# ============================================================================= 
#  Multi-Sleeve Momentum Rotation (Gold / Growth-Tech / Financials)
# =============================================================================

#  Gold-miner gate (GDXU/GDXD, RSI(10)) wraps everything. Inside that, a
#  QQQ 90d-vs-70d cumulative-return split sends the book down one of two
#  paths:
#    Path A (QQQ 90d return > 70d return): a SPY-vs-200SMA regime gate into
#      either (a) RSI(10) overbought cascade across
#      SPY/IOO/TQQQ/VTV/XLF -> UVXY/BIL/BTAL, with a bull-trend branch that
#      picks the single most-volatile-lately of 11 leveraged single-stock
#      ETFs (AAPX/NVDL/BITX/TSLR/FBL/GGLL/AMZU/RGTI/PLTR/BABA/CONL) if its
#      own underlying stock has positive 10d return-momentum, or (b) a
#      TQQQ/SPY RSI<30/31 dip-buy, or a TQQQ-SMA20 momentum-vs-bear
#      hedge branch (TECS/SOXS/SQQQ) with its own top-1-momentum leveraged-
#      stock-ETF picker.
#    Path B (QQQ 90d return <= 70d return): TLT-vs-QQQ momentum gate into
#      GDXD, or a FAS (3x financials)-trend-vs-SMA50/200 gate choosing
#      between (a) a top-3-by-20d-momentum basket across 12 financial/
#      fintech names (V/SOFI/MA/BX/SCHW/KKR/BN/WELL/VTR/BAM/HOOD/IBKR) plus
#      the same leveraged-stock-ETF basket as a 13th candidate, (b) a top-3-
#      by-lowest-RSI(10) contrarian dip-buy among those 12 names, or (c) a
#      FAS-vs-SMA100/RSI(10) gate into TMF/FAZ/leveraged-stock-basket
#      (top-1 by 15d momentum) or an AGQ-vs-FAS RSI(10) switcher.
# 
#  -------------------------------------------------------------------------
#  Decision computed once daily right after the session close (indicators
#  finalize then); orders are MarketOnOpenOrder, filled at the NEXT
#  session's open. No look-ahead.
#
# =============================================================================

from AlgorithmImports import *
from tree_data import TREE_JSON
import json
import numpy as np


class MultiSleeveMomentumRotation(QCAlgorithm):

    PRICE_WINDOW_SIZE = 210  # covers the largest lookback used (200) + buffer
    LEVERAGE = 1.5           # target 100% of NAV in positions (margin)
    CASH_BUFFER_PCT = 0.04   # always leave this fraction of NAV uninvested

    # Simple flat-% intraday stop loss, checked every few minutes during
    # market hours (requires Minute-resolution price data -- indicators
    # below stay Resolution.DAILY, so the sleeve logic itself is unaffected).
    # Basis = that day's open. Backtested at 15% as the best of {5,10,15,20}%
    # tried on this strategy -- Pareto-beat the no-stop baseline on CAGR,
    # drawdown, Sharpe, and Sortino all at once.
    ENABLE_INTRADAY_SL = True
    SL_PCT = 0.15
    INTRADAY_CHECK_MINUTES = 5

    def initialize(self) -> None: 
        self.set_start_date(2020, 1, 1)
        self.set_cash(10_000)
        self.set_brokerage_model(
            BrokerageName.INTERACTIVE_BROKERS_BROKERAGE,
            AccountType.MARGIN,
        )
        self.set_benchmark("SPY")
        self.settings.minimum_order_margin_portfolio_percentage = 0.0

        self.tree = json.loads(TREE_JSON)

        # -- Discover every ticker referenced (as a tradable asset or as an
        #    indicator input) and every (ticker, RSI window) pair needed.
        all_tickers = set()
        self._collect_tickers(self.tree, all_tickers)

        rsi_pairs = set()
        self._collect_rsi_pairs(self.tree, rsi_pairs)

        # -- Subscribe & build per-ticker daily price history + RSI indicators
        self._syms = {}
        self._price_window = {}
        for t in sorted(all_tickers):
            try:
                sym = self.add_equity(t, Resolution.MINUTE).symbol
            except Exception as e:
                self.log(f"WARNING: could not subscribe {t}: {e}")
                continue
            self._syms[t] = sym
            self._price_window[t] = RollingWindow[float](self.PRICE_WINDOW_SIZE)

        self._rsi = {}
        for ticker, window in rsi_pairs:
            if ticker not in self._syms:
                continue
            self._rsi[(ticker, window)] = self.rsi(
                self._syms[ticker], window, MovingAverageType.WILDERS, Resolution.DAILY
            )

        self.set_warm_up(self.PRICE_WINDOW_SIZE + 5, Resolution.DAILY)

