""" TrendRider Public v2.11.0 — Strat Ninja Edition Philosophy: Ride established trends with WIDE stoploss. Key insight: crypto swings 2-4% per hour. Stoploss must be >= 5-6%. Public version: - No external API calls (FNG, Bybit funding/OI) - No SQLite price alerts - No Cornix formatting - Leverage 1x (spot-safe) - All TA-Lib indicators and confidence scoring preserved """ import json import requests import talib.abstract as ta from datetime import datetime, timedelta from mypy.checker import defaultdict from freqtrade.persistence import Trade from freqtrade.rpc.api_server.api_trading import profit from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter, merge_informative_pair from pandas import DataFrame from functools import reduce import logging logger = logging.getLogger(__name__) class TEST11(IStrategy): INTERFACE_VERSION = 3 # --- ROI: Hyperopt-optimized (2026-03-23, 5 pairs) --- minimal_roi = { "0": 0.229, # 22.9% immediate "124": 0.136, # 13.6% after ~2h "290": 0.044, # 4.4% after ~5h "764": 0, # breakeven after ~12.7h } # --- Stoploss: WIDE for crypto volatility --- stoploss = -0.06 # 6% default (ATR-based custom stoploss overrides) use_custom_stoploss = False # --- Trailing Stop: WIDE --- trailing_stop = True trailing_stop_positive = 0.03 # 3% trail trailing_stop_positive_offset = 0.05 # Activate after +5% trailing_only_offset_is_reached = True #盈利达到百分之五之后出发止损，然后每涨到一个最新价格，止损线上涨百分之三锁定利润 # --- General --- timeframe = "1h" startup_candle_count = 210 process_only_new_candles = True can_short = False position_adjustment_enable = False # --- Protections (moved from config.json for Freqtrade 2026.2+) --- # protections = [ # { # "method": "CooldownPeriod", # "stop_duration": 20 #20分钟内不再开新单 # }, # { # "method": "StoplossGuard", # "lookback_period": 720, # "trade_limit": 3, # "stop_duration": 60, # "only_per_pair": False#720分钟内发生3次止损的话暂停交易60分钟 # }, # { # "method": "MaxDrawdown", # "lookback_period": 1440, # 回看过去 1440 分钟（24 小时） # "max_allowed_drawdown": 0.10, # 允许的最大回撤 10% # "stop_duration": 300, # 触发后暂停交易 300 分钟（5 小时） # "trade_limit": 5 # 至少交易 5 笔后才激活此检查 # } # ] # --- HyperOpt Results (applied from optimization session 2026-03-23) --- buy_params = { "adx_threshold": 27, "ema_fast": 15, "ema_slow": 29, "rsi_bounce": 28, "rsi_period": 12, "rsi_pullback_high": 58, "rsi_pullback_low": 45, "volume_factor": 1.031, } plot_config = { # ========== 主图 ========== 'main_plot': { # 优化后的快慢EMA（金叉信号核心） 'ema_15': {'color': '#00BFFF', 'label': 'EMA 15 (Fast)'}, # 快线 - 深天蓝 'ema_29': {'color': '#FFA500', 'label': 'EMA 29 (Slow)'}, # 慢线 - 橙色 # 中期趋势 'ema_50': {'color': '#32CD32', 'label': 'EMA 50'}, # 绿 # 长期趋势（牛熊分界线） 'ema_200': {'color': '#FF1493', 'label': 'EMA 200'}, # 粉红 # 布林带（上下轨虚线） 'bb_upper': {'color': '#9370DB', 'type': 'dash', 'label': 'BB Upper'}, 'bb_lower': {'color': '#9370DB', 'type': 'dash', 'label': 'BB Lower'}, # 日线EMA200（若存在，粗虚线） 'ema_200_1d': {'color': '#8B0000', 'type': 'dash', 'label': 'EMA 200 (1d)'}, }, # ========== 副图 ========== 'subplots': { # 1. RSI（优化周期 = 12） 'RSI': { 'rsi_12': {'color': '#7FFF00', 'label': 'RSI 12'}, # 查特酒绿 }, # 2. MACD 完整三线 'MACD': { 'macd': {'color': '#1E90FF', 'label': 'MACD'}, 'macdsignal': {'color': '#FF4500', 'label': 'Signal'}, 'macdhist': {'color': '#708090', 'type': 'bar', 'label': 'Histogram'}, }, # 3. ADX 趋势强度 + 方向线 'ADX/DI': { 'adx': {'color': '#FFD700', 'label': 'ADX (14)'}, # 金 'plus_di': {'color': '#00FA9A', 'label': '+DI'}, # 春绿 'minus_di': {'color': '#FF6347', 'label': '-DI'}, # 番茄红 }, # 4. 