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>
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jackyu66git
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# --- Do not remove these libs ---
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"""
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Wyckoff BTC — Market-State Gated Spring（Decision Layer）
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Spring = V1_BASELINE（FROZEN）
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Gate v1.1 = LOCKED default Decision rule:
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market_state in {accumulation, markup} -> allow Spring
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else -> block
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Soft-score 不进默认规则。勿改 Spring；勿全样本扫 Gate。
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"""
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from __future__ import annotations
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import json
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import logging
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import sys
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from pathlib import Path
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from pandas import DataFrame
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import pandas as pd
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_CHAN = Path(__file__).resolve().parents[1]
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if str(_CHAN) not in sys.path:
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sys.path.insert(0, str(_CHAN))
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from engine.market_state import apply_decision_gate, compute_market_state_8h # noqa: E402
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from freqtrade.strategy import merge_informative_pair # noqa: E402
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from Wyckoff_BTC_V1_BASELINE import Wyckoff_BTC_V1_BASELINE # noqa: E402
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logger = logging.getLogger(__name__)
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class Wyckoff_BTC_GATED(Wyckoff_BTC_V1_BASELINE):
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"""Baseline Spring + causal Market State Gate。"""
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STRATEGY_VERSION = "GATED_V1_1_LOCKED"
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SETUP_FAMILY = "SPRING_GATED"
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# LOCKED default — 研究脚本可临时改写，跑完必须恢复
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gate_mode: str = "state_set"
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gate_q_sum: float = 100.0
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gate_q_bad: float = 55.0
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decision_log_enabled: bool = True
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decision_log_path: str = str(_CHAN / "logs" / "wyckoff_decision_events.jsonl")
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe = super().populate_indicators(dataframe, metadata)
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pair = metadata["pair"]
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btf = self.bias_timeframe or "8h"
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raw8 = self.dp.get_pair_dataframe(pair=pair, timeframe=btf)
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st8 = compute_market_state_8h(raw8)
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# 覆盖默认门闩为当前 class 配置（可能已被脚本锁定）
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st8 = apply_decision_gate(
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st8,
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mode=str(self.gate_mode),
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q_sum=float(self.gate_q_sum),
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q_bad=float(self.gate_q_bad),
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)
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keep = [
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"date",
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"accumulation_score",
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"markup_score",
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"distribution_score",
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"markdown_score",
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"range_score",
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"market_state",
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"allow_spring",
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"allow_utad",
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"ema_slope",
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"dist_ema200",
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]
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st8 = st8[[c for c in keep if c in st8.columns]].copy()
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dataframe = merge_informative_pair(dataframe, st8, self.timeframe, btf, ffill=True)
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe = super().populate_entry_trend(dataframe, metadata)
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bs = f"_{self.bias_timeframe or '8h'}"
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allow_s = dataframe.get(f"allow_spring{bs}")
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allow_u = dataframe.get(f"allow_utad{bs}")
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if allow_s is None or allow_u is None:
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return dataframe
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allow_s = allow_s.fillna(False).astype(bool)
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allow_u = allow_u.fillna(False).astype(bool)
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block_long = (dataframe["enter_long"] == 1) & (~allow_s)
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block_short = (dataframe["enter_short"] == 1) & (~allow_u)
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self._log_decision_events(dataframe, metadata, allow_s, allow_u, bs)
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dataframe.loc[block_long, ["enter_long", "enter_tag"]] = (0, "")
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dataframe.loc[block_short, ["enter_short", "enter_tag"]] = (0, "")
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return dataframe
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def _decision_log_active(self) -> bool:
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if not bool(getattr(self, "decision_log_enabled", True)):
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return False
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config = getattr(self, "config", {}) or {}
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runmode = config.get("runmode")
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runmode_value = getattr(runmode, "value", str(runmode) if runmode is not None else "")
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if runmode_value:
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return runmode_value == "dry_run"
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return bool(config.get("dry_run", False))
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def _log_decision_events(
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self,
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dataframe: DataFrame,
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metadata: dict,
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allow_s: pd.Series,
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allow_u: pd.Series,
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bias_suffix: str,
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) -> None:
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if not self._decision_log_active():
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return
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pair = metadata.get("pair", "")
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long_candidates = dataframe["enter_long"] == 1
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short_candidates = dataframe["enter_short"] == 1
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if not bool(long_candidates.any() or short_candidates.any()):
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return
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seen = getattr(self, "_decision_log_seen", None)
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if seen is None:
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seen = set()
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self._decision_log_seen = seen
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events = []
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for idx in dataframe.index[long_candidates]:
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events.append(self._decision_event(dataframe.loc[idx], pair, "SPRING_LONG", bool(allow_s.loc[idx]), bias_suffix))
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for idx in dataframe.index[short_candidates]:
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events.append(self._decision_event(dataframe.loc[idx], pair, "UTAD_SHORT", bool(allow_u.loc[idx]), bias_suffix))
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path = Path(str(getattr(self, "decision_log_path", ""))).expanduser()
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try:
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path.parent.mkdir(parents=True, exist_ok=True)
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with path.open("a", encoding="utf-8") as handle:
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for event in events:
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key = (
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event["timestamp"],
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event["pair"],
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event["signal_type"],
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event["gate_version"],
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)
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if key in seen:
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continue
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seen.add(key)
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handle.write(json.dumps(event, ensure_ascii=False, sort_keys=True) + "\n")
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except OSError as exc:
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logger.warning("Decision log write failed: %s", exc)
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def _decision_event(self, row: pd.Series, pair: str, signal_type: str, allow: bool, bias_suffix: str) -> dict:
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state_col = f"market_state{bias_suffix}"
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bias_time_col = f"date{bias_suffix}"
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state = self._json_value(row.get(state_col))
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bias_bar_time = self._json_value(row.get(bias_time_col))
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state_missing = state in (None, "", "missing")
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block_reason = "" if allow else ("state_missing" if state_missing else "not_in_allow_set")
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event = {
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"timestamp": self._json_value(row.get("date")),
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"pair": pair,
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"signal_type": signal_type,
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"market_state": state if not state_missing else "missing",
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"allow": bool(allow),
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"gate_version": self.STRATEGY_VERSION,
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"baseline_signal": signal_type,
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"block_reason": block_reason,
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"bias_bar_time": bias_bar_time,
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"would_enter": True,
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"order_sent": bool(allow),
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}
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for score in [
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"accumulation_score",
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"markup_score",
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"distribution_score",
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"markdown_score",
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"range_score",
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]:
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event[score] = self._json_value(row.get(f"{score}{bias_suffix}"))
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return event
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@staticmethod
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def _json_value(value):
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if value is None:
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return None
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try:
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if pd.isna(value):
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return None
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except (TypeError, ValueError):
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pass
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if hasattr(value, "isoformat"):
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return value.isoformat()
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if hasattr(value, "item"):
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return value.item()
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return value
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