        # -- Seed price windows from history so day-0 evaluation has data
        for t, sym in self._syms.items():
            hist = self.history(sym, self.PRICE_WINDOW_SIZE + 5, Resolution.DAILY)
            if not hist.empty:
                closes = hist["close"].values
                for c in closes[::-1]:  # oldest first push, so window[0] ends most recent
                    if c > 0:
                        self._price_window[t].add(float(c))

        self._trade_count = 0
        self._day_open = {}  # Symbol -> today's open, for the intraday SL

        # -- Feed daily closes into the rolling windows right after each
        #    session close, then compute the rebalance target and place
        #    MarketOnOpenOrders for the next session's open.
        self.schedule.on(
            self.date_rules.every_day(),
            self.time_rules.after_market_close("SPY", 0),
            self._update_price_windows,
        )
        self.schedule.on(
            self.date_rules.every_day(),
            self.time_rules.after_market_close("SPY", 1),
            self._rebalance,
        )

        # -- Snapshot today's open for every held name, then poll every few
        #    minutes during market hours for the intraday stop loss.
        self.schedule.on(
            self.date_rules.every_day(),
            self.time_rules.after_market_open("SPY", 0),
            self._snapshot_day_open,
        )
        self.schedule.on(
            self.date_rules.every_day(),
            self.time_rules.every(timedelta(minutes=self.INTRADAY_CHECK_MINUTES)),
            self._check_intraday_sl,
        )

    # ── Tree-requirement discovery ──────────────────────────────────

    def _collect_tickers(self, node, out: set) -> None:
        if node.get("t") == "node_asset":
            out.add(node["tk"])
        for expr_key in ("cond", "sf"):
            if expr_key in node:
                self._scan_expr_tickers(node[expr_key], out)
        for key in ("c", "th", "el"):
            for ch in node.get(key, []):
                self._collect_tickers(ch, out)

    def _scan_expr_tickers(self, expr, out: set) -> None:
        if isinstance(expr, list):
            for item in expr:
                self._scan_expr_tickers(item, out)
        elif isinstance(expr, str) and expr.startswith("EQUITIES::"):
            out.add(expr.split("::")[1].split("//")[0])

    def _collect_rsi_pairs(self, node, out: set) -> None:
        for expr_key in ("cond",):
            if expr_key in node:
                self._scan_expr_rsi(node[expr_key], None, out)
        if node.get("t") == "node_filter":
            rsi_window = self._rsi_window_in_expr(node["sf"])
            if rsi_window is not None:
                for ch in node["c"]:
                    rep = self._representative_ticker(ch)
                    if rep:
                        out.add((rep, rsi_window))
        for key in ("c", "th", "el"):
            for ch in node.get(key, []):
                self._collect_rsi_pairs(ch, out)

    def _scan_expr_rsi(self, expr, ref_ticker, out: set) -> None:
        if not isinstance(expr, list):
            return
        if expr[0] == "fn_relative_strength_index":
            tk = self._ticker_of(expr[1], ref_ticker)
            if tk:
                out.add((tk, expr[2]))
        for item in expr:
            self._scan_expr_rsi(item, ref_ticker, out)

    def _rsi_window_in_expr(self, expr):
        if not isinstance(expr, list):
            return None
        if expr[0] == "fn_relative_strength_index":
            return expr[2]
        for item in expr:
            r = self._rsi_window_in_expr(item)
            if r is not None:
                return r
        return None

    def _ticker_of(self, metric_close_expr, ref_ticker):
        arg = metric_close_expr[1]
        if isinstance(arg, list) and arg[0] == "reference":
            return ref_ticker
        return arg.split("::")[1].split("//")[0]

    def _representative_ticker(self, node):
        t = node.get("t")
        if t == "node_asset":
            return node["tk"]
        if t == "node_if" and "cond" in node:
            tk = self._first_ticker_in_expr(node["cond"])
            if tk:
                return tk
        for key in ("c", "th", "el"):
            for ch in node.get(key, []):
                tk = self._representative_ticker(ch)
                if tk:
                    return tk
        return None

    def _first_ticker_in_expr(self, expr):
        if isinstance(expr, list):
            for item in expr:
                if isinstance(item, str) and item.startswith("EQUITIES::"):
                    return item.split("::")[1].split("//")[0]
                tk = self._first_ticker_in_expr(item)
                if tk:
                    return tk
        return None