成交量及均量 'Volume': { 'volume': {'color': '#B0C4DE', 'type': 'bar', 'label': 'Volume'}, 'volume_ema': {'color': '#FF69B4', 'label': 'Volume EMA (20)'}, }, } } # Sell parameters: sell_params = { "rsi_exit": 79, } # --- HyperOpt Parameters --- ema_fast = IntParameter(5, 15, default=9, space="buy") ema_slow = IntParameter(15, 30, default=21, space="buy") rsi_period = IntParameter(10, 20, default=14, space="buy") rsi_pullback_low = IntParameter(30, 48, default=35, space="buy") rsi_pullback_high = IntParameter(52, 65, default=60, space="buy") rsi_bounce = IntParameter(25, 35, default=33, space="buy") rsi_exit = IntParameter(72, 85, default=78, space="sell") adx_threshold = IntParameter(20, 35, default=22, space="buy") volume_factor = DecimalParameter(1.0, 2.5, default=1.3, space="buy") # --- Leverage: 1x for Strat Ninja (spot-safe) --- leverage_value = 1 #一倍杠杆就是不适用杠杆 def leverage(self, pair: str, current_time, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str, side: str, **kwargs) -> float: return 1 def informative_pairs(self): pairs = self.dp.current_whitelist() if self.dp else [] informative = [] for pair in pairs: informative.append((pair, "4h")) informative.append((pair, "1d")) # BTC as market sentiment informative.append(("BTC/USDT:USDT", "1h")) informative.append(("BTC/USDT:USDT", "4h")) return informative #实盘或者模拟盘的时候会自动拉去这些数据，每个交易对的1天和四小时的数据 def _send_wecom(self, content: str) -> None: """发送 Markdown 消息到企业微信群机器人""" webhook_url = "https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=939cb90f-dd93-46d9-88fb-3fbdf4f57f75" headers = {"Content-Type": "application/json"} data = { "msgtype": "markdown", "markdown": {"content": content} } try: response = requests.post(webhook_url, data=json.dumps(data), headers=headers, timeout=10) logger.info(f"WeCom response: {response.json()}") except Exception as e: logger.error(f"Failed to send WeCom message: {e}") def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # EMAs (all periods for hyperopt ranges) for period in range(5, 31): #提前计算5到30的所以ema会用到 dataframe[f"ema_{period}"] = ta.EMA(dataframe, timeperiod=period) dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200) # RSI (all periods for hyperopt range 10-20) for period in range(10, 21): #提前计算10到20的所以rsi会用到,进行超参数优化的时候有个范围 dataframe[f"rsi_{period}"] = ta.RSI(dataframe, timeperiod=period) # ADX dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)#衡量趋势的强度（无论上涨还是下跌），值越高代表趋势越强。 dataframe["plus_di"] = ta.PLUS_DI(dataframe, timeperiod=14)#当 plus_di > minus_di 时表示上涨动能占优，是多头信号的一个确认条件 dataframe["minus_di"] = ta.MINUS_DI(dataframe, timeperiod=14) # MACD macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9) dataframe["macd"] = macd["macd"]#两者差值 dataframe["macdsignal"] = macd["macdsignal"] dataframe["macdhist"] = macd["macdhist"]#柱状图（直方图），反映动能变化的快慢 就是绿柱子红柱子 dataframe["macdhist_prev"] = macd["macdhist"].shift(1) # Bollinger Bands 布林带 bb = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0) dataframe["bb_upper"] = bb["upperband"] dataframe["bb_middle"] = bb["middleband"] dataframe["bb_lower"] = bb["lowerband"] # BB width for volatility regime dataframe["bb_width"] = (dataframe["bb_upper"] - dataframe["bb_lower"]) / (dataframe["bb_middle"] + 1e-10) #衡量波动率大小。