fix(web): 自动刷新保留 K 线视窗；威科夫与图表增量更新

自动刷新改用 tail update 与 scrollToPosition 恢复视窗，避免 setData 后跳到最右；拆分 chart_tv 模块并扩展 analyze/recent API。同步威科夫分析、pipeline 增量构建及相关策略与配置。

Co-authored-by: Cursor <cursoragent@cursor.com>

research/lps_v2_failed/REJECT.md

@@ -0,0 +1,43 @@
+# LPS V2 — REJECTED（归档，不再救援）
+
+## Hypothesis
+
+**4h 原生 SOS Confirm → 1h LPS Entry**  
+大级别事件、小级别执行（非 1h 假突破）
+
+## Implementation
+
+见 `Wyckoff_BTC_LPS_V2.py`
+
+4h SOS: 实体收盘离开区间 + vol>MA*1.5 + close strength>0.7 + 3 根 hold  
+1h LPS: 首次回踩 + 0.5~1.5 ATR + vol<breakout_vol + close>prev high
+
+## Result
+
+| Window | Profit | n | PF |
+|--------|--------|---|-----|
+| Train | -3.50% | 3 | 0 |
+| Validate | -1.61% | 2 | 0 |
+| Test | +1.22% | 2 | 1.82 |
+| Full | **-3.91%** | 7 | **0.40** |
+| fee+slip | -4.55% | 7 | **0.35** |
+
+证据文件: `wyckoff_lps_v2_phase2_result.json`
+
+## Funnel
+
+```
+4h sos_raw 183 → confirmed 123 → 1h LPS 7
+```
+
+SOS 识别有产出；**SOS→LPS 映射无稳定边际**。
+
+## Reject reason
+
+在 BTC 永续当前结构下，传统股票式 SOS→LPS→Markup 假设不成立：  
+突破后常不给标准 LPS，或首次回踩已破坏结构。  
+样本少/成本/Regime 均非主因。**停止优化本假设。**
+
+## Reopen only if
+
+成交量分布 / 订单流 / 资金费率等新信息源进入假设。

research/lps_v2_failed/Wyckoff_BTC_LPS_V2.py

@@ -0,0 +1,492 @@
+# --- Do not remove these libs ---
+"""
+Wyckoff BTC — Branch B: LPS Trend Continuation（独立 Setup 研究）
+
+Status: RESEARCH
+Spring V1: BASELINE FROZEN（禁止改动 / 禁止与本分支合并调参）
+
+LPS V2 假设（验证中）:
+  4h 原生 SOS Confirm → 1h LPS Entry
+  不是 1h 假突破回踩
+
+4h SOS:
+  ① close > range_high（实体收盘离开区间，非 wick）
+  ② volume > MA20 * 1.5
+  ③ close strength (close-low)/(high-low) > 0.7
+  ④ 随后 3 根 4h close 仍 > breakout_level
+
+1h LPS:
+  第一次回踩 breakout_level
+  回踩深度 0.5~1.5 ATR(1h)
+  volume_4h < sos_break_volume
+  转强: close > previous high
+
+setup_type / enter_tag: LPS / LPSY
+regime_mode=trend（Range disabled）
+"""
+from freqtrade.strategy import (
+	IStrategy, IntParameter, DecimalParameter, CategoricalParameter,
+	merge_informative_pair, stoploss_from_open, stoploss_from_absolute,
+)
+from freqtrade.persistence import Trade
+import talib.abstract as ta
+from pandas import DataFrame
+import pandas as pd
+import numpy as np
+from datetime import datetime
+from typing import Optional
+import logging
+
+logger = logging.getLogger(__name__)
+
+# freqtrade backtesting -c ./user_data/Chan/config/Wyckoff_BTC_LPS.json \
+#   --strategy Wyckoff_BTC_LPS --strategy-path ./user_data/Chan/strategies --timerange=20230101-
+
+
+class Wyckoff_BTC_LPS(IStrategy):
+	"""LPS V2: 4h 原生 SOS → 1h LPS。不与 Spring 混用。"""
+	INTERFACE_VERSION = 3
+	STRATEGY_VERSION = "LPS_V2"
+	SETUP_FAMILY = "LPS"
+
+	timeframe = "1h"
+	structure_timeframe = "4h"
+	bias_timeframe: Optional[str] = "8h"
+	use_bias_filter = True
+	regime_mode: str = "trend"
+
+	can_short = True
+	process_only_new_candles = True
+	startup_candle_count = 220
+
+	minimal_roi = {
+		"0": 0.12,
+		"1440": 0.06,
+		"4320": 0.03,
+		"10080": 0,
+	}
+	stoploss = -0.10
+	use_custom_stoploss = True
+	trailing_stop = True
+	trailing_stop_positive = 0.025
+	trailing_stop_positive_offset = 0.05
+	trailing_only_offset_is_reached = True
+	use_exit_signal = True
+	exit_profit_only = False
+
+	# ---- 固定规则（不做 hyperopt）----
+	range_lookback = IntParameter(12, 48, default=24, space="buy", optimize=False)
+	sos_vol_mult = DecimalParameter(1.2, 2.5, default=1.5, decimals=1, space="buy", optimize=False)
