# --- Do not remove these libs ---
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date
from freqtrade.persistence import Trade, Order # Added Order here
from freqtrade.strategy import (IStrategy, BooleanParameter, CategoricalParameter, DecimalParameter,
IntParameter, RealParameter, merge_informative_pair, stoploss_from_open,
stoploss_from_absolute)
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal
from pathlib import Path
import logging
import time
current_time = time.time()
from datetime import datetime, timedelta, timezone
import numpy as np
import pandas as pd
import csv
pd.options.mode.chained_assignment = None
from pandas import DataFrame, Series
from typing import List, Tuple, Optional, Dict, Any
import json
import os
from freqtrade.persistence import Trade
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.util import resample_to_interval, resampled_merge
from collections import deque
import optuna
from optuna.samplers import TPESampler
from optuna.exceptions import OptunaError
import warnings
warnings.simplefilter(action="ignore", category=pd.errors.PerformanceWarning)
logger = logging.getLogger(__name__)
# ============================================================================
# ML PREDICTIVE ENTRY SYSTEM
# ============================================================================
try:
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import joblib
ML_AVAILABLE = True
except ImportError:
ML_AVAILABLE = False
ASCII_ART = '''
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║ ║
║ ██████╗ ██╗ ███████╗██╗ ██╗ ██████╗██████╗ ██╗ ██╗██████╗ ████████╗ ██████╗ ██╗ ██╗██╗███╗ ██╗ ██████╗ ║
║ ██╔══██╗██║ ██╔════╝╚██╗██╔╝██╔════╝██╔══██╗╚██╗ ██╔╝██╔══██╗╚══██╔══╝██╔═══██╗██║ ██╔╝██║████╗ ██║██╔════╝ ║
║ ███████║██║ █████╗ ╚███╔╝ ██║ ██████╔╝ ╚████╔╝ ██████╔╝ ██║ ██║ ██║█████╔╝ ██║██╔██╗ ██║██║ ███╗ ║
║ ██╔══██║██║ ██╔══╝ ██╔██╗ ██║ ██╔══██╗ ╚██╔╝ ██╔═══╝ ██║ ██║ ██║██╔═██╗ ██║██║╚██╗██║██║ ██║ ║
║ ██║ ██║███████╗███████╗██╔╝ ██╗╚██████╗██║ ██║ ██║ ██║ ██║ ╚██████╔╝██║ ██╗██║██║ ╚████║╚██████╔╝ ║
║ ╚═╝ ╚═╝╚══════╝╚══════╝╚═╝ ╚═╝ ╚═════╝╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚═════╝ ╚═╝ ╚═╝╚═╝╚═╝ ╚═══╝ ╚═════╝ ║
║ ║
║ PURE OPTUNA OPTIMIZING STRATEGY ║
║ Enhanced Multi-Feature Optimazion ║
║ Machine Learning Powered ║
║ ║
╚════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════╝
'''
def choppiness_index(high, low, close, window=14):
"""Calculate Choppiness Index"""
tr = pd.DataFrame()
tr['h-l'] = high - low
tr['h-pc'] = abs(high - close.shift(1))
tr['l-pc'] = abs(low - close.shift(1))
tr['tr'] = tr[['h-l', 'h-pc', 'l-pc']].max(axis=1)
atr = tr['tr'].rolling(window=window).sum()
highh = high.rolling(window=window).max()
lowl = low.rolling(window=window).min()
chop = 100 * np.log10(atr / (highh - lowl)) / np.log10(window)
return chop.fillna(50)
def chaikin_money_flow(dataframe, period=20):
"""Calculate Chaikin Money Flow"""
mfv = ((dataframe['close'] - dataframe['low']) - (dataframe['high'] - dataframe['close'])) / (dataframe['high'] - dataframe['low'])
mfv = mfv.fillna(0.0)
mfv *= dataframe['volume']
cmf = mfv.rolling(window=period).sum() / dataframe['volume'].rolling(window=period).sum()
return cmf.fillna(0)
def emaKeltner(dataframe, ema_length=20, atr_length=10, atr_multiplier=1):
"""Calculate Keltner Channel"""
df = dataframe.copy()
df['ema'] = ta.EMA(df['close'], timeperiod=ema_length)
df['atr'] = ta.ATR(df, timeperiod=atr_length)
df['upper'] = df['ema'] + (df['atr'] * atr_multiplier)
df['lower'] = df['ema'] - (df['atr'] * atr_multiplier)
return {'upper': df['upper'], 'mid': df['ema'], 'lower': df['lower']}
def pivot_points(dataframe, window=10):
"""Calculate pivot points"""
df = dataframe.copy()
df['pivot_lows'] = df['low'].rolling(window=window, center=True).apply(
lambda x: x[len(x)//2] if x[len(x)//2] == min(x) else np.nan, raw=True
)
df['pivot_highs'] = df['high'].rolling(window=window, center=True).apply(
lambda x: x[len(x)//2] if x[len(x)//2] == max(x) else np.nan, raw=True
)
return df[['pivot_lows', 'pivot_highs']]
def two_bands_check_long(df):
"""Check if conditions are met for long entry (not choppy)"""
kc_ok = df['close'] <= df['kc_lowerband']
bb_ok = df['close'] <= df['bollinger_lowerband']
return kc_ok | bb_ok
def two_bands_check_short(df):
"""Check if conditions are met for short entry (not choppy)"""
kc_ok = df['close'] >= df['kc_upperband']
bb_ok = df['close'] >= df['bollinger_upperband']
return kc_ok | bb_ok
def resample(column_name):
"""Helper function for resampling column names"""
return column_name
# FIX: Remove the problematic relative import and create a simple inline replacement
# from .optuna_manager import OptunaManager
class RealOptunaManager:
"""Real Optuna-based optimization manager"""
def __init__(self, strategy_name: str):
self.strategy_name = strategy_name
self.studies = {} # Store studies per pair
self.best_params_cache = {}
self.performance_history = {}
self.optimization_trigger_count = {}
self.pretraining_enabled = True
self.pretraining_days = 180
self.pretraining_done = {}
# NEW: Track optimization timestamps for 24-hour periodic re-optimization
self.last_optimization_time: Dict[str, datetime] = {}
