--- Initialized new portfolio: ${args.capital:,.0f} cash ---")
 
     # Download data (close + open)
-    close_data, open_data = data_manager.update(market, all_tickers, with_open=True)
+    close_data, open_data = data_manager.update(data_market, all_tickers, with_open=True)
-    tickers = [t for t in tickers if t in close_data.columns]
+    tickers = filter_tradable_tickers(close_data, tickers)
 
     today = open_data.index[-1]
     today_str = str(today.date())
@@ -473,9 +529,8 @@ def cmd_evening(args):
     """Evening: record execution at close prices, update portfolio."""
     market = args.market
     strategy_name = args.strategy
-    universe = UNIVERSES[market]
+    tickers, benchmark = strategy_universe(market, strategy_name)
-    tickers = universe["fetch"]()
+    data_market = strategy_data_market(market, strategy_name)
-    benchmark = universe["benchmark"]
     all_tickers = sorted(set(tickers + [benchmark]))
 
     state = load_state(market, strategy_name)
@@ -495,8 +550,8 @@ def cmd_evening(args):
         return
 
     # Get close prices
-    close_data = data_manager.update(market, all_tickers)
+    close_data = data_manager.update(data_market, all_tickers)
-    tickers = [t for t in tickers if t in close_data.columns]
+    tickers = filter_tradable_tickers(close_data, tickers)
 
     target_date = pd.Timestamp(trade_date)
     all_held = list(set(
@@ -577,11 +632,10 @@ def cmd_status(args):
         return
 
     # Get latest prices
-    universe = UNIVERSES[market]
+    tickers, benchmark = strategy_universe(market, strategy_name)
-    tickers = universe["fetch"]()
+    data_market = strategy_data_market(market, strategy_name)
-    benchmark = universe["benchmark"]
     all_tickers = sorted(set(tickers + [benchmark]))
-    close_data = data_manager.update(market, all_tickers)
+    close_data = data_manager.update(data_market, all_tickers)
 
     last_date = close_data.index[-1]
     all_held = list(state["holdings"].keys())
@@ -883,14 +937,13 @@ def cmd_simulate(args):
     """Simulate day-by-day over a date range."""
     market = args.market
     strategy_name = args.strategy
-    universe = UNIVERSES[market]
+    tickers, benchmark = strategy_universe(market, strategy_name)
-    tickers = universe["fetch"]()
+    data_market = strategy_data_market(market, strategy_name)
-    benchmark = universe["benchmark"]
     all_tickers = sorted(set(tickers + [benchmark]))
 
     # Load both open and close data
-    close_data, open_data = data_manager.update(market, all_tickers, with_open=True)
+    close_data, open_data = data_manager.update(data_market, all_tickers, with_open=True)
-    tickers = [t for t in tickers if t in close_data.columns]
+    tickers = filter_tradable_tickers(close_data, tickers)
 
     # Date range
     start = pd.Timestamp(args.start)
@@ -1259,9 +1312,8 @@ def cmd_auto(args):
 
     market = args.market
     strategy_name = args.strategy
-    universe = UNIVERSES[market]
+    tickers, benchmark = strategy_universe(market, strategy_name)
-    tickers = universe["fetch"]()
+    data_market = strategy_data_market(market, strategy_name)
-    benchmark = universe["benchmark"]
     all_tickers = sorted(set(tickers + [benchmark]))
 
     # Load or init state
@@ -1271,8 +1323,8 @@ def cmd_auto(args):
         print(f"[auto] Initialized new portfolio: ${args.capital:,.0f} cash")
 
     # Download data (close + open)
-    close_data, open_data = data_manager.update(market, all_tickers, with_open=True)
+    close_data, open_data = data_manager.update(data_market, all_tickers, with_open=True)
-    tickers = [t for t in tickers if t in close_data.columns]
+    tickers = filter_tradable_tickers(close_data, tickers)
 
     today = close_data.index[-1]
     today_str = str(today.date())
@@ -1398,7 +1450,7 @@ def main():
                            help="Markets to monitor (default: ALL)")
     p_monitor.add_argument("--strategy", nargs="+",
                            choices=list(STRATEGY_REGISTRY.keys()),
-                           default=list(STRATEGY_REGISTRY.keys()),
+                           default=DEFAULT_MONITOR_STRATEGIES,
                            help="Strategies to run (default: ALL)")
     p_monitor.add_argument("--capital", type=float, default=10_000)
     p_monitor.add_argument("--tx-cost", type=float, default=0.001,