+	sos_close_strength = DecimalParameter(0.55, 0.90, default=0.70, decimals=2, space="buy", optimize=False)
+	sos_hold_bars_4h = IntParameter(1, 6, default=3, space="buy", optimize=False)
+	lps_pb_atr_min = DecimalParameter(0.3, 1.0, default=0.5, decimals=1, space="buy", optimize=False)
+	lps_pb_atr_max = DecimalParameter(1.0, 2.5, default=1.5, decimals=1, space="buy", optimize=False)
+	lps_max_age_1h = IntParameter(12, 120, default=72, space="buy", optimize=False)
+
+	atr_sl_mult = DecimalParameter(1.2, 3.5, default=1.5, decimals=1, space="sell", optimize=False)
+	atr_sl_min = DecimalParameter(0.012, 0.04, default=0.018, decimals=3, space="sell", optimize=False)
+	atr_sl_max = DecimalParameter(0.05, 0.12, default=0.08, decimals=2, space="sell", optimize=False)
+	time_stop_hours = IntParameter(48, 240, default=168, space="sell", optimize=False)
+
+	use_lps_long = CategoricalParameter([True, False], default=True, space="buy", optimize=False)
+	use_lps_short = CategoricalParameter([True, False], default=True, space="buy", optimize=False)
+
+	lev = 1.0
+
+	def informative_pairs(self):
+		pairs = self.dp.current_whitelist() if self.dp else []
+		tfs = {self.structure_timeframe}
+		if self.bias_timeframe and self.use_bias_filter:
+			tfs.add(self.bias_timeframe)
+		return [(pair, tf) for pair in pairs for tf in tfs]
+
+	def _add_bias_tf(self, df: DataFrame) -> DataFrame:
+		df = df.copy()
+		df["ema50"] = ta.EMA(df, timeperiod=50)
+		df["ema200"] = ta.EMA(df, timeperiod=200)
+		df["bull_bias"] = (df["close"] > df["ema200"]) & (df["ema50"] > df["ema200"])
+		df["bear_bias"] = (df["close"] < df["ema200"]) & (df["ema50"] < df["ema200"])
+		return df
+
+	def _add_sos_structure_4h(self, df: DataFrame) -> DataFrame:
+		"""在 4h 原生计算 SOS / SOW（含 hold 确认，无前视进场）。"""
+		df = df.copy()
+		lb = int(self.range_lookback.value)
+		hold = int(self.sos_hold_bars_4h.value)
+		vol_m = float(self.sos_vol_mult.value)
+		strength_min = float(self.sos_close_strength.value)
+
+		df["atr"] = ta.ATR(df, timeperiod=14)
+		df["volume_ma"] = ta.SMA(df, timeperiod=20, price="volume")
+		df["ema50"] = ta.EMA(df, timeperiod=50)
+		df["ema200"] = ta.EMA(df, timeperiod=200)
+		df["adx"] = ta.ADX(df, timeperiod=14)
+
+		# 区间用「突破前」边界：shift(1) 的 rolling，避免当根抬高
+		df["range_high"] = df["high"].rolling(lb).max().shift(1)
+		df["range_low"] = df["low"].rolling(lb).min().shift(1)
+
+		bar_range = (df["high"] - df["low"]).replace(0, np.nan)
+		df["close_strength"] = (df["close"] - df["low"]) / bar_range
+		df["close_weakness"] = (df["high"] - df["close"]) / bar_range
+
+		vol_ok = df["volume"] > df["volume_ma"] * vol_m
+
+		# ① 实体收盘离开区间 ② 放量 ③ Effort Result
+		sos_raw = (
+			df["range_high"].notna()
+			& (df["close"] > df["range_high"])
+			& (df["close"].shift(1) <= df["range_high"])
+			& vol_ok
+			& (df["close_strength"] > strength_min)
+		)
+		sow_raw = (
+			df["range_low"].notna()
+			& (df["close"] < df["range_low"])
+			& (df["close"].shift(1) >= df["range_low"])
+			& vol_ok
+			& (df["close_weakness"] > strength_min)
+		)
+
+		# 事件位：突破当根冻结
+		sos_level = df["range_high"].where(sos_raw)
+		sos_vol = df["volume"].where(sos_raw)
+		sos_origin = df["range_low"].where(sos_raw)
+		sow_level = df["range_low"].where(sow_raw)