self.optimization_interval_hours = 24 # Configure optimization frequency here
logger.info(f"📊 [OPTUNA] Initializing RealOptunaManager for strategy: {strategy_name}")
logger.info(f"🎯 [OPTUNA] Real Optuna optimization enabled")
def get_best_params(self, pair: str) -> Dict[str, Any]:
"""Get best parameters for a pair"""
if pair in self.best_params_cache:
return self.best_params_cache[pair]
# Try to load from existing study
try:
study_name = f"{self.strategy_name}_{pair.replace('/', '_').replace(':', '_')}"
study = optuna.load_study(
study_name=study_name,
storage=f"sqlite:///user_data/strategies/optuna_studies/optuna_{study_name}.db"
)
if study.trials:
self.studies[pair] = study
self.best_params_cache[pair] = study.best_params
return study.best_params
except:
pass
return None
def should_optimize(self, pair: str) -> bool:
"""Check if optimization should be triggered"""
from datetime import datetime
# First: Check if we have no parameters yet (initial optimization)
if pair not in self.best_params_cache:
logger.info(f"🆕 [OPTUNA] {pair} has no cached parameters - triggering initial optimization")
return True
# Second: Check 8-hour periodic optimization trigger
if pair in self.last_optimization_time:
hours_since_last = (datetime.now() - self.last_optimization_time[pair]).total_seconds() / 3600
if hours_since_last >= self.optimization_interval_hours:
logger.info(f"⏰ [OPTUNA] {pair} due for periodic optimization ({hours_since_last:.1f}h since last)")
return True
else:
# If we have cached params but no timestamp, this means params were loaded from DB
# We should set the timestamp to now to start the 8-hour clock
logger.info(f"🕐 [OPTUNA] {pair} has cached params but no timestamp - starting 8h timer")
self.last_optimization_time[pair] = datetime.now()
# Third: Performance-based trigger
if pair in self.performance_history:
recent_performance = self.performance_history[pair][-10:]
if len(recent_performance) >= 5:
avg_performance = sum(recent_performance) / len(recent_performance)
if avg_performance < -0.02:
logger.info(f"📉 [OPTUNA] {pair} triggered by poor performance ({avg_performance:.2%})")
return True
return False
def should_optimize_based_on_performance(self, pair: str) -> bool:
"""Check if performance-based optimization is needed"""
if pair not in self.performance_history:
return False
recent_trades = self.performance_history[pair][-10:]
if len(recent_trades) >= 5:
avg_performance = sum(recent_trades) / len(recent_trades)
return avg_performance < -0.015
return False
def update_performance(self, pair: str, profit_ratio: float):
"""Update performance tracking"""
if pair not in self.performance_history:
self.performance_history[pair] = []
self.performance_history[pair].append(profit_ratio)
if len(self.performance_history[pair]) > 50:
self.performance_history[pair] = self.performance_history[pair][-50:]
logger.debug(f"📈 [OPTUNA] Updated performance for {pair}: {profit_ratio:.4f}")
def optimize_coin(self, pair: str, objective_func, n_trials: int = 60):
"""Optimize parameters for a specific coin using real Optuna"""
logger.info(f"🚀 [OPTUNA] Starting REAL optimization for {pair} with {n_trials} trials")
try:
optuna.logging.set_verbosity(optuna.logging.WARNING)
from pathlib import Path
study_name = f"{self.strategy_name}_{pair.replace('/', '_').replace(':', '_')}"
Path("user_data/strategies/optuna_studies").mkdir(parents=True, exist_ok=True)
study = optuna.create_study(
study_name=study_name,
direction='maximize',
sampler=TPESampler(),
storage=f"sqlite:///user_data/strategies/optuna_studies/optuna_{study_name}.db",
load_if_exists=True
)
study.optimize(objective_func, n_trials=n_trials)
self.studies[pair] = study
self.best_params_cache[pair] = study.best_params
logger.info(f"✅ [OPTUNA] REAL optimization completed for {pair}")
logger.info(f"🏆 [OPTUNA] Best value: {study.best_value:.4f}")
self._log_formatted_parameters(pair, study.best_params)
return True
except Exception as e:
logger.error(f"❌ [OPTUNA] Real optimization failed for {pair}: {e}")
return False
def _log_formatted_parameters(self, pair: str, params: Dict[str, Any]):
"""Log parameters in a nicely formatted way - Enhanced with all optimized parameters"""
logger.info(f"📈 [OPTUNA] Optimized parameters for {pair}:")
# === CORE SHARED INDICATORS ===
logger.info(f"┌─ 📊 CORE SHARED INDICATORS")
logger.info(f"│ • rsi_period: {params.get('rsi_period', 'N/A')}")
logger.info(f"│ • atr_period: {params.get('atr_period', 'N/A')}")
logger.info(f"│ • volume_sma_period: {params.get('volume_sma_period', 'N/A')}")
logger.info(f"│ • ema_short_period: {params.get('ema_short_period', 'N/A')}")
logger.info(f"│ • ema_long_period: {params.get('ema_long_period', 'N/A')}")
logger.info(f"│ • adx_threshold: {params.get('adx_threshold', 'N/A')}")
logger.info(f"│ • bb_period: {params.get('bb_period', 'N/A')} (std: {params.get('bb_std', 'N/A')})")
# === GLOBAL FILTERS ===
logger.info(f"├─ 🎯 GLOBAL FILTERS")
logger.info(f"│ • min_signal_strength: {params.get('min_signal_strength', 'N/A')}")
logger.info(f"│ • volume_threshold: {params.get('volume_threshold', 'N/A')}")
logger.info(f"│ • rsi_overbought/oversold: {params.get('rsi_overbought', 'N/A')}/{params.get('rsi_oversold', 'N/A')}")