带宽越大，价格波动越剧烈；带宽越小，市场越趋于横盘整理。 dataframe["bb_width_sma"] = ta.SMA(dataframe["bb_width"], timeperiod=50) #bb_width 显著高于 bb_width_sma 时，表示当前处于高波动状态；反之则处于低波动状态。 # Volume (fix #4: epsilon guard against division by zero) dataframe["volume_ema"] = ta.EMA(dataframe["volume"], timeperiod=20) dataframe["volume_ratio"] = dataframe["volume"] / (dataframe["volume_ema"] + 1e-10) #volume_ratio 值 含义 # > 1.0 当前成交量高于近期平均水平，属于放量 #= 1.0 成交量与平均水平持平 #< 1.0 成交量低于近期平均水平，属于缩量 #远大于 1.0（如 > 1.5） 显著放量，通常伴随重要价格变动 # OBV dataframe["obv"] = ta.OBV(dataframe)#反映资金流入流出的累积趋势。OBV 上升表示资金净流入，下降表示净流出 dataframe["obv_ema"] = ta.EMA(dataframe["obv"], timeperiod=20) #obv > obv_ema，表示当前 OBV 处于其均线上方，确认资金流向与趋势方向一致。 # ATR for dynamic stoploss dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) #衡量市场波动幅度。ATR 越大，说明价格波动越剧烈 # Regime dataframe["is_bull"] = ( (dataframe["close"] > dataframe["ema_200"]) & (dataframe["ema_50"] > dataframe["ema_200"]) ).astype(int) dataframe["is_bear"] = ( (dataframe["close"] < dataframe["ema_200"]) & (dataframe["ema_50"] < dataframe["ema_200"]) ).astype(int) #判断大趋势是上升还是下降 # --- LONG pullback detection --- ema_slow_key = f"ema_{self.ema_slow.value}" if ema_slow_key in dataframe.columns: dataframe["pullback_to_ema"] = ( (dataframe["low"] <= dataframe[ema_slow_key] * 1.02) & (dataframe["close"] > dataframe[ema_slow_key]) & (dataframe["close"] > dataframe["open"]) # Bullish candle ).astype(int) else: dataframe["pullback_to_ema"] = 0 #有效回踩判断 回踩均线 价格回踩慢速 EMA 并阳线反弹 # EMA50 support bounce (LONG) dataframe["ema50_bounce"] = ( (dataframe["low"] <= dataframe["ema_50"] * 1.01) & (dataframe["close"] > dataframe["ema_50"]) & (dataframe["close"] > dataframe["open"]) ).astype(int) # 有效回踩判断 回踩均线 价格回踩50天 EMA 并阳线反弹 对应深度回调 # --- Multi-Timeframe data --- if self.dp: # 4h data for current pair df_4h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='4h') if len(df_4h) > 0: df_4h['ema_50'] = ta.EMA(df_4h, timeperiod=50) df_4h['ema_200'] = ta.EMA(df_4h, timeperiod=200) df_4h['rsi_14'] = ta.RSI(df_4h, timeperiod=14) df_4h['adx'] = ta.ADX(df_4h, timeperiod=14) df_4h['is_bull'] = ( (df_4h['close'] > df_4h['ema_200']) & (df_4h['ema_50'] > df_4h['ema_200']) ).astype(int) dataframe = merge_informative_pair( dataframe, df_4h[['date', 'ema_50', 'ema_200', 'rsi_14', 'adx', 'is_bull']], self.timeframe, '4h', ffill=True ) else: dataframe['ema_50_4h'] = 0 dataframe['ema_200_4h'] = 0 dataframe['rsi_14_4h'] = 50 dataframe['adx_4h'] = 0 dataframe['is_bull_4h'] = 0 #将4小时的一些指标添加进一小时的框架中可以调用 看短期趋势 # Daily data for macro trend df_1d = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1d') if len(df_1d) > 0: df_1d['ema_200'] = ta.EMA(df_1d, timeperiod=200) dataframe = merge_informative_pair( dataframe, df_1d[['date', 'ema_200']], self.timeframe, '1d', ffill=True ) else: dataframe['ema_200_1d'] = 0 # 将1天的一些指标添加进一小时的框架中可以调用 看长期趋势 # BTC market sentiment df_btc = self.dp.get_pair_dataframe(pair='BTC/USDT:USDT', timeframe='1h') if len(df_btc) > 0: df_btc['btc_ema_200'] = ta.EMA(df_btc, timeperiod=200) df_btc['btc_ema_50'] = ta.EMA(df_btc, timeperiod=50) df_btc['btc_rsi'] = ta.RSI(df_btc, timeperiod=14) df_btc['btc_is_bull'] = ( (df_btc['close'] > df_btc['btc_ema_200']) & (df_btc['btc_ema_50'] > df_btc['btc_ema_200']) ).astype(int) dataframe = merge_informative_pair( dataframe, df_btc[['date', 'btc_ema_200', 'btc_ema_50', 'btc_rsi', 'btc_is_bull']], self.timeframe, '1h', ffill=True ) else: dataframe['btc_is_bull_1h'] = 1 dataframe['btc_rsi_1h'] = 50 #主要用于看bct也就是大盘的指标，添加到一小时框架中 else: # Safety fallback when dp is not available dataframe['is_bull_4h'] = dataframe['is_bull'] dataframe['rsi_14_4h'] = dataframe['rsi_14'] if 'rsi_14' in dataframe.columns else 50 dataframe['adx_4h'] = dataframe['adx'] dataframe['btc_is_bull_1h'] = 1 dataframe['btc_rsi_1h'] = 50 dataframe['ema_200_1d'] = 0 # Ensure columns exist (safety for backtesting edge cases) for col, default in [ ('is_bull_4h', 1), ('rsi_14_4h', 50), ('adx_4h', 20), ('btc_is_bull_1h', 1), ('btc_rsi_1h', 50), ('ema_200_1d', 0), ]: if col not in dataframe.columns: dataframe[col] = default dataframe['fng_value'] = 50 # 恐惧贪婪指数 → 中性 dataframe['funding_rate'] = 0.0 # 资金费率 → 无倾向 dataframe['funding_extreme'] = 0 # 资金费率极端标志 → 无 dataframe['oi_change'] = 0.0 # 持仓量变化 → 无变化 return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: rsi = f"rsi_{self.rsi_period.value}" # ========== LONG ENTRIES ========== # === LONG 1: Trend Pullback to EMA === 回踩慢速均线确认反弹时买入 conditions_pullback = [ dataframe["is_bull"] == 1, dataframe["pullback_to_ema"] == 1, dataframe[rsi] > self.rsi_pullback_low.value, dataframe[rsi] < self.rsi_pullback_high.value, dataframe["adx"] > self.adx_threshold.value, dataframe["volume_ratio"] > self.volume_factor.value, dataframe["plus_di"] > dataframe["minus_di"], dataframe["obv"] > dataframe["obv_ema"], dataframe["volume"] > 0, dataframe["btc_rsi_1h"] > 35, dataframe["fng_value"] >= 25, # Not extreme fear dataframe["fng_value"] <= 85, # Not extreme greed dataframe[rsi] < 70, # Not overbought ] # Daily EMA200 filter — helps filter bad entries if 'ema_200_1d' in dataframe.columns: conditions_pullback.append(dataframe["close"] > dataframe["ema_200_1d"]) dataframe.loc[ reduce(lambda x, y: x & y, conditions_pullback), ["enter_long", "enter_tag"] ] = (1, "trend_pullback") # === LONG 2: EMA50 Support Bounce === 回踩50小时均线确认反弹时买入 conditions_ema50 = [ dataframe["is_bull"] == 1, dataframe["ema50_bounce"] == 1, dataframe[rsi] > 30, dataframe[rsi] < 50, dataframe["adx"] > 20, dataframe["volume_ratio"] > 1.0, dataframe["macdhist"] > dataframe["macdhist"].shift(1), dataframe["volume"] > 0, dataframe["btc_rsi_1h"] > 35, dataframe["fng_value"] >= 25, dataframe["fng_value"] <= 85, dataframe[rsi] < 70, ] dataframe.loc[ reduce(lambda x, y: x & y, conditions_ema50), ["enter_long", "enter_tag"] ] = (1, "ema50_bounce") # === LONG 3: RSI Oversold Bounce === rsi超卖反弹的时候买入 conditions_rsi = [ dataframe["close"] > dataframe["ema_200"], dataframe[rsi].shift(1) < self.rsi_bounce.value, dataframe[rsi] > self.rsi_bounce.value, dataframe["close"] > dataframe["bb_lower"], dataframe["close"] > dataframe["open"], dataframe["volume_ratio"] > 0.8, dataframe["obv"] > dataframe["obv_ema"], dataframe["volume"] > 0, dataframe["btc_rsi_1h"] > 35, dataframe["fng_value"] >= 25, dataframe["fng_value"] <= 85, ] dataframe.loc[ reduce(lambda x, y: x & y, conditions_rsi), ["enter_long", "enter_tag"] ] = (1, "rsi_bounce") # === LONG 4: EMA Crossover (golden cross on fast EMAs) === 快速均线向上穿过慢速均线时买入 # ema金叉时买入 ema_fast_key = f"ema_{self.ema_fast.value}" ema_slow_key = f"ema_{self.ema_slow.value}" conditions_ema_cross = [ (dataframe[ema_fast_key] > dataframe[ema_slow_key]) & (dataframe[ema_fast_key].shift(1) <= dataframe[ema_slow_key].shift(1)), # crossed above dataframe[rsi] > 40, dataframe[rsi] < 75, dataframe["close"] > dataframe["ema_200"], dataframe["volume_ratio"] > 0.5, dataframe["volume"] > 0, dataframe["btc_rsi_1h"] > 35, dataframe["fng_value"] >= 25, dataframe["fng_value"] <= 85, ] dataframe.loc[ reduce(lambda x, y: x & y, conditions_ema_cross), ["enter_long", "enter_tag"] ] = (1, "ema_crossover") # === LONG 5: Bollinger Band Bounce (V4: tightened vol 0.3→0.7, added ADX>18) === 布林带下轨反弹买入 conditions_bb = [ dataframe["close"] <= dataframe["bb_lower"] * 1.005, # close within 0.5% of BB lower dataframe["close"] > dataframe["open"], # bullish candle (bounce) dataframe[rsi] < 45, dataframe["volume_ratio"] > 0.7, # V4: was 0.3, filter weak bounces dataframe["adx"] > 18, # V4: trend strength filter dataframe["volume"] > 0, dataframe["btc_rsi_1h"] > 35, dataframe["fng_value"] >= 25, dataframe["fng_value"] <= 85, ] dataframe.loc[ reduce(lambda x, y: x & y, conditions_bb), ["enter_long", "enter_tag"] ] = (1, "bb_bounce") # === LONG 6: MACD Histogram Reversal (tightened: RSI 40-60, EMA200 filter, volume 0.8x) === conditions_macd = [ (dataframe["macdhist"] > 0) & (dataframe["macdhist"].shift(1) <= 0), # histogram crossed above zero dataframe["close"] > dataframe["ema_50"], dataframe["close"] > dataframe["ema_200"], # confirm uptrend dataframe[rsi] > 40, dataframe[rsi] < 60, dataframe["adx"] > 15, dataframe["volume_ratio"] > 0.8, # volume confirmation dataframe["volume"] > 0, dataframe["btc_rsi_1h"] > 35, dataframe["fng_value"] >= 25, dataframe["fng_value"] <= 85, ] dataframe.loc[ reduce(lambda x, y: x & y, conditions_macd), ["enter_long", "enter_tag"] ] = (1, "macd_reversal") return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: rsi = f"rsi_{self.rsi_period.value}" ema_fast = f"ema_{self.ema_fast.value}" ema_slow = f"ema_{self.ema_slow.value}" # ========== LONG EXITS ========== # EXIT 1: RSI very overbought dataframe.loc[ (dataframe[rsi] > self.rsi_exit.value) & (dataframe["volume"] > 0), ["exit_long", "exit_tag"] ] = (1, "rsi_overbought") # EXIT 2: Bearish EMA cross with MACD confirmation dataframe.loc[ (dataframe[ema_fast] < dataframe[ema_slow]) & (dataframe[ema_fast].shift(1) >= dataframe[ema_slow].shift(1)) & (dataframe["macdhist"] < 0) & (dataframe[rsi] > 50) & (dataframe["volume"] > 0), ["exit_long", "exit_tag"] ] = (1, "ema_bearish_cross") # EXIT 3: Price drops below EMA200 by 1%+ (trend broken, softened to avoid premature exits) dataframe.loc[ (dataframe["close"] < dataframe["ema_200"] * 0.99) & (dataframe["close"].shift(1) >= dataframe["ema_200"].shift(1)) & (dataframe["volume"] > 0), ["exit_long", "exit_tag"] ] = (1, "trend_broken") # EXIT 4 (V4): Trend early warning — RSI overbought reversal near EMA200 # Catches trend exhaustion before price breaks support, saving avg -3% vs trend_broken dataframe.loc[ (dataframe["close"] < dataframe["ema_200"] * 