# source: https://raw.githubusercontent.com/AlexEisie/GeniusTraderTaffy/782b201c5a48e7b21748de9d631ae9f0ef369154/strategies/TaffyMomentum.py # directory_url: https://github.com/AlexEisie/GeniusTraderTaffy/blob/main/strategies/ # User: AlexEisie # Repository: GeniusTraderTaffy # --------------------""" Github_AlexEisie_GeniusTraderTaffy__TaffyMomentum__20260819_144029 — 截面相对动量轮动（现货 / 只做多 / 1h / OKX） ==================================================================== 设计依据（2026-02~08 OKX 1h 数据实证，见 analysis/edge_study2.py）： * 7 日截面动量 top20% 币种的 48h 前瞻收益 +0.50%（top10% 达 +0.76%）， 显著高于 ~0.20% 的往返成本；中/尾部分组均为负。 * 4 日尺度方差比 ~0.78：市场整体均值回归，时序突破无边际， 但"相对强弱"横截面动量依然显著 —— 这是本策略唯一的 alpha 来源。 * 2026-08 市场处于"选择性轮动"阶段（BTC 横盘、强势山寨轮涨）， 与该边际的结构吻合。 核心规则： 入场：7d 动量截面排名 >= rank_enter(0.85) 且自身价格站上 EMA200， 且非抛物线拉升（24h 涨幅 < 20%）、市场广度 >= 0.25。 出场：排名跌破 rank_exit(0.55)（动量衰减）或 跌破 EMA200 且排名走弱； 辅以 ATR 自适应移动止损让利润奔跑。 风控：protections 处理连续止损与组合回撤；广度闸门挡住普跌崩盘。 排名的实现：populate_indicators 中通过 dataprovider 拉取整个白名单的 1h 收盘价，逐时间戳做横截面百分位排名。排名在 t 时刻只使用 <=t 的收盘， 无未来函数；类级缓存避免 O(n^2) 重复计算。 ==================================================================== """ from datetime import datetime import talib.abstract as ta from pandas import DataFrame, Series, isna import pandas as pd from freqtrade.persistence import Trade from freqtrade.strategy import ( DecimalParameter, IStrategy, stoploss_from_open, ) class Github_AlexEisie_GeniusTraderTaffy__TaffyMomentum__20260819_144029(IStrategy): INTERFACE_VERSION = 3 timeframe = "1h" can_short = False # 出场交给信号 + 移动止损，ROI 仅留极端兜底 minimal_roi = {"0": 10} stoploss = -0.10 use_custom_stoploss = True trailing_stop = False process_only_new_candles = True use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # 168h 动量 + EMA200，留缓冲 startup_candle_count = 400 # ---------------- 可调参数 ---------------- rank_enter = DecimalParameter(0.75, 0.95, default=0.85, decimals=2, space="buy", optimize=True) rank_exit = DecimalParameter(0.40, 0.70, default=0.55, decimals=2, space="sell", optimize=True) breadth_min = DecimalParameter(0.10, 0.40, default=0.25, decimals=2, space="buy", optimize=True) max_ret24 = DecimalParameter(0.10, 0.30, default=0.20, decimals=2, space="buy", optimize=True) atr_stop_mult = DecimalParameter(1.5, 3.5, default=2.5, decimals=1, space="sell", optimize=True) atr_trail_mult = DecimalParameter(1.0, 3.0, default=2.0, decimals=1, space="sell", optimize=True) # ---------------- 保护 ---------------- @property def protections(self): return [ {"method": "CooldownPeriod", "stop_duration_candles": 6}, { "method": "MaxDrawdown", "lookback_period_candles": 168, "trade_limit": 8, "stop_duration_candles": 24, "max_allowed_drawdown": 0.12, }, { "method": "StoplossGuard", "lookback_period_candles": 48, "trade_limit": 4, "stop_duration_candles": 24, "only_per_pair": False, }, ] # ---------------- 截面表缓存 ---------------- # 值: (cache_key, DataFrame[date x pair] rank, Series[date] breadth) _xs_cache = None def informative_pairs(self): pairs = self.dp.current_whitelist() return [(p, self.timeframe) for p in pairs] def _xs_tables(self): """跨币种 7d 动量百分位排名 + 市场广度。逐时间戳计算，无未来数据。""" pairs = tuple(sorted(self.dp.current_whitelist())) closes = {} last_dt = None for p in pairs: df = self.dp.get_pair_dataframe(p, self.timeframe) if df is None or df.empty: continue s = df.set_index("date")["close"] closes[p] = s if last_dt is None or s.index[-1] > last_dt: last_dt = s.index[-1] key = (pairs, last_dt) if self._xs_cache is not None and self._xs_cache[0] == key: return self._xs_cache[1], self._xs_cache[2] cm = pd.DataFrame(closes) mom = cm.pct_change(168, fill_method=None) rank = mom.rank(axis=1, pct=True) ema200 = cm.ewm(span=200, min_periods=100).mean() above = (cm > ema200) & cm.notna() breadth = above.sum(axis=1) / cm.notna().sum(axis=1).clip(lower=1) type(self)._xs_cache = (key, rank, breadth) return rank, breadth # ---------------- 指标 ---------------- def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200) dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) dataframe["atr_pct"] = dataframe["atr"] / dataframe["close"] dataframe["ret24"] = dataframe["close"].pct_change(24) rank, breadth = self._xs_tables() pair = metadata["pair"] if pair in rank.columns: dataframe["xs_rank"] = dataframe["date"].map(rank[pair]) else: dataframe["xs_rank"] = float("nan") dataframe["breadth"] = dataframe["date"].map(breadth) return dataframe # ---------------- 入场 ---------------- def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ (dataframe["xs_rank"] >= self.rank_enter.value) & (dataframe["close"] > dataframe["ema200"]) & (dataframe["ret24"] < self.max_ret24.value) & (dataframe["breadth"] >= self.breadth_min.value) & (dataframe["volume"] > 0), ["enter_long", "enter_tag"], ] = (1, "xs_mom") return dataframe # ---------------- 出场 ---------------- def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ (dataframe["xs_rank"] < self.rank_exit.value) & (dataframe["volume"] > 0), ["exit_long", "exit_tag"], ] = (1, "mom_decay") dataframe.loc[ (dataframe["close"] < dataframe["ema200"]) & (dataframe["xs_rank"] < 0.75) & (dataframe["volume"] > 0), ["exit_long", "exit_tag"], ] = (1, "trend_break") return dataframe # ---------------- ATR 自适应止损 ---------------- def custom_stoploss( self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, after_fill: bool, **kwargs, ) -> float | None: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if dataframe is None or len(dataframe) == 0: return None atr_pct = dataframe["atr_pct"].iloc[-1] if isna(atr_pct) or atr_pct <= 0: return None if current_profit < 0.05: # 初始阶段：相对开仓价的 ATR 止损，夹在 4%~10% initial = min(max(atr_pct * self.atr_stop_mult.value, 0.04), 0.10) return stoploss_from_open( -initial, current_profit, is_short=trade.is_short, leverage=trade.leverage ) # 盈利 5% 之后：相对现价的 ATR 移动止损，夹在 2.5%~6% trail = min(max(atr_pct * self.atr_trail_mult.value, 0.025), 0.06) return -trail plot_config = { "main_plot": {"ema200": {"color": "red"}}, "subplots": { "rank": {"xs_rank": {"color": "blue"}}, "breadth": {"breadth": {"color": "green"}}, }, }