logger.info(f"│ • volatility range: {params.get('min_volatility', 'N/A'):.4f} - {params.get('max_volatility', 'N/A'):.4f}")
logger.info(f"│ • volume_factor: {params.get('volume_factor', 'N/A')}")
# === DIVERGENCE OPTIMIZATION (ENHANCED) ===
logger.info(f"├─ 💎 DIVERGENCE OPTIMIZATION (Enhanced)")
logger.info(f"│")
logger.info(f"│ ┌─ Core Detection")
logger.info(f"│ │ • window: {params.get('window', 'N/A')}")
logger.info(f"│ │ • index_range: {params.get('index_range', 'N/A')}")
logger.info(f"│ │ • lookback_period: {params.get('divergence_lookback_period', 'N/A')}")
logger.info(f"│ │ • min_rsi_delta: {params.get('divergence_min_rsi_delta', 'N/A')}")
logger.info(f"│ │ • min_price_delta: {params.get('divergence_min_price_delta', 'N/A')}")
logger.info(f"│ │ • RSI thresholds: {params.get('divergence_rsi_low_threshold', 'N/A')}/{params.get('divergence_rsi_high_threshold', 'N/A')}")
logger.info(f"│ │")
logger.info(f"│ ┌─ 🌡️ Market Regime Filters (NEW)")
logger.info(f"│ │ • ADX range: {params.get('divergence_min_adx', 'N/A')}-{params.get('divergence_max_adx', 'N/A')}")
logger.info(f"│ │ • ATR multiplier range: {params.get('divergence_min_atr_mult', 'N/A'):.2f}-{params.get('divergence_max_atr_mult', 'N/A'):.2f}")
logger.info(f"│ │")
logger.info(f"│ ┌─ ✅ Confirmation Requirements (NEW)")
logger.info(f"│ │ • volume_multiplier: {params.get('divergence_volume_multiplier', 'N/A')}")
logger.info(f"│ │ • require_volume_increase: {params.get('divergence_require_volume_increase', 'N/A')}")
logger.info(f"│ │ • require_trend_context: {params.get('divergence_require_trend_context', 'N/A')}")
logger.info(f"│ │ • require_1h_alignment: {params.get('divergence_require_1h_alignment', 'N/A')}")
logger.info(f"│ │ • require_bands: {params.get('divergence_require_bands', 'N/A')}")
logger.info(f"│ │")
logger.info(f"│ ┌─ 🎖️ Quality Scoring (NEW)")
logger.info(f"│ │ • price_strength_weight: {params.get('divergence_price_strength_weight', 'N/A'):.2f}")
logger.info(f"│ │ • rsi_strength_weight: {params.get('divergence_rsi_strength_weight', 'N/A'):.2f}")
logger.info(f"│ │ • timespan_weight: {params.get('divergence_timespan_weight', 'N/A'):.2f}")
logger.info(f"│ │ • min_quality_score: {params.get('divergence_min_quality_score', 'N/A'):.2f}")
logger.info(f"│ │")
logger.info(f"│ ┌─ ⏱️ Entry Timing (NEW)")
logger.info(f"│ │ • entry_wait_candles: {params.get('divergence_entry_wait_candles', 'N/A')}")
logger.info(f"│ │ • require_pullback: {params.get('divergence_require_pullback', 'N/A')}")
logger.info(f"│ │")
logger.info(f"│ ┌─ 🛡️ HQ Divergence Filters")
logger.info(f"│ │ LONG: adx_max={params.get('hq_adx_max', 'N/A')}, momentum_lb={params.get('hq_momentum_lookback', 'N/A')}")
logger.info(f"│ │ green_candle={params.get('hq_require_green_candle', 'N/A')}, ema_trend={params.get('hq_ema_trend_filter', 'N/A')}")
logger.info(f"│ │ SHORT: adx_max={params.get('hq_adx_max_short', 'N/A')}, momentum_lb={params.get('hq_momentum_lookback_short', 'N/A')}")
logger.info(f"│ │ red_candle={params.get('hq_require_red_candle', 'N/A')}, ema_trend={params.get('hq_ema_trend_filter_short', 'N/A')}")
logger.info(f"│ │")
logger.info(f"│ ┌─ 🎯 Quality-Based Risk Management (NEW)")
logger.info(f"│ │ • SUPER stop ATR mult: {params.get('divergence_stop_atr_mult_super', 'N/A'):.2f}")
logger.info(f"│ │ • HQ stop ATR mult: {params.get('divergence_stop_atr_mult_hq', 'N/A'):.2f}")
logger.info(f"│ │ • REGULAR stop ATR mult: {params.get('divergence_stop_atr_mult_regular', 'N/A'):.2f}")
logger.info(f"│ │")
logger.info(f"│ └─ 🎯 Quality-Based Profit Targets (NEW)")
logger.info(f"│ │ • SUPER target mult: {params.get('divergence_target_mult_super', 'N/A'):.2f}")
logger.info(f"│ │ • HQ target mult: {params.get('divergence_target_mult_hq', 'N/A'):.2f}")
logger.info(f"│ └─ • REGULAR target mult: {params.get('divergence_target_mult_regular', 'N/A'):.2f}")
# === FISHER TRANSFORM OPTIMIZATION (ENHANCED) ===
logger.info(f"├─ 🎣 FISHER TRANSFORM OPTIMIZATION (Enhanced)")
logger.info(f"│")
logger.info(f"│ ┌─ Core Parameters")
logger.info(f"│ │ • fisher_period: {params.get('fisher_period', 'N/A')}")
logger.info(f"│ │ • fisher_oversold: {params.get('fisher_oversold', 'N/A')}")
logger.info(f"│ │ • fisher_overbought: {params.get('fisher_overbought', 'N/A')}")
logger.info(f"│ │ • fisher_rsi_max_long: {params.get('fisher_rsi_max_long', 'N/A')}")
logger.info(f"│ │ • fisher_rsi_min_short: {params.get('fisher_rsi_min_short', 'N/A')}")
logger.info(f"│ │")
logger.info(f"│ ┌─ 🌡️ Market Regime Filters (NEW)")
logger.info(f"│ │ • ADX range: {params.get('fisher_min_adx', 'N/A')}-{params.get('fisher_max_adx', 'N/A')}")
logger.info(f"│ │ • Volatility range: {params.get('fisher_min_volatility', 'N/A'):.4f}-{params.get('fisher_max_volatility', 'N/A'):.4f}")
logger.info(f"│ │")
logger.info(f"│ ┌─ ✅ Confirmation Requirements (NEW)")
logger.info(f"│ │ • require_volume: {params.get('fisher_require_volume', 'N/A')}")
logger.info(f"│ │ • volume_mult: {params.get('fisher_volume_mult', 'N/A')}")
logger.info(f"│ │ • require_1h_alignment: {params.get('fisher_require_1h_alignment', 'N/A')}")
logger.info(f"│ │ • require_price_action: {params.get('fisher_require_price_action', 'N/A')}")
logger.info(f"│ │")
logger.info(f"│ ┌─ 💪 Signal Strength (NEW)")