0.995) & # within 0.5% of breaking (dataframe[rsi] > 72) & # exhausted (dataframe["macdhist"] < dataframe["macdhist"].shift(1)) & # momentum dropping (dataframe["volume"] > 0), ["exit_long", "exit_tag"] ] = (1, "trend_early_warning") return dataframe # def bot_loop_start(self, current_time: datetime, **kwargs) -> None: # ndays = 20*6 # x_minutes = 60*8 # current_timestamp = current_time.timestamp() # last_execution_timestame = getattr(self,'last_execution_timestame',0) # if current_timestamp - last_execution_timestame >= round(x_minutes,0): # closed_trades = Trade.get_trades_proxy(is_open=False) # profit_by_category = defaultdict(float) # for trade in closed_trades: # profit_by_category["空单" if trade.is_short else "多单"] += trade.close_profit_abs # profit_by_category[trade.enter_tag] += trade.close_profit_abs # # for category in profit_by_category.items(): # logger.info(f"最近{ndays}天,{category}收益:{profit}") # # self.last_execution_timestame = current_timestamp def _calc_confidence(self, last: dict) -> tuple: #打分机制 """Calculate signal confidence based on weighted indicator alignment. Max score ~17.5. Returns (level_str, bar_str, details_list, numeric_level). """ score = 0.0 details = [] rsi_key = f"rsi_{self.rsi_period.value}" rsi_val = last.get(rsi_key, 50) # RSI in healthy zone (not overbought): +1.5 if 35 < rsi_val < 60: score += 1.5 details.append("RSI healthy") # Strong trend (ADX): +2.5 strong, +1.5 moderate adx_val = last.get('adx', 0) if adx_val > 30: score += 2.5 details.append("Strong trend") elif adx_val > self.adx_threshold.value: score += 1.5 details.append("Moderate trend") # Volume confirmation: +2.5 high, +1.5 normal vol_ratio = last.get('volume_ratio', 0) if vol_ratio > 1.5: score += 2.5 details.append("High volume") elif vol_ratio > 1.0: score += 1.5 details.append("Normal volume") # MACD positive histogram: +1.5, bonus +0.5 if rising macd_hist = last.get('macdhist', 0) macd_hist_prev = last.get('macdhist_prev', 0) if macd_hist > 0: score += 1.5 if macd_hist > macd_hist_prev: score += 0.5 details.append("MACD positive+rising") else: details.append("MACD positive") # OBV rising AND above EMA: +1.5 if last.get('obv', 0) > last.get('obv_ema', 0): score += 1.5 details.append("OBV rising") # BTC healthy (RSI 40-70): +1.5 btc_rsi = last.get('btc_rsi_1h', 50) if 40 < btc_rsi < 70: score += 1.5 details.append("BTC healthy") # 4h trend alignment AND ADX_4h > 20: +1.5 if last.get('is_bull_4h', 0) == 1 and last.get('adx_4h', 0) > 20: score += 1.5 details.append("4H trend aligned") # Bollinger Band position (close near lower = good for long): +1 close = last.get('close', 0) bb_lower = last.get('bb_lower', 0) bb_upper = last.get('bb_upper', 0) bb_range = bb_upper - bb_lower if bb_upper > bb_lower else 1 if bb_lower > 0 and close > 0: bb_position = (close - bb_lower) / bb_range if bb_position < 0.35: score += 1.0 details.append("Near BB lower") # Plus_DI > Minus_DI spread > 10: +1 plus_di = last.get('plus_di', 0) minus_di = last.get('minus_di', 0) if plus_di - minus_di > 10: score += 1.0 details.append("Strong DI spread") # FNG bonus: neutral/healthy (40-60): +1 fng_val = last.get('fng_value', 50) if 40 <= fng_val <= 60: score += 1.0 details.append("FNG neutral") # On-chain: healthy funding rate: +1 