+		sow_vol = df["volume"].where(sow_raw)
+		sow_origin = df["range_high"].where(sow_raw)
+
+		# ④ Hold：突破后 hold 根 4h 收盘仍在突破侧 → 在第 hold 根确认（无前视）
+		sos_confirmed = sos_raw.shift(hold).fillna(False)
+		sow_confirmed = sow_raw.shift(hold).fillna(False)
+		for k in range(hold):
+			sos_confirmed = sos_confirmed & (df["close"].shift(k) > sos_level.shift(hold))
+			sow_confirmed = sow_confirmed & (df["close"].shift(k) < sow_level.shift(hold))
+
+		# 确认当根带出冻结字段，再 ffill 供 1h 使用
+		df["sos_raw"] = sos_raw.fillna(False)
+		df["sow_raw"] = sow_raw.fillna(False)
+		df["sos_confirmed"] = sos_confirmed.fillna(False)
+		df["sow_confirmed"] = sow_confirmed.fillna(False)
+
+		df["sos_break_level"] = sos_level.shift(hold).where(df["sos_confirmed"])
+		df["sos_break_volume"] = sos_vol.shift(hold).where(df["sos_confirmed"])
+		df["sos_origin"] = sos_origin.shift(hold).where(df["sos_confirmed"])
+		df["sow_break_level"] = sow_level.shift(hold).where(df["sow_confirmed"])
+		df["sow_break_volume"] = sow_vol.shift(hold).where(df["sow_confirmed"])
+		df["sow_origin"] = sow_origin.shift(hold).where(df["sow_confirmed"])
+
+		df["sos_break_level"] = df["sos_break_level"].ffill()
+		df["sos_break_volume"] = df["sos_break_volume"].ffill()
+		df["sos_origin"] = df["sos_origin"].ffill()
+		df["sow_break_level"] = df["sow_break_level"].ffill()
+		df["sow_break_volume"] = df["sow_break_volume"].ffill()
+		df["sow_origin"] = df["sow_origin"].ffill()
+
+		df["bull_bias"] = (df["close"] > df["ema200"]) & (df["ema50"] > df["ema200"])
+		df["bear_bias"] = (df["close"] < df["ema200"]) & (df["ema50"] < df["ema200"])
+		return df
+
+	@staticmethod
+	def _bars_since(event: pd.Series) -> pd.Series:
+		ev = event.fillna(False).astype(bool).to_numpy()
+		out = np.full(len(ev), np.nan)
+		c = np.nan
+		for i, e in enumerate(ev):
+			if e:
+				c = 0.0
+			elif not np.isnan(c):
+				c += 1.0
+			out[i] = c
+		return pd.Series(out, index=event.index)
+
+	@staticmethod
+	def _expanding_max_since(event: pd.Series, value: pd.Series) -> pd.Series:
+		"""每个 event 之后对 value 做分段累计 max。"""
+		ev = event.fillna(False).astype(bool).to_numpy()
+		vals = value.to_numpy(dtype=float)
+		out = np.full(len(ev), np.nan)
+		cur = np.nan
+		active = False
+		for i in range(len(ev)):
+			if ev[i]:
+				active = True
+				cur = vals[i]
+			elif active:
+				if not np.isnan(vals[i]):
+					cur = vals[i] if np.isnan(cur) else max(cur, vals[i])
+			out[i] = cur if active else np.nan
+		return pd.Series(out, index=event.index)
+
+	@staticmethod
+	def _expanding_min_since(event: pd.Series, value: pd.Series) -> pd.Series:
+		ev = event.fillna(False).astype(bool).to_numpy()
+		vals = value.to_numpy(dtype=float)
+		out = np.full(len(ev), np.nan)
+		cur = np.nan
+		active = False
+		for i in range(len(ev)):
+			if ev[i]:
+				active = True
+				cur = vals[i]
+			elif active:
+				if not np.isnan(vals[i]):
+					cur = vals[i] if np.isnan(cur) else min(cur, vals[i])
+			out[i] = cur if active else np.nan
+		return pd.Series(out, index=event.index)
+
+	def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+		pair = metadata["pair"]
+		stf = self.structure_timeframe
+		btf = self.bias_timeframe
+
+		inf4 = self.dp.get_pair_dataframe(pair=pair, timeframe=stf)
+		inf4 = self._add_sos_structure_4h(inf4)
+		keep4 = [