    # ── Metric / condition evaluation ───────────────────────────────

    def _eval_metric(self, expr, ref_ticker=None) -> float:
        head = expr[0]
        if head == "fn_constant":
            return expr[1]
        if head == "weight_every_fn":
            return self._eval_metric(expr[1], ref_ticker)
        if head == "metric_close":
            tk = self._ticker_of(expr, ref_ticker)
            w = self._price_window.get(tk)
            return w[0] if w and w.is_ready else 0.0
        if head == "fn_relative_strength_index": 
            tk = self._ticker_of(expr[1], ref_ticker)
            window = expr[2]
            ind = self._rsi.get((tk, window))
            return (ind.current.value / 100.0) if ind and ind.is_ready else 0.5
        if head == "fn_simple_moving_average":
            inner, window = expr[1], expr[2]
            if inner[0] == "metric_close":
                tk = self._ticker_of(inner, ref_ticker)
                w = self._price_window.get(tk)
                if not w or not w.is_ready or w.count < window:
                    return 0.0
                return float(np.mean([w[i] for i in range(window)]))
            elif inner[0] == "fn_rate_of_return":
                tk = self._ticker_of(inner[1], ref_ticker)
                w = self._price_window.get(tk)
                if not w or not w.is_ready or w.count < window + 1:
                    return 0.0
                rets = [w[i] / w[i + 1] - 1.0 for i in range(window)]
                return float(np.mean(rets))
        if head == "fn_standard_deviation":
            inner, window = expr[1], expr[2]
            tk = self._ticker_of(inner[1], ref_ticker)
            w = self._price_window.get(tk)
            if not w or not w.is_ready or w.count < window + 1:
                return 0.0
            rets = [w[i] / w[i + 1] - 1.0 for i in range(window)]
            return float(np.std(rets))
        if head == "fn_cumulative_return":
            inner, window = expr[1], expr[2]
            tk = self._ticker_of(inner, ref_ticker)
            w = self._price_window.get(tk)
            if not w or not w.is_ready or w.count < window + 1:
                return 0.0
            return w[0] / w[window] - 1.0
        if head == "fn_rate_of_return":
            tk = self._ticker_of(expr[1], ref_ticker)
            w = self._price_window.get(tk)
            if not w or not w.is_ready or w.count < 2:
                return 0.0
            return w[0] / w[1] - 1.0
        raise ValueError(f"unhandled metric expr head: {head}")

    def _eval_cond(self, expr, ref_ticker=None) -> bool:
        head = expr[0]
        if head == "fn_gt":
            return self._eval_metric(expr[1], ref_ticker) > self._eval_metric(expr[2], ref_ticker)
        if head == "fn_lt":
            return self._eval_metric(expr[1], ref_ticker) < self._eval_metric(expr[2], ref_ticker)
        if head == "fn_or":
            return self._eval_cond(expr[1], ref_ticker) or self._eval_cond(expr[2], ref_ticker)
        raise ValueError(f"unhandled condition head: {head}")

    # ── Tree resolution -> target weights ───────────────────────────

    def _resolve(self, node) -> dict:
        t = node["t"]
        if t == "node_root":
            return self._resolve_children(node["c"])
        if t == "node_weight":
            return self._resolve_weighted(node["c"], node["w"])
        if t == "node_if":
            branch = node["th"] if self._eval_cond(node["cond"]) else node.get("el", [])
            return self._resolve_children(branch)
        if t == "node_asset":
            sym = self._syms.get(node["tk"])
            return {sym: 1.0} if sym is not None else {}
        if t == "node_filter":
            return self._resolve_filter(node)
        raise ValueError(f"unknown node type: {t}")

    def _resolve_children(self, children) -> dict:
        if not children:
            return {}
        combined = {}
        share = 1.0 / len(children)
        for ch in children:
            for sym, w in self._resolve(ch).items():
                combined[sym] = combined.get(sym, 0.0) + w * share
        return combined

    def _resolve_weighted(self, children, weight_spec) -> dict:
        kind = weight_spec[0]
        if kind == "weight_equal":
            return self._resolve_children(children)
        if kind == "weight_constants":
            weights = weight_spec[1]
            combined = {}
            for ch, wt in zip(children, weights):
                for sym, w in self._resolve(ch).items():
                    combined[sym] = combined.get(sym, 0.0) + w * wt
            return combined
        raise ValueError(f"unknown weight kind: {kind}")

    def _resolve_filter(self, node) -> dict:
        sort_fn = node["sf"]
        n = node["n"]
        direction = node["dir"]
        scored = []
        for ch in node["c"]:
            ref_ticker = self._representative_ticker(ch)
            score = self._eval_metric(sort_fn, ref_ticker)
            scored.append((score, ch))
        scored.sort(key=lambda x: x[0], reverse=(direction == "desc"))
        selected = [ch for _, ch in scored[:n]]
        return self._resolve_children(selected)