funding = last.get('funding_rate', 0) if abs(funding) < 0.0001: # Normal funding score += 1 details.append("Healthy funding") # Smooth mapping to 1-10 (max score ~17.5) numeric = max(1, min(10, round(score * 10 / 17.5))) # Level label # if numeric >= 8: # level = "STRONG" # elif numeric >= 6: # level = "GOOD" # elif numeric >= 4: # level = "MEDIUM" # else: # level = "WEAK" if numeric >= 6: level = "STRONG" elif numeric >= 4: level = "GOOD" elif numeric >= 2: level = "MEDIUM" else: level = "WEAK" # Dynamic bar bar = "|" * numeric + "-" * (10 - numeric) + f" {numeric}/10" return level, bar, details, numeric #返回四个数据分别是等级，打了多少分，什么类型的加分， def _market_context(self, last: dict) -> str: """Generate market context string.""" btc_rsi = last.get('btc_rsi_1h', 50) btc_bull = last.get('btc_is_bull_1h', 0) bull_4h = last.get('is_bull_4h', 0) if btc_bull and btc_rsi > 55: btc_status = "Bullish" elif btc_rsi > 40: btc_status = "Neutral" else: btc_status = "Bearish" tf_4h = "Uptrend" if bull_4h else "Downtrend" parts = [f"BTC: {btc_status} (RSI {btc_rsi:.0f})", f"4H: {tf_4h}"] return " | ".join(parts) def _get_market_regime(self, last: dict) -> str: """Detect market regime from ADX + EMA200 + BB width.""" adx_val = last.get('adx', 0) ema_200 = last.get('ema_200', 0) close = last.get('close', 0) is_bull = last.get('is_bull', 0) bb_width = last.get('bb_width', 0) bb_width_sma = last.get('bb_width_sma', 0) high_vol = bb_width > bb_width_sma * 1.5 if bb_width_sma > 0 else False if adx_val < 20: return "Ranging (High Vol)" if high_vol else "Ranging" elif is_bull and close > ema_200: return "Trending Bull" else: return "Trending Bear (High Vol)" if high_vol else "Trending Bear" def custom_exit(self, pair: str, trade, current_time: datetime, #类似于时间止盈机制 current_rate: float, current_profit: float, **kwargs): """V4 cascading early exit — stop bleeding before 24h timeout. Real dry-run data (51 trades): time_exit_24h cost -$13.01 across 9 trades, avg -2.85% loss after holding full 24h. Cascade catches losers earlier: - 2h: cut if -1.5% (already broken thesis) - 4h: cut if red (no recovery momentum) - 8h: cut if not at +0.5% (dead trade) - 16h: cut if not at +1% (final mercy) """ duration_hours = (current_time - trade.open_date_utc).total_seconds() / 3600 if duration_hours >= 2 and current_profit < -0.015: return "early_loss_cut_2h" if duration_hours >= 4 and current_profit < 0: return "early_loss_cut_4h" if duration_hours >= 8 and current_profit < 0.005: return "early_loss_cut_8h" if duration_hours >= 16 and current_profit < 0.01: return "early_loss_cut_16h" if duration_hours >= 24: return "time_exit_24h" return None def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time: datetime, entry_tag: str | None, side: str, **kwargs) -> bool: # Calculate levels (LONG only, can_short = False) sl_price = rate * (1 + self.stoploss) tp2_price = rate * 1.05 # +5% leverage = self.leverage_value side_str = "LONG" # Risk/reward ratio risk = abs(rate - sl_price) reward = abs(tp2_price - rate) rr_ratio = reward / risk if risk > 0 else 0 # Entry reason mapping reasons = { "trend_pullback": "Pullback to EMA in uptrend, bounce with volume confirmation", "ema50_bounce": "Deep pullback to EMA50, bounce with rising MACD", "rsi_bounce": "RSI oversold, bounce