+			"date", "atr", "adx", "volume",
+			"range_high", "range_low", "close_strength",
+			"sos_raw", "sow_raw", "sos_confirmed", "sow_confirmed",
+			"sos_break_level", "sos_break_volume", "sos_origin",
+			"sow_break_level", "sow_break_volume", "sow_origin",
+			"bull_bias", "bear_bias",
+		]
+		inf4 = inf4[[c for c in keep4 if c in inf4.columns]].copy()
+		dataframe = merge_informative_pair(dataframe, inf4, self.timeframe, stf, ffill=True)
+
+		if btf and self.use_bias_filter and btf != stf:
+			infb = self.dp.get_pair_dataframe(pair=pair, timeframe=btf)
+			infb = self._add_bias_tf(infb)
+			infb = infb[["date", "bull_bias", "bear_bias", "ema50", "ema200"]].copy()
+			dataframe = merge_informative_pair(dataframe, infb, self.timeframe, btf, ffill=True)
+
+		ss = f"_{stf}"
+		bs = f"_{btf}" if btf and btf != stf else ss
+
+		dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
+		dataframe["ema21"] = ta.EMA(dataframe, timeperiod=21)
+		dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50)
+		dataframe["volume_ma"] = ta.SMA(dataframe, timeperiod=20, price="volume")
+
+		# 8h bias（优先）；否则退回 4h bias
+		if f"bull_bias{bs}" in dataframe.columns:
+			bull = dataframe[f"bull_bias{bs}"].fillna(False).astype(bool)
+			bear = dataframe[f"bear_bias{bs}"].fillna(False).astype(bool)
+		else:
+			bull = dataframe[f"bull_bias{ss}"].fillna(False).astype(bool)
+			bear = dataframe[f"bear_bias{ss}"].fillna(False).astype(bool)
+		dataframe["bias_long_ok"] = bull
+		dataframe["bias_short_ok"] = bear
+
+		sos_conf = dataframe[f"sos_confirmed{ss}"].fillna(False).astype(bool)
+		sow_conf = dataframe[f"sow_confirmed{ss}"].fillna(False).astype(bool)
+		# 确认沿上升沿：4h 确认映射到 1h 后的首次 True
+		sos_event = sos_conf & ~sos_conf.shift(1).fillna(False)
+		sow_event = sow_conf & ~sow_conf.shift(1).fillna(False)
+
+		sos_level = dataframe[f"sos_break_level{ss}"]
+		sos_bvol = dataframe[f"sos_break_volume{ss}"]
+		sos_origin = dataframe[f"sos_origin{ss}"]
+		sow_level = dataframe[f"sow_break_level{ss}"]
+		sow_bvol = dataframe[f"sow_break_volume{ss}"]
+		sow_origin = dataframe[f"sow_origin{ss}"]
+		vol4 = dataframe[f"volume{ss}"]
+
+		sos_age = self._bars_since(sos_event)
+		sow_age = self._bars_since(sow_event)
+		post_high = self._expanding_max_since(sos_event, dataframe["high"])
+		post_low = self._expanding_min_since(sow_event, dataframe["low"])
+
+		atr = dataframe["atr"]
+		pb_min = float(self.lps_pb_atr_min.value)
+		pb_max = float(self.lps_pb_atr_max.value)
+		max_age = float(self.lps_max_age_1h.value)
+
+		# 回踩深度：SOS 后高点回撤的 ATR 倍数
+		retrace_long = (post_high - dataframe["low"]) / atr.replace(0, np.nan)
+		retrace_short = (dataframe["high"] - post_low) / atr.replace(0, np.nan)
+
+		near_sos = dataframe["low"] <= (sos_level + atr * 0.35)
+		near_sow = dataframe["high"] >= (sow_level - atr * 0.35)
+		vol_dry_long = vol4 < sos_bvol
+		vol_dry_short = vol4 < sow_bvol
+		reclaim_long = dataframe["close"] > dataframe["high"].shift(1)
+		reclaim_short = dataframe["close"] < dataframe["low"].shift(1)
+
+		first_near_long = near_sos & ~near_sos.shift(1).fillna(False)
+		first_near_short = near_sow & ~near_sow.shift(1).fillna(False)
+
+		alive_long = (
+			sos_age.notna()
+			& (sos_age >= 1)
+			& (sos_age <= max_age)
+			& (dataframe["close"] > sos_origin)
+		)
+		alive_short = (