    # ── Scheduled events ─────────────────────────────────────────────

    def _update_price_windows(self) -> None:
        for t, sym in self._syms.items():
            close = self.securities[sym].close
            if close > 0:
                self._price_window[t].add(float(close))

    def _snapshot_day_open(self) -> None:
        """Record today's open for every subscribed ticker; the intraday SL
        is measured off this, not average cost."""
        if not self.ENABLE_INTRADAY_SL:
            return
        for sym in self._syms.values():
            price = self.securities[sym].price
            if price > 0:
                self._day_open[sym] = float(price)

    def _check_intraday_sl(self) -> None:
        if not self.ENABLE_INTRADAY_SL or self.is_warming_up:
            return
        if not self.is_market_open(self._syms.get("SPY")):
            return
        for h in list(self.portfolio.values()):
            if not h.invested:
                continue
            sym = h.symbol
            open_px = self._day_open.get(sym)
            price = self.securities[sym].price
            if not open_px or open_px <= 0 or price <= 0:
                continue
            chg = price / open_px - 1.0
            if chg <= -self.SL_PCT:
                self.liquidate(sym, tag=f"intraday SL {chg:.1%} vs open (limit {self.SL_PCT:.0%})")
                self.log(f"[SL] {self.time} {sym.value} {chg:.1%} vs open -> liquidated intraday")

    def _rebalance(self) -> None:
        if self.is_warming_up:
            return

        combined = self._resolve(self.tree)
        total_w = sum(combined.values())
        if total_w <= 0:
            return

        targets = set(combined)
        for h in list(self.portfolio.values()):
            if h.invested and h.symbol not in targets:
                self.market_on_open_order(h.symbol, -h.quantity)

        pv = self.portfolio.total_portfolio_value * self.LEVERAGE * (1.0 - self.CASH_BUFFER_PCT)

        # All orders here are MarketOnOpenOrder -- none settle until tomorrow's
        # open, so the exits submitted above can't be relied on to have freed
        # up any margin yet when a buy order below gets validated. Track a
        # local margin budget (starting from what's actually free right now,
        # with a safety haircut) and cap/skip buy orders against it instead of
        # submitting the full computed size and letting the broker reject it.
        margin_budget = self.portfolio.margin_remaining * 0.9
        MARGIN_RATE_ESTIMATE = 0.5  # matches this account's observed Reg-T rate

        for sym, wt in combined.items():
            price = self.securities[sym].price
            if price <= 0:
                continue
            target_qty = int(pv * wt / price)
            delta = target_qty - int(self.portfolio[sym].quantity)
            if delta == 0:
                continue
            if delta > 0:
                est_margin_needed = delta * price * MARGIN_RATE_ESTIMATE
                if est_margin_needed > margin_budget:
                    affordable_qty = int(margin_budget / (price * MARGIN_RATE_ESTIMATE))
                    if affordable_qty <= 0:
                        self.log(f"[MARGIN-SKIP] {self.time.date()} {sym.value} skipped, no margin budget left")
                        continue
                    self.log(f"[MARGIN-CAP] {self.time.date()} {sym.value} capped {delta} -> {affordable_qty} shares (insufficient margin)")
                    delta = affordable_qty
                    est_margin_needed = delta * price * MARGIN_RATE_ESTIMATE
                margin_budget -= est_margin_needed
            self.market_on_open_order(sym, delta)

        self._trade_count += 1
        net = "+".join(f"{round(w*100):.0f}%{s.value}"
                        for s, w in sorted(combined.items(), key=lambda x: -x[1]) if w > 0.005)
        self.log(f"[{self._trade_count:04d}] {self.time.date()} | net={net}")

    def on_end_of_algorithm(self) -> None:
        self.log(f"\n  Final NAV: ${self.portfolio.total_portfolio_value:>15,.2f}  |  Rebalances: {self._trade_count}")

# region imports
from AlgorithmImports import *
# endregion
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