# source: https://raw.githubusercontent.com/RoboticAutomations/freqtradestrategies/52c4bd40414645f079a980cf4486dc7af4061efb/dirty%20LA/from-AIRA/pivot-supertrend/AlexBandSniperV6553.py # directory_url: https://github.com/RoboticAutomations/freqtradestrategies/blob/main/dirty LA/from-AIRA/pivot-supertrend/ # User: RoboticAutomations # Repository: freqtradestrategies # --------------------# AlexBandSniper V6551 - Real-time divergence detection with improved filtering # Based on V655 but with tighter signal quality to reduce noise # --- Do not remove these libs --- from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date import logging from datetime import timedelta, datetime import datetime import numpy as np import pandas as pd pd.options.mode.chained_assignment = None from pandas import DataFrame, Series from technical.util import resample_to_interval, resampled_merge from freqtrade.strategy import (IStrategy, BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, RealParameter, merge_informative_pair, stoploss_from_open, stoploss_from_absolute, merge_informative_pair) from freqtrade.persistence import Trade from typing import List, Tuple, Optional from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal # -------------------------------- import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from collections import deque import warnings warnings.simplefilter(action="ignore", category=pd.errors.PerformanceWarning) logger = logging.getLogger(__name__) class PlotConfig(): def __init__(self): self.config = { 'main_plot': { resample('bollinger_upperband'): {'color': 'rgba(4,137,122,0.7)'}, resample('kc_upperband'): {'color': 'rgba(4,146,250,0.7)'}, resample('kc_middleband'): {'color': 'rgba(4,146,250,0.7)'}, resample('kc_lowerband'): {'color': 'rgba(4,146,250,0.7)'}, resample('bollinger_lowerband'): { 'color': 'rgba(4,137,122,0.7)', 'fill_to': resample('bollinger_upperband'), 'fill_color': 'rgba(4,137,122,0.07)' }, resample('ema9'): {'color': 'purple'}, resample('ema20'): {'color': 'yellow'}, resample('ema50'): {'color': 'red'}, resample('ema200'): {'color': 'white'}, }, 'subplots': { "ATR": { resample('atr'): {'color': 'firebrick'} }, "Signal Strength": { resample('bull_signal_strength'): {'color': 'green'}, resample('bear_signal_strength'): {'color': 'crimson'}, resample('signal_strength'): {'color': 'blue'} }, "Squeeze": { resample('squeeze_on'): {'color': 'orange'}, resample('squeeze_momentum'): {'color': 'cyan'} } } } def add_total_divergences_in_config(self, dataframe): self.config['main_plot'][resample("total_bullish_divergences")] = { "plotly": { 'mode': 'markers+text', 'text': resample("total_bullish_divergences_count"), 'hovertext': resample("total_bullish_divergences_names"), 'textfont': {'size': 11, 'color': 'green'}, 'textposition': 'bottom center', 'marker': { 'symbol': 'diamond', 'size': 11, 'line': {'width': 2}, 'color': 'green' } } } self.config['main_plot'][resample("total_bearish_divergences")] = { "plotly": { 'mode': 'markers+text', 'text': resample("total_bearish_divergences_count"), 'hovertext': resample("total_bearish_divergences_names"), 'textfont': {'size': 11, 'color': 'crimson'}, 'textposition': 'top center', 'marker': { 'symbol': 'diamond', 'size': 11, 'line': {'width': 2}, 'color': 'crimson' } } } return self class Github_RoboticAutomations_freqtradestrategies__AlexBandSniperV6553__20260915_233254(IStrategy): """ ╔════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════╗ ║ ║ ║ ██████╗ ██╗ ███████╗██╗ ██╗ ██████╗██████╗ ██╗ ██╗██████╗ ████████╗ ██████╗ ██╗ ██╗██╗███╗ ██╗ ██████╗ ║ ║ ██╔══██╗██║ ██╔════╝╚██╗██╔╝██╔════╝██╔══██╗╚██╗ ██╔╝██╔══██╗╚══██╔══╝██╔═══██╗██║ ██╔╝██║████╗ ██║██╔════╝ ║ ║ ███████║██║ █████╗ ╚███╔╝ ██║ ██████╔╝ ╚████╔╝ ██████╔╝ ██║ ██║ ██║█████╔╝ ██║██╔██╗ ██║██║ ███╗ ║ ║ ██╔══██║██║ ██╔══╝ ██╔██╗ ██║ ██╔══██╗ ╚██╔╝ ██╔═══╝ ██║ ██║ ██║██╔═██╗ ██║██║╚██╗██║██║ ██║ ║ ║ ██║ ██║███████╗███████╗██╔╝ ██╗╚██████╗██║ ██║ ██║ ██║ ██║ ╚██████╔╝██║ ██╗██║██║ ╚████║╚██████╔╝ ║ ║ ╚═╝ ╚═╝╚══════╝╚══════╝╚═╝ ╚═╝ ╚═════╝╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚═════╝ ╚═╝ ╚═╝╚═╝╚═╝ ╚═══╝ ╚═════╝ ║ ║ ║ ║ AlexBandSniperV6 ║ ║ ║ ║ ║ ║ ║ ╚════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════╝ ╔═══════════════════════════════════════════════════════════════╗ ║ ║ ║ AlexBandSniper V6551 ║ ║ ║ ║ REAL-TIME DIVERGENCE — with IMPROVED FILTERING ║ ║ ║ ║ Based on V655 but with tighter signal quality to reduce ║ ║ noise and false entries while maintaining the real-time ║ ║ divergence approach (no lookahead bias). ║ ║ ║ ║ CHANGES FROM V655: ║ ║ - Tighter Fast signal thresholds (strength >= 2, div >= 3) ║ ║ - Choppiness filter added to ALL signal types ║ ║ - Volatility filter added to ALL signal types ║ ║ - No-conflict rule (no opposite signal strength) ║ ║ - Higher min_divergence_count (2 → 3) ║ ║ - Larger window default (7 → 9) for more significant swings║ ║ ║ ║ WHY: V655 was catching too many micro-swings. The real-time ║ ║ approach is correct but needs stronger quality gates since ║ ║ we no longer have the natural "significance filter" that ║ ║ pivot-based detection provided. ║ ║ ║ ║ Result: Fewer trades, higher quality, same honest backtest. ║ ║ ║ ║ - V654 _rt shift columns (no longer needed) ║ ║ ║ ║ Added: ║ ║ - realtime_divergence_scan() — fully vectorized ║ ║ - add_realtime_divergences() method on the strategy ║ ║ - Per-pair divergence-bar count log so you can see ║ ║ how many bars actually had divergences in the dataframe ║ ║ ║ ║ Tunable params (existing IntParameters reused): ║ ║ - window (5-15, default 7) → swing tightness ║ ║ - index_range (20-50, default 30) → max lookback distance ║ ║ ║ ╚═══════════════════════════════════════════════════════════════╝ """ INTERFACE_VERSION = 3 def version(self) -> str: return "v65513" class HyperOpt: def stoploss_space(): return [SKDecimal(-0.15, -0.03, decimals=2, name='stoploss')] def max_open_trades_space() -> List[Dimension]: return [Integer(3, 8, name='max_open_trades')] def trailing_space() -> List[Dimension]: return [ Categorical([True], name='trailing_stop'), SKDecimal(0.02, 0.3, decimals=2, name='trailing_stop_positive'), SKDecimal(0.03, 0.1, decimals=2, name='trailing_stop_positive_offset_p1'), Categorical([True, False], name='trailing_only_offset_is_reached'), ] # ── ROI ────────────────────────────────────────────────────── minimal_roi = { "0": 0.99 # Disabled — custom_exit handles everything } # ── Risk ───────────────────────────────────────────────────── stoploss = -0.15 can_short = True use_custom_stoploss = False leverage_value = 5.0 # ── Trailing (disabled — custom_exit does trailing) ────────── trailing_stop = False use_custom_exits = True # ── Timeframe ──────────────────────────────────────────────── timeframe = '15m' timeframe_minutes = timeframe_to_minutes(timeframe) process_only_new_candles = True use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # ═════════════════════════════════════════════════════════════ # OPTIMIZABLE PARAMETERS # ═════════════════════════════════════════════════════════════ # -- Signal quality -- min_divergence_count = IntParameter(2, 5, default=3, space='buy', optimize=True, load=True) min_signal_strength = IntParameter(3, 10, default=4, space='buy', optimize=True, load=True) volume_threshold = DecimalParameter(1.1, 2.5, default=1.1, decimals=1, space='buy', optimize=True, load=True) # -- Market condition -- rsi_overbought = DecimalParameter(65.0, 85.0, default=80.0, decimals=1, space='buy', optimize=True, load=True) rsi_oversold = DecimalParameter(15.0, 35.0, default=15.0, decimals=1, space='buy', optimize=True, load=True) adx_threshold = IntParameter(20, 45, default=20, space='buy', optimize=True, load=True) # -- Volatility -- max_volatility = DecimalParameter(0.015, 0.035, default=0.025, decimals=3, space='buy', optimize=True, load=True) min_volatility = DecimalParameter(0.003, 0.008, default=0.005, decimals=3, space='buy', optimize=True, load=True) # -- Exit -- rsi_exit_overbought = DecimalParameter(70.0, 90.0, default=80.0, decimals=1, space='sell', optimize=True, load=True) rsi_exit_oversold = DecimalParameter(10.0, 30.0, default=20.0, decimals=1, space='sell', optimize=True, load=True) adx_exit_threshold = IntParameter(15, 30, default=20, space='sell', optimize=True, load=True) # -- Trend confirmation -- trend_strength_threshold = IntParameter(20, 40, default=25, space='buy', optimize=True, load=True) # -- Technical -- window = IntParameter(5, 15, default=9, space="buy", optimize=True, load=True) index_range = IntParameter(20, 50, default=30, space='buy', optimize=True, load=True) # ── Fast signals: V62 original logic (shift(1), div>=2) ───── # These showed perfectly on chart — reverted by Alex's request. # ── V64 NEW: Squeeze parameters ───────────────────────────── squeeze_lookback = IntParameter(3, 10, default=6, space='buy', optimize=True, load=True) squeeze_momentum_threshold = DecimalParameter(0.0, 0.005, default=0.001, decimals=4, space='buy', optimize=True, load=True) # ── V64 NEW: Momentum Burst parameters ────────────────────── momentum_rsi_cross = DecimalParameter(40.0, 55.0, default=50.0, decimals=1, space='buy', optimize=True, load=True) momentum_volume_mult = DecimalParameter(1.3, 2.5, default=1.5, decimals=1, space='buy', optimize=True, load=True) momentum_macd_threshold = DecimalParameter(0.0, 0.5, default=0.0, decimals=2, space='buy', optimize=True, load=True) # ── V64 NEW: Choppiness filter ────────────────────────────── chop_threshold = DecimalParameter(55.0, 70.0, default=61.8, decimals=1, space='buy', optimize=True, load=True) # ── V64 NEW: ATR-based exit multipliers ───────────────────── trail_atr_multiplier = DecimalParameter(0.5, 2.0, default=1.0, decimals=1, space='sell', optimize=True, load=True) profit_floor_pct = DecimalParameter(0.3, 0.6, default=0.5, decimals=1, space='sell', optimize=True, load=True) # ── Protection ─────────────────────────────────────────────── startup_candle_count: int = 200 cooldown_lookback = IntParameter(2, 48, default=5, space="protection", optimize=True) stop_duration = IntParameter(12, 200, default=20, space="protection", optimize=True) use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True) use_cooldown_protection = BooleanParameter(default=True, space="protection", optimize=True) use_max_drawdown_protection = BooleanParameter(default=True, space="protection", optimize=True) max_drawdown_lookback = IntParameter(100, 300, default=200, space="protection", optimize=True) max_drawdown_trade_limit = IntParameter(5, 15, default=10, space="protection", optimize=True) max_drawdown_stop_duration = IntParameter(1, 5, default=2, space="protection", optimize=True) max_allowed_drawdown = DecimalParameter(0.08, 0.25, default=0.15, decimals=2, space="protection", optimize=True) stoploss_guard_lookback = IntParameter(30, 80, default=50, space="protection", optimize=True) stoploss_guard_trade_limit = IntParameter(2, 6, default=3, space="protection", optimize=True) stoploss_guard_only_per_pair = BooleanParameter(default=True, space="protection", optimize=True) # ── Orders ─────────────────────────────────────────────────── order_types = { 'entry': 'market', 'exit': 'market', 'stoploss': 'limit', 'stoploss_on_exchange': True } order_time_in_force = { 'entry': 'gtc', 'exit': 'gtc' } plot_config = None def __init__(self, config: dict, **kwargs): super().__init__(config, **kwargs) self.profit_peaks = {} def get_ticker_indicator(self): return int(self.timeframe[:-1]) def informative_pairs(self): pairs = self.dp.current_whitelist() return [(pair, '1h') for pair in pairs] # ═════════════════════════════════════════════════════════════ # INDICATORS # ═════════════════════════════════════════════════════════════ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ All indicators — V64 adds squeeze detection + momentum burst columns """ # ── MULTI-TIMEFRAME: 1H ───────────────────────────────── try: if hasattr(self.dp, 'runmode') and self.dp.runmode.value in ['backtest', 'hyperopt']: informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1h') else: try: informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1h') except Exception: informative_1h = None if (informative_1h is not None and len(informative_1h) > 50 and not informative_1h.empty and 'close' in informative_1h.columns): try: informative_1h['ema50_1h'] = ta.EMA(informative_1h, timeperiod=50) informative_1h['ema200_1h'] = ta.EMA(informative_1h, timeperiod=200) informative_1h['trend_1h'] = ta.EMA(informative_1h, timeperiod=21) informative_1h['trend_strength_1h'] = ta.ADX(informative_1h) informative_1h['rsi_1h'] = ta.RSI(informative_1h) informative_1h = informative_1h.bfill().ffill() dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, '1h', ffill=True) logger.info(f"Merged 1h data for {metadata['pair']}") except Exception as merge_error: logger.warning(f"Failed to merge 1h data for {metadata['pair']}: {merge_error}") self._add_dummy_1h_columns(dataframe) else: self._add_dummy_1h_columns(dataframe) except Exception as e: logger.warning(f"Error accessing 1h data for {metadata['pair']}: {e}") self._add_dummy_1h_columns(dataframe) # ── 15M CORE INDICATORS ────────────────────────────────── informative = dataframe.copy() # Volume try: informative['volume_sma'] = informative['volume'].rolling(window=20, min_periods=1).mean() informative['volume_ratio'] = informative['volume'] / informative['volume_sma'] informative['volume_ratio'] = informative['volume_ratio'].fillna(1.0) except: informative['volume_sma'] = informative['volume'] informative['volume_ratio'] = 