+			sow_age.notna()
+			& (sow_age >= 1)
+			& (sow_age <= max_age)
+			& (dataframe["close"] < sow_origin)
+		)
+
+		dataframe["lps"] = (
+			alive_long
+			& first_near_long
+			& retrace_long.between(pb_min, pb_max)
+			& (dataframe["low"] > sos_origin)
+			& (dataframe["close"] >= sos_level * 0.995)
+			& vol_dry_long
+			& reclaim_long
+			& dataframe["bias_long_ok"]
+		)
+		dataframe["lpsy"] = (
+			alive_short
+			& first_near_short
+			& retrace_short.between(pb_min, pb_max)
+			& (dataframe["high"] < sow_origin)
+			& (dataframe["close"] <= sow_level * 1.005)
+			& vol_dry_short
+			& reclaim_short
+			& dataframe["bias_short_ok"]
+		)
+
+		dataframe["sos"] = sos_event
+		dataframe["sow"] = sow_event
+		dataframe["sos_level"] = sos_level
+		dataframe["sos_origin"] = sos_origin
+		dataframe["sow_level"] = sow_level
+		dataframe["sow_origin"] = sow_origin
+		dataframe["sos_age"] = sos_age
+		dataframe["sow_age"] = sow_age
+
+		for col in ["lps", "lpsy", "bias_long_ok", "bias_short_ok", "sos", "sow"]:
+			dataframe[col] = dataframe[col].fillna(False).astype(bool)
+
+		dataframe["setup_type"] = ""
+		dataframe.loc[dataframe["lps"], "setup_type"] = "LPS"
+		dataframe.loc[dataframe["lpsy"], "setup_type"] = "LPSY"
+		return dataframe
+
+	def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+		dataframe["enter_long"] = 0
+		dataframe["enter_short"] = 0
+		dataframe["enter_tag"] = ""
+		vol_ok = dataframe["volume"] > 0
+
+		if bool(self.use_lps_long.value):
+			cond = vol_ok & dataframe["lps"]
+			dataframe.loc[cond, ["enter_long", "enter_tag"]] = (1, "LPS")
+
+		if bool(self.use_lps_short.value):
+			cond = vol_ok & dataframe["lpsy"]
+			dataframe.loc[cond, ["enter_short", "enter_tag"]] = (1, "LPSY")
+
+		self._apply_regime_filter(dataframe)
+		return dataframe
+
+	def _apply_regime_filter(self, dataframe: DataFrame) -> None:
+		rm = getattr(self, "regime_mode", "all")
+		if rm == "all" or not self.bias_timeframe:
+			return
+		bs = f"_{self.bias_timeframe}"
+		bc, ec = f"bull_bias{bs}", f"bear_bias{bs}"
+		if bc not in dataframe.columns or ec not in dataframe.columns:
+			return
+		bull = dataframe[bc].fillna(False).astype(bool)
+		bear = dataframe[ec].fillna(False).astype(bool)
+		both = bull & bear
+		bull, bear = bull & ~both, bear & ~both
+		range_m = (~bull) & (~bear)
+		if rm == "bull":
+			mask = ~bull
+		elif rm == "bear":
+			mask = ~bear
+		elif rm == "range":
+			mask = ~range_m
+		elif rm == "trend":
+			mask = range_m
+		else:
+			return
+		dataframe.loc[mask, ["enter_long", "enter_short"]] = (0, 0)
+		dataframe.loc[mask, "enter_tag"] = ""
+
+	def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+		dataframe["exit_long"] = 0
+		dataframe["exit_short"] = 0
+		dataframe["exit_tag"] = ""
+		# 结构失效：收盘跌破 SOS 突破位 / 升破 SOW 突破位
+		exit_long = (
+			dataframe["sos_level"].notna()
+			& (dataframe["close"] < dataframe["sos_level"])
+			& (dataframe["close"] < dataframe["ema21"])
+		) | dataframe["sow"]
+		exit_short = (
+			dataframe["sow_level"].notna()
+			& (dataframe["close"] > dataframe["sow_level"])
+			& (dataframe["close"] > dataframe["ema21"])
+		) | dataframe["sos"]
+		dataframe.loc[exit_long.fillna(False), ["exit_long", "exit_tag"]] = (1, "lps_structure_fail")
+		dataframe.loc[exit_short.fillna(False), ["exit_short", "exit_tag"]] = (1, "lps_structure_fail")