logger.info(f"│ │ • min_signal_strength: {params.get('fisher_min_signal_strength', 'N/A'):.2f}")
logger.info(f"│ │ • require_momentum: {params.get('fisher_require_momentum', 'N/A')}")
logger.info(f"│ │ • momentum_lookback: {params.get('fisher_momentum_lookback', 'N/A')}")
logger.info(f"│ │")
logger.info(f"│ ┌─ ⏱️ Entry Timing (NEW)")
logger.info(f"│ │ • entry_wait_candles: {params.get('fisher_entry_wait_candles', 'N/A')}")
logger.info(f"│ │ • require_reversal_candle: {params.get('fisher_require_reversal_candle', 'N/A')}")
logger.info(f"│ │")
logger.info(f"│ └─ 🎯 Risk Management (NEW)")
logger.info(f"│ │ • stop_atr_mult: {params.get('fisher_stop_atr_mult', 'N/A'):.2f}")
logger.info(f"│ └─ • profit_target_mult: {params.get('fisher_profit_target_mult', 'N/A'):.2f}")
# === OTHER SIGNAL INDICATORS ===
logger.info(f"├─ ┌─ 📉 OTHER SIGNAL INDICATORS")
logger.info(f"│ │ • MACD: fast={params.get('macd_fast', 'N/A')}, slow={params.get('macd_slow', 'N/A')}, signal={params.get('macd_signal', 'N/A')}")
logger.info(f"│ │ • Stochastic: k={params.get('stoch_k', 'N/A')}, d={params.get('stoch_d', 'N/A')}")
logger.info(f"│ │ • Williams %R: {params.get('willr_period', 'N/A')}")
logger.info(f"│ │ • CCI: {params.get('cci_period', 'N/A')}")
logger.info(f"│ │ • Momentum: {params.get('mom_period', 'N/A')}")
logger.info(f"│ │ • CMF: {params.get('cmf_period', 'N/A')}")
logger.info(f"│ │ • NATR: {params.get('natr_period', 'N/A')}")
logger.info(f"│ └─ • Choppiness: {params.get('chop_period', 'N/A')}")
# === GLOBAL EXIT OPTIMIZATION ===
logger.info(f"├─ 🚪 GLOBAL EXIT OPTIMIZATION")
logger.info(f"│")
logger.info(f"│ ┌─ Session Multipliers")
logger.info(f"│ │ • overlap: {params.get('session_multiplier_overlap', 'N/A'):.2f}")
logger.info(f"│ │ • major: {params.get('session_multiplier_major', 'N/A'):.2f}")
logger.info(f"│ │ • volatility_sensitivity: {params.get('volatility_sensitivity', 'N/A')}")
logger.info(f"│ │ • high_signal_mult: {params.get('high_signal_multiplier', 'N/A'):.2f}")
logger.info(f"│ │ • low_signal_mult: {params.get('low_signal_multiplier', 'N/A'):.2f}")
logger.info(f"│ │")
logger.info(f"│ ┌─ Base Targets")
logger.info(f"│ │ • medium_term_base: {params.get('medium_term_base_target', 'N/A'):.3f} ({params.get('medium_term_base_target', 0)*100:.1f}%)")
logger.info(f"│ │ • trend_profit: {params.get('trend_profit_target', 'N/A'):.3f} ({params.get('trend_profit_target', 0)*100:.1f}%)")
logger.info(f"│ │")
logger.info(f"│ ┌─ RSI Exit Conditions")
logger.info(f"│ │ • overbought: {params.get('rsi_exit_overbought', 'N/A')}")
logger.info(f"│ │ • oversold: {params.get('rsi_exit_oversold', 'N/A')}")
logger.info(f"│ │ • extreme_min_profit: {params.get('rsi_extreme_min_profit', 'N/A'):.3f} ({params.get('rsi_extreme_min_profit', 0)*100:.1f}%)")
logger.info(f"│ │")
logger.info(f"│ ┌─ Momentum Exit")
logger.info(f"│ │ • fade_min_profit: {params.get('momentum_fade_min_profit', 'N/A'):.3f} ({params.get('momentum_fade_min_profit', 0)*100:.1f}%)")
logger.info(f"│ │ • fade_threshold: {params.get('momentum_fade_threshold', 'N/A'):.3f} ({params.get('momentum_fade_threshold', 0)*100:.1f}%)")
logger.info(f"│ │")
logger.info(f"│ ┌─ Extended Duration Management")
logger.info(f"│ │ • 2hr target: {params.get('extended_2hr_target', 'N/A'):.3f} ({params.get('extended_2hr_target', 0)*100:.1f}%)")
logger.info(f"│ │ • 3hr target: {params.get('extended_3hr_target', 'N/A'):.3f} ({params.get('extended_3hr_target', 0)*100:.1f}%)")
logger.info(f"│ │ • 5hr target: {params.get('extended_5hr_target', 'N/A'):.3f} ({params.get('extended_5hr_target', 0)*100:.1f}%)")
logger.info(f"│ │")
logger.info(f"│ ┌─ Market Protection")
logger.info(f"│ │ • friday_close: {params.get('friday_close_min_profit', 'N/A'):.3f} ({params.get('friday_close_min_profit', 0)*100:.1f}%)")
logger.info(f"│ │ • overnight: {params.get('overnight_min_profit', 'N/A'):.3f} ({params.get('overnight_min_profit', 0)*100:.1f}%)")
logger.info(f"│ │ • low_liquidity: {params.get('low_liquidity_min_profit', 'N/A'):.3f} ({params.get('low_liquidity_min_profit', 0)*100:.1f}%)")
logger.info(f"│ │")
logger.info(f"│ ┌─ Hybrid Exit Parameters")
logger.info(f"│ │ • band_exit_tolerance: {params.get('band_exit_tolerance', 'N/A'):.4f}")
logger.info(f"│ │ • atr_trailing_mult: {params.get('atr_trailing_multiplier', 'N/A'):.2f}")
logger.info(f"│ │ • atr_trail_min_profit: {params.get('atr_trail_min_profit', 'N/A'):.3f} ({params.get('atr_trail_min_profit', 0)*100:.1f}%)")
logger.info(f"│ │ • emergency_exit_pct: {params.get('emergency_exit_pct', 'N/A'):.2f} ({params.get('emergency_exit_pct', 0)*100:.0f}%)")
logger.info(f"│ │")
logger.info(f"│ ┌─ Sideways Market Detection")
logger.info(f"│ │ • adx_max: {params.get('sideways_adx_max', 'N/A')}")
logger.info(f"│ │ • kc_width_max: {params.get('sideways_kc_width_max', 'N/A'):.4f}")
logger.info(f"│ │ • ema_flat_max: {params.get('sideways_ema_flat_max', 'N/A'):.4f}")
logger.info(f"│ │ • range_max: {params.get('sideways_range_max', 'N/A'):.3f}")
logger.info(f"│ │ • min_conditions: {params.get('sideways_min_conditions', 'N/A')}")
logger.info(f"│ │")
logger.info(f"│ ┌─ Reversal Exits")
logger.info(f"│ │ • min_profit_long: {params.get('reversal_min_profit_long', 'N/A'):.3f} ({params.get('reversal_min_profit_long', 0)*100:.1f}%)")