1.0 # Volatility / ATR try: informative['atr'] = qtpylib.atr(informative, window=14, exp=False) informative['volatility'] = informative['atr'] / informative['close'] informative['volatility'] = informative['volatility'].fillna(0.01) except: informative['atr'] = informative['close'] * 0.02 informative['volatility'] = 0.01 # Momentum suite try: informative['rsi'] = ta.RSI(informative) informative['stoch'] = ta.STOCH(informative)['slowk'] informative['roc'] = ta.ROC(informative) informative['uo'] = ta.ULTOSC(informative) informative['ao'] = qtpylib.awesome_oscillator(informative) macd_data = ta.MACD(informative) informative['macd'] = macd_data['macd'] informative['macd_signal'] = macd_data['macdsignal'] informative['macd_hist'] = macd_data['macdhist'] informative['cci'] = ta.CCI(informative) informative['cmf'] = chaikin_money_flow(informative, 20) informative['obv'] = ta.OBV(informative) informative['mfi'] = ta.MFI(informative) informative['adx'] = ta.ADX(informative) for col in ['rsi', 'stoch', 'roc', 'uo', 'ao', 'macd', 'macd_signal', 'macd_hist', 'cci', 'cmf', 'obv', 'mfi', 'adx']: if col in informative.columns: informative[col] = informative[col].bfill().fillna(50 if col in ['rsi', 'mfi'] else 0) except Exception as e: logger.warning(f"Momentum indicator error: {e}") informative['rsi'] = 50 informative['stoch'] = 50 informative['roc'] = 0 informative['uo'] = 50 informative['ao'] = 0 informative['macd'] = 0 informative['macd_signal'] = 0 informative['macd_hist'] = 0 informative['cci'] = 0 informative['cmf'] = 0 informative['obv'] = informative['volume'].cumsum() informative['mfi'] = 50 informative['adx'] = 25 # Keltner Channel try: keltner = emaKeltner(informative) informative["kc_upperband"] = keltner["upper"] informative["kc_middleband"] = keltner["mid"] informative["kc_lowerband"] = keltner["lower"] except: informative["kc_upperband"] = informative['close'] * 1.02 informative["kc_middleband"] = informative['close'] informative["kc_lowerband"] = informative['close'] * 0.98 # Bollinger Bands try: bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=20, stds=2) informative['bollinger_upperband'] = bollinger['upper'] informative['bollinger_lowerband'] = bollinger['lower'] informative['bollinger_middleband'] = bollinger['mid'] informative['bollinger_width'] = (bollinger['upper'] - bollinger['lower']) / bollinger['mid'] except: informative['bollinger_upperband'] = informative['close'] * 1.02 informative['bollinger_lowerband'] = informative['close'] * 0.98 informative['bollinger_middleband'] = informative['close'] informative['bollinger_width'] = 0.04 # EMAs try: informative['ema9'] = ta.EMA(informative, timeperiod=9) informative['ema20'] = ta.EMA(informative, timeperiod=20) informative['ema50'] = ta.EMA(informative, timeperiod=50) informative['ema200'] = ta.EMA(informative, timeperiod=200) for col in ['ema9', 'ema20', 'ema50', 'ema200']: informative[col] = informative[col].bfill().fillna(informative['close']) except: informative['ema9'] = informative['close'] informative['ema20'] = informative['close'] informative['ema50'] = informative['close'] informative['ema200'] = informative['close'] # V6551: Pivot points removed — real-time divergence engine doesn't use them. # ── V64 NEW: SQUEEZE DETECTION ────────────────────────── # Squeeze = Bollinger Bands INSIDE Keltner Channel (low vol compression) # Release = BB expands back outside KC (breakout imminent) try: informative['squeeze_on'] = ( (informative['bollinger_lowerband'] > informative['kc_lowerband']) & (informative['bollinger_upperband'] < informative['kc_upperband']) ).astype(int) # Squeeze just released: was on, now off informative['squeeze_release'] = ( (informative['squeeze_on'].shift(1) == 1) & (informative['squeeze_on'] == 0) ).astype(int) # How many consecutive bars was the squeeze on? squeeze_count = informative['squeeze_on'].copy() groups = (squeeze_count != squeeze_count.shift()).cumsum() informative['squeeze_duration'] = squeeze_count.groupby(groups).cumsum() # Squeeze momentum = linear regression slope of close over squeeze period # Simplified: use rate-of-change of midline as proxy informative['squeeze_momentum'] = ( (informative['close'] - informative['kc_middleband']) / informative['atr'] ).fillna(0) except Exception as e: logger.warning(f"Squeeze calculation error: {e}") informative['squeeze_on'] = 0 informative['squeeze_release'] = 0 informative['squeeze_duration'] = 0 informative['squeeze_momentum'] = 0 # ── V64 NEW: MOMENTUM BURST DETECTION ─────────────────── try: # RSI crossing key level informative['rsi_cross_up'] = ( (informative['rsi'] > self.momentum_rsi_cross.value) & (informative['rsi'].shift(1) <= self.momentum_rsi_cross.value) ) informative['rsi_cross_down'] = ( (informative['rsi'] < (100 - self.momentum_rsi_cross.value)) & (informative['rsi'].shift(1) >= (100 - self.momentum_rsi_cross.value)) ) # MACD histogram flip informative['macd_hist_flip_bull'] = ( (informative['macd_hist'] > self.momentum_macd_threshold.value) & (informative['macd_hist'].shift(1) <= self.momentum_macd_threshold.value) ) informative['macd_hist_flip_bear'] = ( (informative['macd_hist'] < -self.momentum_macd_threshold.value) & (informative['macd_hist'].shift(1) >= -self.momentum_macd_threshold.value) ) # Volume surge informative['volume_surge'] = ( informative['volume_ratio'] >= self.momentum_volume_mult.value ) # EMA momentum: 9 > 20 (bullish) or 9 < 20 (bearish) informative['ema_bull'] = informative['ema9'] > informative['ema20'] informative['ema_bear'] = informative['ema9'] < informative['ema20'] except Exception as e: logger.warning(f"Momentum burst error: {e}") informative['rsi_cross_up'] = False informative['rsi_cross_down'] = False informative['macd_hist_flip_bull'] = False informative['macd_hist_flip_bear'] = False informative['volume_surge'] = False informative['ema_bull'] = False informative['ema_bear'] = False # ── V6551: REAL-TIME DIVERGENCE ANALYSIS ────────────────── # No pivots, no lookahead. Divergence fires AT the bar it forms. # Entry triggered next bar's open. See add_realtime_divergences(). try: self.initialize_divergences_lists(informative) informative = self.add_realtime_divergences(informative) except Exception as e: logger.warning(f"V6551 realtime divergence engine error: {e}") informative["total_bullish_divergences"] = np.nan informative["total_bullish_divergences_count"] = 0 informative["total_bullish_divergences_names"] = '' informative["total_bearish_divergences"] = np.nan informative["total_bearish_divergences_count"] = 0 informative["total_bearish_divergences_names"] = '' # ── SIGNAL STRENGTH ────────────────────────────────────── try: informative['signal_strength'] = self.calculate_signal_strength(informative) except: informative['signal_strength'] = 0 # ── MERGE BACK ─────────────────────────────────────────── for col in informative.columns: dataframe[col] = informative[col] # ── CHOPPINESS INDEX ───────────────────────────────────── try: dataframe['chop'] = choppiness_index(dataframe['high'], dataframe['low'], dataframe['close'], window=14) dataframe['natr'] = ta.NATR(dataframe['high'], dataframe['low'], dataframe['close'], window=14) dataframe['natr_diff'] = dataframe['natr'] - dataframe['natr'].shift(1) dataframe['natr_direction_change'] = (dataframe['natr_diff'] * dataframe['natr_diff'].shift(1) < 0) except: dataframe['chop'] = 50 dataframe['natr'] = 0.02 dataframe['natr_diff'] = 0 dataframe['natr_direction_change'] = False # ── SUPPORT / RESISTANCE ───────────────────────────────── try: dataframe['swing_high'] = dataframe['high'].rolling(window=50, min_periods=1).max() dataframe['swing_low'] = dataframe['low'].rolling(window=50, min_periods=1).min() dataframe['distance_to_resistance'] = (dataframe['swing_high'] - dataframe['close']) / dataframe['close'] dataframe['distance_to_support'] = (dataframe['close'] - dataframe['swing_low']) / dataframe['close'] except: dataframe['swing_high'] = dataframe['high'] dataframe['swing_low'] = dataframe['low'] dataframe['distance_to_resistance'] = 0.02 dataframe['distance_to_support'] = 0.02 # ── PLOT CONFIG ────────────────────────────────────────── try: self.plot_config = ( PlotConfig() .add_total_divergences_in_config(dataframe) .config) except: self.plot_config = None return dataframe def _add_dummy_1h_columns(self, dataframe): """Fallback 1h columns from 15m data with scaled periods""" try: dataframe['ema50_1h_1h'] = ta.EMA(dataframe, timeperiod=200) dataframe['ema200_1h_1h'] = ta.EMA(dataframe, timeperiod=800) dataframe['trend_1h_1h'] = ta.EMA(dataframe, timeperiod=84) dataframe['trend_strength_1h_1h'] = ta.ADX(dataframe) dataframe['rsi_1h_1h'] = ta.RSI(dataframe, timeperiod=56) for col in ['ema50_1h_1h', 'ema200_1h_1h', 'trend_1h_1h', 'trend_strength_1h_1h', 'rsi_1h_1h']: if col in dataframe.columns: dataframe[col] = dataframe[col].bfill().fillna( dataframe['close'] if 'ema' in col or 'trend' in col else 25 if 'strength' in col else 50 ) except Exception as e: logger.warning(f"Dummy 1h column error: {e}") dataframe['ema50_1h_1h'] = dataframe['close'] dataframe['ema200_1h_1h'] = dataframe['close'] dataframe['trend_1h_1h'] = dataframe['close'] dataframe['trend_strength_1h_1h'] = 25 dataframe['rsi_1h_1h'] = 50 def calculate_signal_strength(self, dataframe: DataFrame) -> Series: """Directional signal strength — bull vs bear""" volume_strength = np.where(dataframe['volume_ratio'] > 1.5, 2, np.where(dataframe['volume_ratio'] > 1.2, 1, 0)) adx_strength = np.where(dataframe['adx'] > 30, 1, 0) ema_bullish = np.where( (dataframe['ema20'] > dataframe['ema50']) & (dataframe['ema50'] > dataframe['ema200']), 1, 0) ema_bearish = np.where( (dataframe['ema20'] < dataframe['ema50']) & (dataframe['ema50'] < dataframe['ema200']), 1, 0) # V64: add squeeze bonus squeeze_bonus = np.where(dataframe['squeeze_release'] == 1, 1, 0) dataframe['bull_signal_strength'] = ( dataframe['total_bullish_divergences_count'] * 2 + volume_strength + ema_bullish + adx_strength + squeeze_bonus ) dataframe['bear_signal_strength'] = ( dataframe['total_bearish_divergences_count'] * 2 + volume_strength + ema_bearish + adx_strength + squeeze_bonus ) strength = dataframe['bull_signal_strength'] + dataframe['bear_signal_strength'] return strength # ═════════════════════════════════════════════════════════════ # ENTRIES # ═════════════════════════════════════════════════════════════ def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Multi-signal entry engine with: 1) Raw signal preservation 2) Signal stack visibility 3) Deterministic priority routing 4) One final executable signal per candle (Freqtrade-safe) 5) Per-condition diagnostic logging (which filter kills each signal) """ pair = metadata.get("pair", "UNKNOWN") div_lookback = 2 consensus_lookback = 2 fast_lookback = 1 dataframe["enter_long"] = 0 dataframe["enter_short"] = 0 dataframe["enter_tag"] = "" # ───────────────────────────────────────────────────────────── # BASE FILTERS # ───────────────────────────────────────────────────────────── has_volume = dataframe["volume"].shift(1) > 0 not_choppy = dataframe["chop"].shift(1) < self.chop_threshold.value volatility_filter = ( (dataframe["volatility"].shift(1) >= self.min_volatility.value) & (dataframe["volatility"].shift(1) <= self.max_volatility.value) ) bands_long = two_bands_check_long(dataframe) bands_short = two_bands_check_short(dataframe) rsi_long_filter = ( (dataframe["rsi"].shift(1) < self.rsi_overbought.value) & (dataframe["rsi"].shift(1) > 30) ) rsi_short_filter = ( (dataframe["rsi"].shift(1) > self.rsi_oversold.value) & (dataframe["rsi"].shift(1) < 70) ) trend_strength = dataframe["adx"].shift(1) > self.adx_threshold.value # ───────────────────────────────────────────────────────────── # 1H TREND CONTEXT (from merged informative) # ───────────────────────────────────────────────────────────── if "trend_1h_1h" in dataframe.columns: trend_1h_bull = dataframe["close"] > dataframe["trend_1h_1h"] trend_1h_bear = dataframe["close"] < dataframe["trend_1h_1h"] else: # fallback if 1h merge failed — don't block signals trend_1h_bull = pd.Series(True, index=dataframe.index) trend_1h_bear = pd.Series(True, index=dataframe.index) # ───────────────────────────────────────────────────────────── # SHARED DIVERGENCE / STRENGTH CALCULATIONS # V6551: divergence columns are now REAL-TIME populated # (fired at the bar they form, no pivot lookahead). No _rt # shift needed — current-bar count is what was knowable at # candle close. Entry triggers next-bar open via Freqtrade. # ───────────────────────────────────────────────────────────── prev_bull_div = dataframe["total_bullish_divergences"].rolling(div_lookback, min_periods=1).max() prev_bear_div = dataframe["total_bearish_divergences"].rolling(div_lookback, min_periods=1).max() prev_bull_div_count = dataframe["total_bullish_divergences_count"].rolling(div_lookback, min_periods=1).sum() prev_bear_div_count = dataframe["total_bearish_divergences_count"].rolling(div_lookback, min_periods=1).sum() prev_bull_div_count_c = dataframe["total_bullish_divergences_count"].rolling(window=consensus_lookback, min_periods=1).sum() prev_bear_div_count_c = dataframe["total_bearish_divergences_count"].rolling(window=consensus_lookback, min_periods=1).sum() prev_bull_strength = dataframe["bull_signal_strength"].shift(1) prev_bear_strength = dataframe["bear_signal_strength"].shift(1) prev_bull_strength_c = dataframe["bull_signal_strength"].rolling(window=consensus_lookback, min_periods=1).sum() prev_bear_strength_c = dataframe["bear_signal_strength"].rolling(window=consensus_lookback, min_periods=1).sum() fast_bull_div_count = dataframe["total_bullish_divergences_count"].rolling(window=fast_lookback, min_periods=1).sum() fast_bear_div_count = dataframe["total_bearish_divergences_count"].rolling(window=fast_lookback, min_periods=1).sum() fast_bull_strength = dataframe["bull_signal_strength"].rolling(window=fast_lookback, min_periods=1).sum() fast_bear_strength = dataframe["bear_signal_strength"].rolling(window=fast_lookback, min_periods=1).sum() fast_bull_strength_raw = dataframe["bull_signal_strength"] fast_bear_strength_raw = dataframe["bear_signal_strength"] bullish_divergence = ( (prev_bull_div > 0) & (prev_bull_div_count >= self.min_divergence_count.value) ) bearish_divergence = ( (prev_bear_div > 0) & (prev_bear_div_count >= self.min_divergence_count.value) ) # ───────────────────────────────────────────────────────────── # SIGNALS # ───────────────────────────────────────────────────────────── # 1. HQ (highest quality) long_hq = ( bullish_divergence & volatility_filter & bands_long & rsi_long_filter & trend_strength & (prev_bull_strength > prev_bear_strength) & (prev_bull_strength >= 3) & (prev_bull_div_count >= 4) & trend_1h_bull & # ← V65513: gate bullish HQ behind 1H uptrend has_volume # (mirror of short_hq's trend_1h_bear; kills the ) # -87 USDT ungated HQ_Bull_Div bleed: 147 of 149 # HQ_Bull_Div trades were counter-trend losers, # only the 2 trend-aligned HQ_Bull_Div2 won) short_hq = ( bearish_divergence & bands_short & rsi_short_filter & trend_strength & (prev_bear_strength > prev_bull_strength) & (prev_bear_strength >= 3) & (prev_bear_div_count >= 4) & trend_1h_bear & # ← V65512: gate bearish HQ behind 1H downtrend has_volume ) # 1b. HQ2 (HQ + 1h trend confirmation) long_hq2 = long_hq & trend_1h_bull short_hq2 = short_hq & trend_1h_bear # 2. Consensus (with choppiness, volatility, no-conflict filters) consensus_long = ( (prev_bull_strength_c >= 2) & (prev_bull_strength_c > prev_bear_strength_c) & (prev_bull_div_count_c >= 3) & (prev_bear_strength_c < 1) & # ← No bearish conflict not_choppy & # ← Skip ranging markets volatility_filter & # ← Skip extreme volatility has_volume ) consensus_short = ( (prev_bear_strength_c >= 2) & (prev_bear_strength_c > prev_bull_strength_c) & (prev_bear_div_count_c >= 3) & (prev_bull_strength_c < 1) & # ← No bullish conflict not_choppy & # ← Skip ranging markets volatility_filter & # ← Skip extreme volatility trend_1h_bear & # ← V65512: gate bearish consensus behind 1H downtrend has_volume ) # 3. Fast Divergence (TIGHTENED: strength >= 2, div >= 3, no conflict) fast_long = ( (fast_bull_strength >= 2) & # ← Was >= 1 (fast_bull_strength_raw >= 2) & # ← Require raw strength too (fast_bull_div_count >= 3) & # ← Was >= 2 (fast_bear_strength < 1) & # ← No bearish conflict (fast_bear_div_count < 2) & # ← No bearish divergence not_choppy & # ← Skip ranging markets volatility_filter & # ← Skip extreme volatility has_volume ) fast_short = ( (fast_bear_strength >= 2) & # ← Was >= 1 (fast_bear_strength_raw >= 2) & # ← Require raw strength too (fast_bear_div_count >= 3) & # ← Was >= 2 (fast_bull_strength < 1) & # ← No bullish conflict (fast_bull_div_count < 2) & # ← No bullish divergence not_choppy & # ← Skip ranging markets volatility_filter & # ← Skip extreme volatility trend_1h_bear & # ← V65512: gate fast short behind 1H downtrend has_volume ) # 4. Squeeze Breakout squeeze_duration_prev = dataframe["squeeze_duration"].shift(1) >= self.squeeze_lookback.value squeeze_momentum_prev = dataframe["squeeze_momentum"].shift(1) recent_release = dataframe["squeeze_release"].rolling(3).max().shift(1) == 1 squeeze_long = ( recent_release & squeeze_duration_prev & (squeeze_momentum_prev > self.squeeze_momentum_threshold.value) & (dataframe["ema9"].shift(1) > dataframe["ema20"].shift(1)) & (dataframe["close"].shift(1) > dataframe["ema9"].shift(1)) & (dataframe["volume_ratio"].shift(1) > 1.1) & not_choppy & has_volume ) squeeze_short = ( recent_release & squeeze_duration_prev & (squeeze_momentum_prev < -self.squeeze_momentum_threshold.value) & (dataframe["ema9"].shift(1) < dataframe["ema20"].shift(1)) & (dataframe["close"].shift(1) < dataframe["ema9"].shift(1)) & (dataframe["volume_ratio"].shift(1) > 1.1) & not_choppy & has_volume ) # 5. Momentum Burst momentum_long = ( dataframe["rsi_cross_up"].shift(1) & dataframe["macd_hist_flip_bull"].shift(1) & dataframe["ema_bull"].shift(1) & not_choppy & has_volume ) momentum_short = ( dataframe["rsi_cross_down"].shift(1) & dataframe["macd_hist_flip_bear"].shift(1) & dataframe["ema_bear"].shift(1) & not_choppy & has_volume ) # ═════════════════════════════════════════════════════════ # SET ENTRY SIGNALS (order = tag priority, last wins) # ═════════════════════════════════════════════════════════ # enter_long / enter_short — order doesn't matter (1 is 1) # V65513: momentum_* and squeeze_* signals DISABLED. Their tag lines # (below) were already commented out, so they entered with enter_tag='' # — the untagged "empty entries" that bled -19.51 USDT over 33 trades # with no way to attribute or manage them. Re-enable WITH tags + a 1H # trend gate if you want to A/B them as first-class signals later. #dataframe.loc[momentum_long, 'enter_long'] = 1 #dataframe.loc[momentum_short, 'enter_short'] = 1 #dataframe.loc[squeeze_long, 'enter_long'] = 1 #dataframe.loc[squeeze_short, 'enter_short'] = 1 dataframe.loc[fast_long, 'enter_long'] = 1 dataframe.loc[fast_short, 'enter_short'] = 1 dataframe.loc[consensus_long, 'enter_long'] = 1 dataframe.loc[consensus_short, 'enter_short'] = 1 dataframe.loc[long_hq, 'enter_long'] = 1 dataframe.loc[short_hq, 'enter_short'] = 1 dataframe.loc[long_hq2, 'enter_long'] = 1 dataframe.loc[short_hq2, 'enter_short'] = 1 dataframe['enter_tag'] = '' # enter_tag — order MATTERS, weakest first, strongest last #dataframe.loc[momentum_long, 'enter_tag'] = 'Momentum_Long' #dataframe.loc[momentum_short, 'enter_tag'] = 'Momentum_Short' #dataframe.loc[squeeze_long, 'enter_tag'] = 'Squeeze_Long' #dataframe.loc[squeeze_short, 'enter_tag'] = 'Squeeze_Short' dataframe.loc[fast_long, 'enter_tag'] = 'Div_Fast_Long' dataframe.loc[fast_short, 'enter_tag'] = 'Div_Fast_Short' dataframe.loc[consensus_long, 'enter_tag'] = 'Div_Bull_Cons' dataframe.loc[consensus_short, 'enter_tag'] = 'Div_Bear_Cons' dataframe.loc[long_hq, 'enter_tag'] = 'HQ_Bull_Div' dataframe.loc[short_hq, 'enter_tag'] = 'HQ_Bear_Div' dataframe.loc[long_hq2, 'enter_tag'] = 'HQ_Bull_Div2' dataframe.loc[short_hq2, 'enter_tag'] = 'HQ_Bear_Div2' # ═════════════════════════════════════════════════════════ # V6551 DIAGNOSTIC: per-signal counters across the dataframe. # Also shows how many bars had ANY divergence (real-time). # If div_bars stays at 0, the realtime engine isn't finding # divergences — try smaller `window` or larger `index_range`. # If div_bars > 0 but signal counts still 0, downstream # filters (volatility, RSI bounds, ADX, bands) are killing them. # ═════════════════════════════════════════════════════════ if len(dataframe) > 0: try: bull_div_bars = int((dataframe["total_bullish_divergences_count"] > 0).sum()) bear_div_bars = int((dataframe["total_bearish_divergences_count"] > 0).sum()) signal_counts = ( f"div_bars=B{bull_div_bars}/Be{bear_div_bars} | " f"mom_l={int(momentum_long.sum())} mom_s={int(momentum_short.sum())} | " f"sqz_l={int(squeeze_long.sum())} sqz_s={int(squeeze_short.sum())} | " f"fast_l={int(fast_long.sum())} fast_s={int(fast_short.sum())} | " f"cons_l={int(consensus_long.sum())} cons_s={int(consensus_short.sum())} | " f"hq_l={int(long_hq.sum())} hq_s={int(short_hq.sum())} | " f"hq2_l={int(long_hq2.sum())} hq2_s={int(short_hq2.sum())}" ) logger.info(f"[{pair}] V6551 signal totals — {signal_counts}") except Exception as e: logger.debug(f"[{pair}] signal counter log error: {e}") c = dataframe.iloc[-1] logger.info( f"[{metadata['pair']}] {c.get('date', '')} | " f"Div: B{c.get('total_bullish_divergences_count', 0)}/Be{c.get('total_bearish_divergences_count', 0)} | " f"Str: {c.get('signal_strength', 0)} | " f"Vol: {c.get('volume_ratio', 0):.2f} | " f"Sqz: {c.get('squeeze_on', 0)} | " f"Chop: {c.get('chop', 0):.1f} | " f"HTF: {'Bull' if c.get('ema50_1h_1h', 0) > c.get('ema200_1h_1h', 0) else 'Bear'} | " f"Entry: L{c.get('enter_long', 0)}/S{c.get('enter_short', 0)} [{c.get('enter_tag', '')}]" ) return dataframe # ═════════════════════════════════════════════════════════════ # EXITS # ═════════════════════════════════════════════════════════════ def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return dataframe # ═════════════════════════════════════════════════════════════ # LEVERAGE # ═════════════════════════════════════════════════════════════ 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: """ V64: Signal-type aware leverage + volatility scaling """ base_lev = self.leverage_value try: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if len(dataframe) > 1: current_vol = dataframe['volatility'].iloc[-2] sig_strength = dataframe['signal_strength'].iloc[-2] # Scale DOWN in high volatility if current_vol > 0.02: base_lev *= 0.7 elif current_vol > 0.015: base_lev *= 0.85 # Scale by signal strength if sig_strength >= 8: lev = base_lev elif sig_strength >= 6: lev = base_lev * 0.8 elif sig_strength >= 4: lev = base_lev * 0.6 else: lev = base_lev * 0.4 # Squeeze / momentum signals get slightly lower leverage (faster moves = more risk) if entry_tag and ('Squeeze' in entry_tag or 'Momentum' in entry_tag): lev *= 0.8 return max(1.0, min(lev, max_leverage)) except: pass return max(1.0, base_lev * 0.5) # ═════════════════════════════════════════════════════════════ # CUSTOM EXIT — V64 ATR-BASED TRAILING # ═════════════════════════════════════════════════════════════ def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): """ V652: V651 logic + Zone 0 reversal protection + verbose logging. """ actual_profit = trade.calc_profit_ratio(current_rate) current_profit = actual_profit entry_tag = getattr(trade, 'enter_tag', '') or '' trade_duration_minutes = (current_time - trade.open_date_utc).total_seconds() / 60 # ── PROFIT PEAK TRACKING ───────────────────────────────── trade_id = f"{pair}_{trade.open_date_utc.timestamp()}" if trade_id not in self.profit_peaks: self.profit_peaks[trade_id] = current_profit elif current_profit > self.profit_peaks[trade_id]: self.profit_peaks[trade_id] = current_profit profit_peak = self.profit_peaks[trade_id] # ── GET CURRENT ATR FOR ADAPTIVE EXITS ─────────────────── atr_pct = 0.015 # fallback: 1.5% try: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if len(dataframe) > 1: last_candle = dataframe.iloc[-2] atr_pct = last_candle['atr'] / last_candle['close'] atr_pct = max(0.003, min(atr_pct, 0.05)) except: pass atr_profit_unit = atr_pct * trade.leverage * self.trail_atr_multiplier.value # ── EXIT LOGGER ────────────────────────────────────────── def log_and_exit(reason): logger.info(f"✅ EXIT: {pair}") logger.info(f" 💰 Profit: {current_profit * 100:.2f}%") logger.info(f" 📈 Peak: {profit_peak * 100:.2f}%") logger.info(f" 🕐 Duration: {int(trade_duration_minutes)} min") logger.info(f" 🏷️ Tag: {entry_tag}") logger.info(f" 🎯 Reason: {reason}") if trade_id in self.profit_peaks: del self.profit_peaks[trade_id] return reason # ── HOLD LOGGER (call with current floor or None) ──────── def log_hold(floor=None): if floor is not None: logger.debug( f"⏳ HOLD: {pair} | Profit: {current_profit*100:.2f}% | " f"Peak: {profit_peak*100:.2f}% | Floor: {floor*100:.2f}% | " f"Dur: {int(trade_duration_minutes)}m | Tag: {entry_tag}" ) else: logger.debug( f"⏳ HOLD: {pair} | Profit: {current_profit*100:.2f}% | " f"Peak: {profit_peak*100:.2f}% | Zone0 | " f"Dur: {int(trade_duration_minutes)}m | Tag: {entry_tag}" ) # ── DEAD-ZONE LEAK DETECTOR (info level — see leaks without full debug) ── # Logs when a trade with a meaningful peak is sitting in Zone 0 — exactly # the scenario you saw on the chart. Comment out once tuning is done. if current_profit < 0.02 and profit_peak >= 0.008: giveback = (profit_peak - current_profit) * 100 logger.info( f"⚠️ DEAD_ZONE: {pair} | peak={profit_peak*100:.2f}% | " f"now={current_profit*100:.2f}% | giveback={giveback:.2f}% | " f"dur={int(trade_duration_minutes)}m | tag={entry_tag}" ) # ── SIGNAL-TYPE PROFILE ────────────────────────────────── is_fast_trade = any(t in entry_tag for t in ['Squeeze', 'Momentum', 'Fast']) trail_tightness = 0.7 if is_fast_trade else 1.0 # ── ZONE 0: UNDER 2% (and never reached a trail tier) ─── # V65513: added `and profit_peak < 0.02`. Previously ANY trade under 2% # current profit was trapped in this scratch block and never reached the # Light/Medium/Strong/Monster trail engine (those are the real winners). # Now a trade that ever peaked >= 2% falls through to the trail logic, # which protects it with a real ATR pullback + floor instead of the flat # Sub2 scratch. This is what lets the "sub stuff" be removed safely. if current_profit < 0.02 and profit_peak < 0.02: # ▼▼▼ FAST/MOMENTUM TRADE PROTECTION ▼▼▼ if is_fast_trade: # V65512: only react to a real peak (was 0.6% = noise) so fast # trades aren't scratched before reaching the trail zones. if profit_peak >= 0.015 and current_profit < profit_peak * 0.4: return log_and_exit(f"Fast_Reversal_peak{profit_peak*100:.2f}%_now{current_profit*100:.2f}%") if profit_peak >= 0.012 and current_profit < 0.003: return log_and_exit(f"Fast_Collapse_{profit_peak*100:.2f}%") if trade_duration_minutes >= 45 and profit_peak >= 0.008 and current_profit < 0.004: return log_and_exit(f"Fast_Stale_{int(trade_duration_minutes)}m") # ▼▼▼ GENERAL SUB-2% REVERSAL — REMOVED in V65513 ▼▼▼ # The "sub stuff." With the new Zone-0 gate (profit_peak < 0.02), # any trade that peaked into a trail tier now exits via the trail # engine instead of being scratched here at ~0.5-1%. The old block # required profit_peak >= 0.025, which can no longer be reached # inside this branch, so it was dead code and is deleted. if current_profit >= 0.005: stale_1 = 120 if is_fast_trade else 240 stale_2 = 240 if is_fast_trade else 480 stale_3 = 480 if is_fast_trade else 960 if trade_duration_minutes >= stale_1 and profit_peak >= 0.015 and current_profit < 0.008: return log_and_exit(f"Stagnant_Reversal_{profit_peak * 100:.1f}%") if trade_duration_minutes >= stale_2 and current_profit < 0.012: return log_and_exit(f"Stagnant_{int(stale_2/60)}HR_{current_profit * 100:.1f}%") if trade_duration_minutes >= stale_3 and current_profit < 0.015: return log_and_exit(f"Stagnant_{int(stale_3/60)}HR_{current_profit * 100:.1f}%") if trade_duration_minutes >= 1440 and current_profit >= 0.002: return log_and_exit(f"Timeout_24HR_{current_profit * 100:.1f}%") log_hold(floor=None) return None # ── PROFIT FLOOR ───────────────────────────────────────── floor_ratio = self.profit_floor_pct.value if profit_peak >= 0.20: profit_floor = profit_peak * 0.60 elif profit_peak >= 0.12: profit_floor = profit_peak * floor_ratio + 0.02 elif profit_peak >= 0.08: profit_floor = profit_peak * floor_ratio + 0.01 elif profit_peak >= 0.04: profit_floor = profit_peak * 0.40 else: profit_floor = 0.005 # ── TIME-BASED TIGHTENING ──────────────────────────────── time_factor = 1.0 if trade_duration_minutes > 720: time_factor = 0.7 elif trade_duration_minutes > 360: time_factor = 0.85 # ── ATR TRAILING ZONES ─────────────────────────────────── if profit_peak >= 0.02 and profit_peak < 0.04: pullback = 2.0 * atr_profit_unit * trail_tightness * time_factor trail_stop = max(profit_peak - pullback, profit_floor) if current_profit > 0 and current_profit < trail_stop: return log_and_exit(f"Light_Trail_{profit_peak * 100:.1f}%") log_hold(floor=trail_stop) return None if profit_peak >= 0.04 and profit_peak < 0.08: pullback = 1.5 * atr_profit_unit * trail_tightness * time_factor trail_stop = max(profit_peak - pullback, profit_floor) if current_profit > 0 and current_profit < trail_stop: return log_and_exit(f"Medium_Trail_{profit_peak * 100:.1f}%") log_hold(floor=trail_stop) return None if profit_peak >= 0.08 and profit_peak < 0.15: pullback = 1.2 * atr_profit_unit * trail_tightness * time_factor trail_stop = max(profit_peak - pullback, profit_floor) if current_profit > 0 and current_profit < trail_stop: return log_and_exit(f"Strong_Trail_{profit_peak * 100:.1f}%") log_hold(floor=trail_stop) return None if profit_peak >= 0.15: pullback = 1.0 * atr_profit_unit * trail_tightness * time_factor trail_stop = max(profit_peak - pullback, profit_floor) if current_profit > 0 and current_profit < trail_stop: return log_and_exit(f"Monster_Trail_{profit_peak * 100:.1f}%") log_hold(floor=trail_stop) return None return None # ═════════════════════════════════════════════════════════════ # V6551: REAL-TIME DIVERGENCE ENGINE # # Detects divergences at the bar they form, with no pivot- # confirmation lag. At each bar i: # 1. Bar i is a fresh local extremum (lowest/highest of last # `swing+1` bars looking BACKWARD only — knowable at close). # 2. Within the prior [lookback_min, lookback_max] window, # there exists a 2-sided swing extremum at bar j (this is # fully knowable since j is in the past). # 3. Price between j→i went one direction, indicator the # opposite (the divergence). # 4. Both moves cleared an ATR/indicator floor. # If any indicator satisfies this for any j, divergence fires # AT bar i and Freqtrade enters at bar i+1's open. No lookahead. # ═════════════════════════════════════════════════════════════ def initialize_divergences_lists(self, dataframe: DataFrame): dataframe["total_bullish_divergences"] = np.nan dataframe["total_bullish_divergences_count"] = 0 dataframe["total_bullish_divergences_names"] = '' dataframe["total_bearish_divergences"] = np.nan dataframe["total_bearish_divergences_count"] = 0 dataframe["total_bearish_divergences_names"] = '' def add_realtime_divergences(self, dataframe: DataFrame) -> DataFrame: """V6551: Real-time divergence detection across all configured indicators.""" indicators = ['rsi', 'stoch', 'roc', 'uo', 'ao', 'macd', 'cci', 'cmf', 'obv', 'mfi'] n = len(dataframe) bull_count = np.zeros(n, dtype=np.int32) bear_count = np.zeros(n, dtype=np.int32) bull_names = [''] * n bear_names = [''] * n # Tunable boundaries — tied to existing optimizable params: # self.window.value (5..15, default 7) → swing tightness # self.index_range.value (20..50, default 30) → max lookback range swing = max(2, int(self.window.value) // 3) # default → 2 lookback_min = 5 lookback_max = max(lookback_min + 5, int(self.index_range.value)) try: low_arr = dataframe['low'].values.astype(np.float64) high_arr = dataframe['high'].values.astype(np.float64) atr_arr = dataframe['atr'].values.astype(np.float64) except Exception as e: logger.warning(f"V6551 input array error: {e}") return dataframe for ind_name in indicators: if ind_name not in dataframe.columns: continue try: ind_arr = dataframe[ind_name].values.astype(np.float64) bull_d = realtime_divergence_scan( low_arr, ind_arr, atr_arr, divergence_type='bullish', lookback_min=lookback_min, lookback_max=lookback_max, swing=swing, min_atr_mult=0.5, min_ind_diff=5.0, ) bear_d = realtime_divergence_scan( high_arr, ind_arr, atr_arr, divergence_type='bearish', lookback_min=lookback_min, lookback_max=lookback_max, swing=swing, min_atr_mult=0.5, min_ind_diff=5.0, ) bull_count += bull_d.astype(np.int32) bear_count += bear_d.astype(np.int32) bull_idx = np.where(bull_d)[0] bear_idx = np.where(bear_d)[0] tag = ind_name.upper() + '
' for idx in bull_idx: bull_names[idx] += tag for idx in bear_idx: bear_names[idx] += tag # Per-indicator occurrence column (compatible with V653 plot conventions) close_arr = dataframe['close'].values dataframe[f'bullish_divergence_{ind_name}_occurence'] = np.where(bull_d, close_arr, np.nan) dataframe[f'bearish_divergence_{ind_name}_occurence'] = np.where(bear_d, close_arr, np.nan) except Exception as e: logger.warning(f"V6551 realtime divergence error for {ind_name}: {e}") continue close_arr = dataframe['close'].values dataframe['total_bullish_divergences_count'] = bull_count dataframe['total_bearish_divergences_count'] = bear_count dataframe['total_bullish_divergences_names'] = bull_names dataframe['total_bearish_divergences_names'] = bear_names dataframe['total_bullish_divergences'] = np.where(bull_count > 0, close_arr, np.nan) dataframe['total_bearish_divergences'] = np.where(bear_count > 0, close_arr, np.nan) return dataframe @property def