from lower Bollinger in bull market", "ema_crossover": "EMA9 crossed above EMA16, golden cross with trend confirmation", "bb_bounce": "Price bounced from lower Bollinger Band with oversold RSI", "macd_reversal": "MACD histogram turned positive, momentum shift above EMA50", } reason = reasons.get(entry_tag, entry_tag or "Signal") # Get current indicators for context dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if len(dataframe) > 0: last = dataframe.iloc[-1] rsi_key = f"rsi_{self.rsi_period.value}" rsi_val = last.get(rsi_key, 0) adx_val = last.get("adx", 0) vol_ratio = last.get("volume_ratio", 0) macd_hist = last.get("macdhist", 0) else: rsi_val = adx_val = vol_ratio = macd_hist = 0 last = {} # Confidence & market context conf_level, conf_bar, conf_details, conf_numeric = self._calc_confidence(last) market_ctx = self._market_context(last) regime = self._get_market_regime(last) # --- REJECT WEAK SIGNALS --- min_conf = 5 if "Bear" in regime else 4 if conf_numeric < min_conf: # logger.info(f"Rejecting signal for {pair}: confidence {conf_numeric}/10 < {min_conf} (regime: {regime})") return False # --- Main Telegram Signal --- msg = ( f"*TRENDRIDER SIGNAL*\n" f"{'='*28}\n" f"*{pair}* | *{side_str}* | {leverage}x\n" f"{'='*28}\n\n" f"*Entry:* `{rate:.2f}` USDT\n" f"*Stop Loss:* `{sl_price:.2f}` ({self.stoploss*100:+.1f}%)\n" f" R:R = 1:{rr_ratio:.1f}\n\n" f"*Confidence:* {conf_level}\n" f" [{conf_bar}]\n" f" {', '.join(conf_details)}\n\n" f"*Regime:* {regime}\n" f"*Indicators:*\n" f" RSI: {rsi_val:.1f} | ADX: {adx_val:.1f}\n" f" Volume: {vol_ratio:.2f}x | MACD: {'+' if macd_hist > 0 else '-'}\n\n" f"*Market:* {market_ctx}\n\n" f"*Why:* {reason}\n" f"{'='*28}\n" f"_TrendRider AI_" ) #self.dp.send_msg(msg, always_send=True) self._send_wecom(msg) return True def confirm_trade_exit(self, pair: str, trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: # Calculate results (LONG only) profit_pct = ((rate - trade.open_rate) / trade.open_rate) * 100 * trade.leverage duration_hours = (current_time - trade.open_date_utc).total_seconds() / 3600 # Exit reason mapping exit_reasons = { "roi": "ROI target reached", "stop_loss": "Stop Loss hit", "trailing_stop_loss": "Trailing Stop", "exit_signal": "Exit signal", "rsi_overbought": "RSI overbought (>81)", "ema_bearish_cross": "EMA bearish crossover", "trend_broken": "Trend broken (below EMA200)", "force_exit": "Force exit", "time_exit_24h": "Time exit (24h, low profit)", } reason_text = exit_reasons.get(exit_reason, exit_reason) # Result line if profit_pct > 0: result_line = f"+{profit_pct:.2f}%" else: result_line = f"{profit_pct:.2f}%" # Duration formatting if duration_hours < 1: dur_str = f"{int(duration_hours * 60)}m" elif duration_hours < 24: dur_str = f"{duration_hours:.1f}h" else: dur_str = f"{duration_hours/24:.1f}d" msg = ( f"*TRADE CLOSED* {'WIN' if profit_pct > 0 else 'LOSS'}\n" f"{'='*25}\n" f"*{pair}* | LONG | {trade.leverage}x\n" f"{'='*25}\n\n" f"*Entry:* `{trade.open_rate:.2f}`\n" f"*Exit:* `{rate:.2f}`\n" f"*Result:* *{result_line}*\n" f"*Duration:* {dur_str}\n" f"*Reason:* {reason_text}\n" f"*Max price:* `{trade.max_rate:.2f}`\n" f"{'='*25}\n" f"_TrendRider AI_" ) #self.dp.send_msg(msg, always_send=True) self._send_wecom(msg) return True