logger.info(f"│ │ • min_profit_short: {params.get('reversal_min_profit_short', 'N/A'):.3f} ({params.get('reversal_min_profit_short', 0)*100:.1f}%)")
logger.info(f"│ │")
logger.info(f"│ └─ Trend Following Exit")
logger.info(f"│ │ • ema_break_periods: {params.get('trend_ema_break_periods', 'N/A')}")
logger.info(f"│ └─ • atr_multiplier: {params.get('atr_multiplier', 'N/A'):.2f}")
# === OPTIMIZATION SUMMARY ===
logger.info(f"├─ 📋 OPTIMIZATION SUMMARY")
# Count parameters by category
divergence_params = len([k for k in params.keys() if 'divergence_' in k or k in ['window', 'index_range']])
fisher_params = len([k for k in params.keys() if 'fisher_' in k])
exit_params = len([k for k in params.keys() if any(x in k for x in [
'session_', 'extended_', 'friday_', 'overnight_', 'low_liquidity_',
'band_exit', 'atr_trailing', 'emergency_exit', 'sideways_',
'reversal_', 'trend_ema', 'momentum_fade', 'rsi_exit', 'medium_term'
])])
indicator_params = len([k for k in params.keys() if any(x in k for x in [
'rsi_period', 'atr_period', 'bb_period', 'bb_std', 'macd_', 'ema_',
'stoch_', 'willr_', 'cci_', 'mom_', 'cmf_', 'natr_', 'chop_', 'volume_sma'
])])
filter_params = len([k for k in params.keys() if any(x in k for x in [
'min_signal_strength', 'volume_threshold', 'volume_factor',
'adx_threshold', 'min_volatility', 'max_volatility',
'rsi_overbought', 'rsi_oversold'
])])
hq_params = len([k for k in params.keys() if 'hq_' in k])
logger.info(f"│ • Divergence parameters: {divergence_params} (ENHANCED with regime filters)")
logger.info(f"│ • Fisher Transform parameters: {fisher_params} (ENHANCED with regime filters)")
logger.info(f"│ • HQ Divergence filters: {hq_params}")
logger.info(f"│ • Exit parameters: {exit_params}")
logger.info(f"│ • Indicator parameters: {indicator_params}")
logger.info(f"│ • Global filter parameters: {filter_params}")
logger.info(f"│ • Total parameters: {len(params)}")
logger.info(f"└─ 🎯 Live optimization on {len(params)} parameters for {pair}")
logger.info(f"✅ [OPTUNA] Parameter logging complete for {pair}")
def pretrain_with_historical_data(self, pair: str, strategy_instance) -> bool:
"""Stub for compatibility - real Optuna doesn't need pre-training"""
return False
class PlotConfig():
def __init__(self):
self.config = {
'main_plot': {
# Try direct column names first to test
'bollinger_upperband': {'color': 'rgba(4,137,122,0.7)'},
'kc_upperband': {'color': 'rgba(4,146,250,0.7)'},
'kc_middleband': {'color': 'rgba(4,146,250,0.7)'},
'kc_lowerband': {'color': 'rgba(4,146,250,0.7)'},
'bollinger_lowerband': {
'color': 'rgba(4,137,122,0.7)',
'fill_to': 'bollinger_upperband',
'fill_color': 'rgba(4,137,122,0.07)'
},
'ema9': {'color': 'purple'},
'ema20': {'color': 'yellow'},
'ema50': {'color': 'red'},
'ema200': {'color': 'white'},
'trend_1h_1h': {'color': 'orange'},
},
'subplots': {
"RSI": {
'rsi': {'color': 'green'}
},
"ATR": {
'atr': {'color': 'firebrick'}
},
"Signal Strength": {
'signal_strength': {'color': 'blue'}
}
}
}
def add_total_divergences_in_config(self, dataframe):
# Test if columns exist before adding them
if 'total_bullish_divergences' in dataframe.columns:
self.config['main_plot']['total_bullish_divergences'] = {
"plotly": {
'mode': 'markers',
'marker': {
'symbol': 'diamond',
'size': 11,
'color': 'green'
}
}
}
if 'total_bearish_divergences' in dataframe.columns:
self.config['main_plot']['total_bearish_divergences'] = {
"plotly": {
'mode': 'markers',
'marker': {
'symbol': 'diamond',
'size': 11,
'color': 'crimson'
}
}
}
return self
class AlexBandSniperV10AI(IStrategy):
"""
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║ ║
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║ ╚═╝ ╚═╝╚══════╝╚══════╝╚═╝ ╚═╝ ╚═════╝╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚═════╝ ╚═╝ ╚═╝╚═╝╚═╝ ╚═══╝ ╚═════╝ ║
║ ║
║ Advanced RL + Optuna Trading Strategy ║
║ Enhanced Multi-Pair Optimization ║
║ Machine Learning Powered ║
║ ║
╚════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════╝
Alex BandSniper on 15m Timeframe - OPTIMIZED VERSION WITH OPTUNA INTEGRATION
Version 9-Optuna - Claude optimized Entry & Exit with Optuna Management # CHANGED: Versionshinweis angepasst
Key improvements:
--Divergence Optuna Full Integration
--Fisher Optuna Full Integration
--Full Optuna Integration
--Fixed Repainting
- Fixed Entry Tags Assignment
- Added better Custom_Exit
- Enabled ROI and TRAILING
- Integrated OPTUNA Fully
- Opimizing Daily Optimizing
- Enhanced Parameters for Optuna
- Optuna integration
- Fixed ROI and Trailing adjusted Custom Exits
- Fixed Entry Signals
- Included 1h Informative Timeframe
- Multi-timeframe analysis (1h trend confirmation)
- Enhanced signal filtering with minimum divergence counts
- Volume and volatility filters
- Adaptive position sizing based on signal strength
- Improved risk management
- ADDED: Optuna-based parameter optimization per coin # ADDED: Neuer Kommentar
- ADDED: Historical pre-training for better startup performance # ADDED: Neuer Kommentar
- ADDED: Dynamic parameter adjustment based on performance # ADDED: Neuer Kommentar
"""
INTERFACE_VERSION = 3
def version(self) -> str:
return "V97-Optuna"
class HyperOpt:
# Define a custom stoploss space.
def stoploss_space():
return [SKDecimal(-0.15, -0.03, decimals=2, name='stoploss')]
# Define a custom max_open_trades space
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'),
]
# Minimal ROI designed for the strategy.
minimal_roi = {
"0": 0.99 # Disabled - let custom_exit handle everything intelligently
}
# Optimal stoploss designed for the strategy.
stoploss = -0.25
can_short = True
use_custom_stoploss = False
leverage_value = 7.0 # Reduced leverage for better risk management
trailing_stop = False
#trailing_stop_positive = 0.04 # Activate trailing at 4% profit
#trailing_stop_positive_offset = 0.041
#trailing_only_offset_is_reached = True # Only trail after 4% reached
# Optimal timeframe for the strategy.
timeframe = '15m'
timeframe_minutes = timeframe_to_minutes(timeframe)
# Run "populate_indicators()" only for new candle.
process_only_new_candles = True
# These values can be overridden in the "exit_pricing" section in the config.
use_exit_signal = True
exit_profit_only = False
ignore_roi_if_entry_signal = False
use_custom_exits = True
use_entry_signal = True
# In your hyperopt parameters, consider these more permissive defaults:
# === HYBRID EXIT PARAMETERS ===
# Band exit settings
use_band_exits = BooleanParameter(default=True, space='sell', optimize=False)
band_exit_tolerance = DecimalParameter(0.995, 1.005, default=0.998, decimals=3, space='sell', optimize=True, load=True)
# ATR trailing settings
use_atr_trailing = BooleanParameter(default=True, space='sell', optimize=False)
atr_trailing_multiplier = DecimalParameter(1.5, 3.5, default=2.0, decimals=1, space='sell', optimize=True, load=True)
atr_trail_min_profit = DecimalParameter(0.01, 0.03, default=0.015, decimals=3, space='sell', optimize=True, load=True)
# RSI momentum exit settings
use_rsi_exits = BooleanParameter(default=True, space='sell', optimize=False)
rsi_exit_extreme = IntParameter(70, 85, default=75, space='sell', optimize=True, load=True)
rsi_exit_extreme_profit = DecimalParameter(0.02, 0.05, default=0.03, decimals=3, space='sell', optimize=True, load=True)
# Emergency profit taking
emergency_exit_pct = DecimalParameter(0.12, 0.20, default=0.15, decimals=2, space='sell', optimize=True, load=True)
min_signal_strength = IntParameter(1, 5, default=1, space='buy', optimize=False, load=True) # Reduced from 3-10
volume_threshold = DecimalParameter(1.0, 1.5, default=1.0, decimals=1, space='buy', optimize=True, load=True) # Reduced from 1.1-2.5
# Make ADX less restrictive
adx_threshold = IntParameter(15, 30, default=15, space='buy', optimize=True, load=True) # Reduced from 25-45
# Market Condition Filters
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)
# Volatility Filters
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 Parameters
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 Parameters
trend_strength_threshold = IntParameter(20, 40, default=25, space='buy', optimize=True, load=True)
# Technical Parameters
window = IntParameter(3, 6, default=4, space="buy", optimize=True, load=True)
index_range = IntParameter(20, 50, default=30, space='buy', optimize=True, load=True)
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 10
# Protection parameters
cooldown_lookback = IntParameter(2, 48, default=1, 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=False, space="protection", optimize=True)
# Enhanced protection parameters
use_max_drawdown_protection = BooleanParameter(default=False, 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=1, 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)