protections(self): prot = [] if self.use_cooldown_protection.value: prot.append({ "method": "CooldownPeriod", "stop_duration_candles": self.cooldown_lookback.value }) if self.use_max_drawdown_protection.value: prot.append({ "method": "MaxDrawdown", "lookback_period_candles": self.max_drawdown_lookback.value, "trade_limit": self.max_drawdown_trade_limit.value, "stop_duration_candles": self.max_drawdown_stop_duration.value, "max_allowed_drawdown": self.max_allowed_drawdown.value }) if self.use_stop_protection.value: prot.append({ "method": "StoplossGuard", "lookback_period_candles": self.stoploss_guard_lookback.value, "trade_limit": self.stoploss_guard_trade_limit.value, "stop_duration_candles": self.stop_duration.value, "only_per_pair": self.stoploss_guard_only_per_pair.value, }) return prot # ═════════════════════════════════════════════════════════════════ # HELPER FUNCTIONS # ═════════════════════════════════════════════════════════════════ def choppiness_index(high, low, close, window=14): """ V643 FIX: Standard choppiness formula using True Range instead of NATR. Standard formula: choppiness = 100 * log10(sum(true_range over window) / (highest_high - lowest_low)) / log10(window) This fixes the issue where NATR (already normalized) was divided by raw price span, mixing incompatible units and producing unrealistic/negative values. """ # Calculate True Range prev_close = close.shift(1) tr1 = high - low tr2 = (high - prev_close).abs() tr3 = (low - prev_close).abs() true_range = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1) # Sum True Range over window tr_sum = true_range.rolling(window=window, min_periods=window).sum() # Calculate price span (high - low over window) high_max = high.rolling(window=window, min_periods=window).max() low_min = low.rolling(window=window, min_periods=window).min() span = (high_max - low_min).replace(0, np.nan) # Calculate choppiness ratio ratio = (tr_sum / span).replace([np.inf, -np.inf], np.nan) # Final choppiness calculation choppiness = 100 * np.log10(ratio) / np.log10(window) return choppiness def resample(indicator): return indicator def two_bands_check_long(dataframe): prev_low = dataframe['low'].shift(1) prev_close = dataframe['close'].shift(1) prev_kc_lower = dataframe['kc_lowerband'].shift(1) return ( (prev_low <= prev_kc_lower) | (prev_close <= prev_kc_lower) ) def two_bands_check_short(dataframe): prev_high = dataframe['high'].shift(1) prev_close = dataframe['close'].shift(1) prev_kc_upper = dataframe['kc_upperband'].shift(1) return ( (prev_high >= prev_kc_upper) | (prev_close >= prev_kc_upper) ) def green_candle(dataframe): return dataframe[resample('open')] < dataframe[resample('close')] def red_candle(dataframe): return dataframe[resample('open')] > dataframe[resample('close')] def realtime_divergence_scan(price, indicator, atr, divergence_type='bullish', lookback_min=5, lookback_max=30, swing=2, min_atr_mult=0.5, min_ind_diff=5.0): """ V6551 real-time divergence detector. Fully vectorized. For each bar i, returns True if a regular divergence forms AT bar i: - Bullish: price made a lower low than a prior swing low j, while the indicator made a higher low at i vs j. - Bearish: symmetric on highs. No pivot confirmation lag — bar i is treated as a "fresh" extremum if it's the lowest/highest of the last (swing+1) bars LOOKING BACKWARD ONLY. The prior bar j inside the [lookback_min, lookback_max] window must be a 2-sided swing extremum (knowable since j is past). Parameters ---------- price : 1-D array of low (for bullish) or high (for bearish) indicator : 1-D oscillator array (same length as price) atr : 1-D ATR array (same length as price) divergence_type : 'bullish' or 'bearish' lookback_min, lookback_max : range of N to scan, where j = i - N swing : how many bars on each side define a swing extremum at j (and how many bars LEFT-only define the fresh extremum at i) min_atr_mult : minimum |price[i] - price[j]| in ATR units min_ind_diff : minimum absolute indicator difference Returns ------- np.ndarray of bool, length len(price) """ n = len(price) out = np.zeros(n, dtype=bool) if n < lookback_max + swing + 2: return out price_arr = np.asarray(price, dtype=np.float64) ind_arr = np.asarray(indicator, dtype=np.float64) atr_arr = np.asarray(atr, dtype=np.float64) s_price = pd.Series(price_arr) if divergence_type == 'bullish': # Bar i is fresh local low if price[i] <= min(price[i-swing : i]) prev_extr = s_price.rolling(swing, min_periods=swing).min().shift(1).values is_current_swing = price_arr <= prev_extr # Bar j is 2-sided swing low (centered window of 2*swing+1) center_extr = s_price.rolling(2 * swing + 1, center=True, min_periods=2 * swing + 1).min().values is_past_swing = price_arr <= center_extr else: prev_extr = s_price.rolling(swing, min_periods=swing).max().shift(1).values is_current_swing = price_arr >= prev_extr center_extr = s_price.rolling(2 * swing + 1, center=True, min_periods=2 * swing + 1).max().values is_past_swing = price_arr >= center_extr is_current_swing = np.where(np.isnan(prev_extr), False, is_current_swing).astype(bool) is_past_swing = np.where(np.isnan(center_extr), False, is_past_swing).astype(bool) span = lookback_max - lookback_min + 1 shifted_price = np.full((n, span), np.nan, dtype=np.float64) shifted_ind = np.full((n, span), np.nan, dtype=np.float64) shifted_swing = np.zeros((n, span), dtype=bool) for k in range(span): N = lookback_min + k shifted_price[N:, k] = price_arr[:n - N] shifted_ind[N:, k] = ind_arr[:n - N] shifted_swing[N:, k] = is_past_swing[:n - N] p2d = price_arr[:, np.newaxis] i2d = ind_arr[:, np.newaxis] a2d = atr_arr[:, np.newaxis] cur_swing_2d = is_current_swing[:, np.newaxis] # NaN comparisons in numpy evaluate to False — clean booleans below. if divergence_type == 'bullish': valid = ( cur_swing_2d & shifted_swing & (p2d < shifted_price) & (i2d > shifted_ind) & ((shifted_price - p2d) >= min_atr_mult * a2d) & ((i2d - shifted_ind) >= min_ind_diff) ) else: valid = ( cur_swing_2d & shifted_swing & (p2d > shifted_price) & (i2d < shifted_ind) & ((p2d - shifted_price) >= min_atr_mult * a2d) & ((shifted_ind - i2d) >= min_ind_diff) ) return valid.any(axis=1) def emaKeltner(dataframe): keltner = {} atr = qtpylib.atr(dataframe, window=10) ema20 = ta.EMA(dataframe, timeperiod=20) keltner['upper'] = ema20 + atr keltner['mid'] = ema20 keltner['lower'] = ema20 - atr return keltner def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series: df = dataframe.copy() mfv = ((df['close'] - df['low']) - (df['high'] - df['close'])) / (df['high'] - df['low']) mfv = mfv.fillna(0.0) mfv *= df['volume'] cmf = (mfv.rolling(n, min_periods=0).sum() / df['volume'].rolling(n, min_periods=0).sum()) if fillna: cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0) return Series(cmf, name='cmf')