+		return dataframe
+
+	def custom_stoploss(
+		self, pair: str, trade: Trade, current_time: datetime,
+		current_rate: float, current_profit: float, after_fill: bool, **kwargs,
+	) -> Optional[float]:
+		dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+		if dataframe.empty:
+			return None
+		last = dataframe.iloc[-1]
+		atr = float(last["atr"]) if pd.notna(last["atr"]) else 0.0
+		if atr <= 0 or trade.open_rate <= 0:
+			return None
+
+		atr_dist = float(self.atr_sl_mult.value) * atr
+		tag = trade.enter_tag or ""
+		buffer = atr * 0.15
+
+		if after_fill and trade.get_custom_data("struct_stop") is None:
+			if tag == "LPS" and pd.notna(last.get("sos_origin")):
+				trade.set_custom_data("struct_stop", float(last["sos_origin"]) - buffer)
+			elif tag == "LPSY" and pd.notna(last.get("sow_origin")):
+				trade.set_custom_data("struct_stop", float(last["sow_origin"]) + buffer)
+			elif trade.is_short:
+				trade.set_custom_data("struct_stop", float(last["high"]) + buffer)
+			else:
+				trade.set_custom_data("struct_stop", float(last["low"]) - buffer)
+
+		struct = trade.get_custom_data("struct_stop")
+		if trade.is_short:
+			atr_stop = trade.open_rate + atr_dist
+			stop_price = min(atr_stop, float(struct)) if struct is not None else atr_stop
+		else:
+			atr_stop = trade.open_rate - atr_dist
+			stop_price = max(atr_stop, float(struct)) if struct is not None else atr_stop
+
+		raw = abs(trade.open_rate - stop_price) / trade.open_rate
+		raw = min(max(raw, float(self.atr_sl_min.value)), float(self.atr_sl_max.value))
+		if struct is not None and tag in ("LPS", "LPSY"):
+			sl = stoploss_from_absolute(
+				stop_price, current_rate, is_short=trade.is_short, leverage=trade.leverage
+			)
+			return sl if sl and sl > 0 else None
+		return stoploss_from_open(
+			-raw, current_profit, is_short=trade.is_short, leverage=trade.leverage
+		) or None
+
+	def custom_exit(
+		self, pair: str, trade: Trade, current_time: datetime,
+		current_rate: float, current_profit: float, **kwargs,
+	) -> Optional[str]:
+		hours = (current_time - trade.open_date_utc).total_seconds() / 3600
+		if hours > float(self.time_stop_hours.value) and current_profit < 0:
+			return "wyckoff_time_stop"
+		if hours > float(self.time_stop_hours.value) * 2:
+			return "wyckoff_time_stop_max"
+		return None
+
+	def leverage(
+		self, pair: str, current_time: datetime, current_rate: float,
+		proposed_leverage: float, max_leverage: float, entry_tag: Optional[str],
+		side: str, **kwargs,
+	) -> float:
+		return min(self.lev, max_leverage)

research/lps_v2_failed/wyckoff_lps_v2_phase2_result.json

@@ -0,0 +1,150 @@
+{
+  "version": "LPS_V2",
+  "wfo": {
+    "train": {
+      "timerange": "20230101-20250101",
+      "profit_pct": -3.5049591933000004,
+      "trades": 3,
+      "dd_pct": 3.504959193300001,
+      "pf": 0.0,
+      "winrate": 0.0,
+      "final": 9649.50408067,
+      "fee_used": 0.0005,
+      "regime_loaded": "trend"
+    },
+    "validate": {
+      "timerange": "20250101-20260101",
+      "profit_pct": -1.6143743830000001,
+      "trades": 2,
+      "dd_pct": 1.614374382999995,
+      "pf": 0.0,
+      "winrate": 0.0,
+      "final": 9838.5625617,
+      "fee_used": 0.0005,
+      "regime_loaded": "trend"