# Fisher Transform parameters
# Optional order type mapping.
order_types = {
'entry': 'market',
'exit': 'market',
'stoploss': 'limit',
'stoploss_on_exchange': True
}
# Optional order time in force.
order_time_in_force = {
'entry': 'gtc',
'exit': 'gtc'
}
plot_config = None
def __init__(self, config: dict):
# Signal Performance Tracking
self.signal_performance_file = Path("user_data/strategies/AlexBandSniper_Signal_Performance.csv")
self.signal_performance = self.load_signal_performance()
self.signal_locks = {}
# Configurable parameters - add these to your get_default_params()
self.performance_window = 20 # More data points (was 15)
self.min_trades_for_eval = 12 # More trades before evaluation (was 8)
self.min_win_rate = 0.25 # 25% win rate (was 35%)
self.min_avg_profit = -0.025 # -2.5% average (was -1.5%)
self.lock_duration_hours = 8 # Shorter lock period (was 12)
# ============================================================================
# ML PREDICTIVE ENTRY SYSTEM INITIALIZATION - MUST BE BEFORE super().__init__
# ============================================================================
self.ml_enabled = True # Set False to disable ML predictions
self.ml_model = None
self.ml_trained = False
self.ml_model_trained = False # Flag for backtest training
self.ml_training_data = []
self.ml_feature_names = [
'rsi', 'adx', 'atr_norm', 'volume_ratio', 'volatility',
'ema_alignment', 'bb_position', 'signal_strength',
'close_vs_ema21', 'close_vs_ema50', 'trend_strength',
'momentum', 'divergence_present', 'recent_profit_avg'
]
super().__init__(config)
print(ASCII_ART, flush=True)
logger.info(f"🚀 [STRATEGY] Initializing {self.__class__.__name__}") # AUTO-UPDATED: Klassenname wird automatisch angepasst
# Initialize simple Optuna manager replacement
try:
self.optuna_manager = RealOptunaManager("AlexBandSniperV10AI") # CHANGED: Strategy name angepasst
logger.info(f"✅ [OPTUNA] Successfully initialized RealOptunaManager")
except Exception as e:
logger.error(f"❌ [OPTUNA] Failed to initialize RealOptunaManager: {e}")
self.optuna_manager = None
# Per-coin optimized parameters
self.coin_params: Dict[str, Dict] = {} # ADDED: Dictionary für coin-spezifische Parameter
# Track performance for optimization
self.coin_performance: Dict[str, float] = {} # ADDED: Performance-Tracking pro Coin
# Optimization settings - CHANGED: Enable proactive optimization
self.enable_dynamic_optimization = True # ADDED: Dynamische Optimierung aktiviert (deaktivieren für Backtest)
self.optimization_min_trades = 0 # CHANGED: No minimum trades required for startup optimization
logger.info(f"⚙️ [STRATEGY] Dynamic optimization: {'ENABLED' if self.enable_dynamic_optimization else 'DISABLED'}")
logger.info(f"⚙️ [STRATEGY] Minimum trades for optimization: {self.optimization_min_trades}")
logger.info(f"✅ [STRATEGY] {self.__class__.__name__} initialization completed")
# ML will be initialized in bot_start() where dp is available
if ML_AVAILABLE and self.ml_enabled:
logger.info("🔍 [INIT] ML system enabled - training will happen in bot_start()")
else:
logger.info(f"🧠 [INIT] ML predictions disabled")
def bot_start(self, **kwargs) -> None:
"""
Called once when the bot starts (after everything is initialized)
Perfect place to do ML training since DataProvider is ready
"""
if ML_AVAILABLE and self.ml_enabled:
logger.info("🔍 [BOT_START] Checking ML model status...")
# Try to load existing model first
self.validate_and_load_ml_model()
self.load_ml_training_data()
logger.info(f"📊 [BOT_START] After loading: ml_trained={self.ml_trained}, samples={len(self.ml_training_data)}")
if not self.ml_trained:
logger.info("🎯 [BOT_START] No ML model found - starting backtest training...")
success = self.train_ml_from_backtest()
if success:
logger.info("✅ [BOT_START] ML training successful!")
self.ml_model_trained = True # Set flag so trades can begin
self.ml_trained = True
else:
logger.warning("⚠️ [BOT_START] Backtest training failed - ML disabled")
self.ml_enabled = False
self.ml_model_trained = False
else:
logger.info(f"✅ [BOT_START] ML model already trained with {len(self.ml_training_data)} samples")
self.ml_model_trained = True # Set flag since model is loaded
# ============================================================================
# CUSTOM INDICATOR CALCULATION METHODS (From AlexSmartMoneyAdaptive)
# ============================================================================
def JMA(self, src, length, phase, power):
"""
Jurik Moving Average (JMA)
A smoothed moving average that reduces lag
Args:
src: Price series (typically close)
length: Period for JMA
phase: Phase parameter (typically 0)
power: Power parameter (typically 2)
Returns:
pandas.Series: JMA values
"""
beta = 0.45 * (length - 1) / (0.45 * (length - 1) + 2)
alpha = beta ** power
e0, e1, e2, jma = [0.0] * len(src), [0.0] * len(src), [0.0] * len(src), [0.0] * len(src)
for i in range(1, len(src)):
e0[i] = (1 - alpha) * src[i] + alpha * e0[i - 1]
e1[i] = (src[i] - e0[i]) * (1 - beta) + beta * e1[i - 1]
e2[i] = (e0[i] + phase * e1[i] - jma[i - 1]) * (1 - alpha) ** 2 + alpha ** 2 * e2[i - 1]
jma[i] = e2[i] + jma[i - 1]
return pd.Series(jma, index=src.index)
def calculate_adx(self, dataframe, length):
"""
Calculate ADX (Average Directional Index) with DI+ and DI-
Args:
dataframe: DataFrame with high, low, close columns
length: Period for ADX calculation
Returns:
tuple: (di_plus, di_minus, adx) as pandas Series
"""
high, low, close = dataframe['high'], dataframe['low'], dataframe['close']
# Calculate True Range
tr = pd.concat([
high - low,
abs(high - close.shift()),
abs(low - close.shift())
], axis=1).max(axis=1)
tr_sma = tr.rolling(window=length).mean()
# Calculate Directional Movement
plus_dm = high.diff().clip(lower=0)
minus_dm = -low.diff().clip(upper=0)
# Only keep the larger directional movement
plus_dm = plus_dm.where(plus_dm > minus_dm, 0)
minus_dm = minus_dm.where(minus_dm > plus_dm, 0)
# Smooth the directional movements
plus_dm_sma = plus_dm.rolling(window=length).mean()
minus_dm_sma = minus_dm.rolling(window=length).mean()
# Calculate Directional Indicators
di_plus = 100 * (plus_dm_sma / tr_sma)