+    },
+    "test": {
+      "timerange": "20260101-",
+      "profit_pct": 1.2174468187999996,
+      "trades": 2,
+      "dd_pct": 1.4924489317000007,
+      "pf": 1.81573767312309,
+      "winrate": 50.0,
+      "final": 10121.74468188,
+      "fee_used": 0.0005,
+      "regime_loaded": "trend"
+    },
+    "full": {
+      "timerange": "20230101-",
+      "profit_pct": -3.9055710949000004,
+      "trades": 7,
+      "dd_pct": 6.479119889400008,
+      "pf": 0.39720654015221873,
+      "winrate": 14.285714285714285,
+      "final": 9609.44289051,
+      "fee_used": 0.0005,
+      "regime_loaded": "trend"
+    }
+  },
+  "regimes": {
+    "trend": {
+      "profit_pct": -3.9055710949000004,
+      "trades": 7,
+      "dd_pct": 6.479119889400008,
+      "pf": 0.39720654015221873,
+      "winrate": 14.285714285714285,
+      "final": 9609.44289051,
+      "fee_used": 0.0005,
+      "regime_loaded": "trend"
+    },
+    "bull": {
+      "profit_pct": -5.0599199851,
+      "trades": 5,
+      "dd_pct": 5.059919985100005,
+      "pf": 0.0,
+      "winrate": 0.0,
+      "final": 9494.00800149,
+      "fee_used": 0.0005,
+      "regime_loaded": "bull"
+    },
+    "bear": {
+      "profit_pct": 1.2174468187999996,
+      "trades": 2,
+      "dd_pct": 1.4924489317000007,
+      "pf": 1.81573767312309,
+      "winrate": 50.0,
+      "final": 10121.74468188,
+      "fee_used": 0.0005,
+      "regime_loaded": "bear"
+    },
+    "range": {
+      "profit_pct": 0.0,
+      "trades": 0,
+      "dd_pct": 0.0,
+      "pf": 0.0,
+      "winrate": 0.0,
+      "final": 10000.0,
+      "fee_used": 0.0005,
+      "regime_loaded": "range"
+    },
+    "all": {
+      "profit_pct": -3.9055710949000004,
+      "trades": 7,
+      "dd_pct": 6.479119889400008,
+      "pf": 0.39720654015221873,
+      "winrate": 14.285714285714285,
+      "final": 9609.44289051,
+      "fee_used": 0.0005,
+      "regime_loaded": "all"
+    }
+  },
+  "cost_stress": {
+    "fee_5bps": {
+      "profit_pct": -3.9055710949000004,
+      "trades": 7,
+      "dd_pct": 6.479119889400008,
+      "pf": 0.39720654015221873,
+      "winrate": 14.285714285714285,
+      "final": 9609.44289051,
+      "fee_used": 0.0005,
+      "regime_loaded": "trend"
+    },
+    "fee_5bps+slip_5bps": {
+      "profit_pct": -4.5549168852,
+      "trades": 7,
+      "dd_pct": 7.020877735199993,
+      "pf": 0.3512325585213674,
+      "winrate": 14.285714285714285,
+      "final": 9544.50831148,
+      "fee_used": 0.001,
+      "regime_loaded": "trend"
+    },
+    "fee_10bps+slip_10bps": {
+      "profit_pct": -5.371762636800001,
+      "trades": 7,
+      "dd_pct": 8.1297357765,
+      "pf": 0.33924511392759654,
+      "winrate": 14.285714285714285,
+      "final": 9462.82373632,
+      "fee_used": 0.002,
+      "regime_loaded": "trend"
+    }
+  },
+  "target": {
+    "pf": 1.2,
+    "dd": 15.0,
+    "note": "LPS V2: 4h SOS→1h LPS; PF>1.2; ~5-15/yr"
+  },
+  "verdict": {
+    "full_pf": 0.39720654015221873,
+    "full_dd": 6.479119889400008,
+    "trades_per_year": 1.9444444444444444,
+    "net_mid_pf": 0.3512325585213674,
+    "target_pf_ok": false,
+    "target_dd_ok": true,
+    "freq_ok": false,
+    "regime_logic_ok": true,
+    "status": "FAIL",
+    "hypothesis": "4h native SOS → 1h LPS"
+  }
+}