di_minus = 100 * (minus_dm_sma / tr_sma)
# Calculate DX and ADX
dx = 100 * abs(di_plus - di_minus) / (di_plus + di_minus)
adx = dx.rolling(window=length).mean()
return di_plus, di_minus, adx
# ============================================================================
# END OF CUSTOM INDICATOR METHODS
# ============================================================================
def get_coin_params(self, pair: str) -> Dict[str, Any]:
"""Get optimized parameters for specific coin using real Optuna"""
if pair not in self.coin_params:
logger.debug(f"🔍 [OPTUNA] Loading parameters for new pair: {pair}")
# Check if optimization is disabled first (for backtesting)
if not self.enable_dynamic_optimization:
logger.info(f"⏸️ [OPTUNA] Dynamic optimization DISABLED for {pair} - using default parameters")
self.coin_params[pair] = self.get_default_params()
return self.coin_params[pair]
# Real Optuna optimization - only if enabled
if self.enable_dynamic_optimization and self.optuna_manager:
logger.info(f"🚀 [OPTUNA] Real Optuna will optimize {pair} based on actual trade results")
if self.optuna_manager:
best_params = self.optuna_manager.get_best_params(pair)
if best_params:
self.coin_params[pair] = best_params
logger.info(f"✅ [OPTUNA] Loaded optimized parameters for {pair}: {len(best_params)} params")
logger.debug(f"📊 [OPTUNA] Parameters from study with {len(self.optuna_manager.studies.get(pair, {}).trials) if pair in self.optuna_manager.studies else 0} trials")
else:
self.coin_params[pair] = self.get_default_params()
logger.info(f"🔧 [OPTUNA] Using default parameters for {pair} (no study found - will optimize after trades)")
# ADD THIS SECTION: Force startup optimization for new pairs
if self.enable_dynamic_optimization and self.optuna_manager:
logger.info(f"🎯 [OPTUNA] Triggering STARTUP OPTIMIZATION for new pair: {pair}")
try:
# Call maybe_optimize_coin with force_startup=True
self.maybe_optimize_coin(pair, force_startup=True)
# After optimization, try to reload the optimized parameters
optimized_params = self.optuna_manager.get_best_params(pair)
if optimized_params:
self.coin_params[pair] = optimized_params
logger.info(f"✅ [OPTUNA] Successfully loaded STARTUP optimized parameters for {pair}")
else:
logger.warning(f"⚠️ [OPTUNA] Startup optimization completed but no parameters returned for {pair}")
except Exception as e:
logger.error(f"❌ [OPTUNA] Startup optimization failed for {pair}: {e}")
# Keep using default parameters if optimization fails
else:
self.coin_params[pair] = self.get_default_params()
logger.warning(f"⚠️ [OPTUNA] RealOptunaManager not available, using default parameters for {pair}")
# ENSURE ALL PARAMETERS ARE PRESENT
default_params = self.get_default_params()
current_params = self.coin_params.get(pair, {})
# Merge defaults with current params (current params override defaults)
complete_params = {**default_params, **current_params}
self.coin_params[pair] = complete_params
logger.debug(f"♻️ [OPTUNA] Using complete parameter set for {pair}")
return self.coin_params[pair]
def get_default_params(self) -> Dict[str, Any]:
"""Get enhanced default parameters - aligned with Optuna optimization"""
return {
# ============================================
# CORE SHARED INDICATORS
# ============================================
'rsi_period': 14,
'atr_period': 14,
'volume_sma_period': 20,
'ema_short_period': 8,
'ema_long_period': 21,
'adx_threshold': 20,
'bb_period': 20,
'bb_std': 2.0,
# ============================================
# GLOBAL FILTERS
# ============================================
'min_signal_strength': 3,
'volume_threshold': 1.2,
'rsi_overbought': 75.0,
'rsi_oversold': 25.0,
'max_volatility': 0.025,
'min_volatility': 0.005,
'volume_factor': 1.5,
# ============================================
# DIVERGENCE PARAMETERS (Enhanced)
# ============================================
# Core Divergence Detection
'window': 4,
'index_range': 30,
'divergence_lookback_period': 5,
'divergence_min_rsi_delta': 4.0,
'divergence_min_price_delta': 1.0,
# Divergence Thresholds
'divergence_rsi_low_threshold': 30,
'divergence_rsi_high_threshold': 70,
# === NEW: Market Regime Filters for Divergence ===
'divergence_min_adx': 20, # Avoid choppy markets
'divergence_max_adx': 40, # Avoid overly trending
'divergence_min_atr_mult': 0.8, # Min volatility
'divergence_max_atr_mult': 3.0, # Max volatility
# === NEW: Divergence Confirmation Requirements ===
'divergence_volume_multiplier': 1.5,
'divergence_require_volume_increase': False,
'divergence_require_trend_context': True,
'divergence_require_1h_alignment': False,
'divergence_require_bands': False,
# === NEW: Divergence Quality Scoring ===
'divergence_price_strength_weight': 1.0,
'divergence_rsi_strength_weight': 1.0,
'divergence_timespan_weight': 1.0,
'divergence_min_quality_score': 5.0,
# === NEW: Entry Timing for Divergence ===
'divergence_entry_wait_candles': 1,
'divergence_require_pullback': False,
# HQ Divergence Filters (LONG)
'hq_adx_max': 30,
'hq_momentum_lookback': 2,
'hq_require_green_candle': True,
'hq_ema_trend_filter': True,
# HQ Divergence Filters (SHORT)
'hq_adx_max_short': 30,
'hq_momentum_lookback_short': 2,
'hq_require_red_candle': True,
'hq_ema_trend_filter_short': True,
# === NEW: Quality-Based Risk Management ===
'divergence_stop_atr_mult_super': 3.0,
'divergence_stop_atr_mult_hq': 3.5,
'divergence_stop_atr_mult_regular': 4.5,
# === NEW: Quality-Based Profit Targets ===
'divergence_target_mult_super': 2.0,
'divergence_target_mult_hq': 1.5,
'divergence_target_mult_regular': 1.3,
# ============================================
# FISHER TRANSFORM PARAMETERS (Enhanced)
# ============================================
# Core Fisher Parameters
'fisher_period': 9,
'fisher_oversold': -2.0,
'fisher_overbought': 2.0,
'fisher_rsi_max_long': 40,
'fisher_rsi_min_short': 60,
# === NEW: Fisher Market Regime Filters ===
'fisher_min_adx': 20,
'fisher_max_adx': 40,
'fisher_min_volatility': 0.005,
'fisher_max_volatility': 0.025,
# === NEW: Fisher Confirmation Requirements ===
'fisher_require_volume': False,
'fisher_volume_mult': 1.5,
'fisher_require_1h_alignment': False,
'fisher_require_price_action': False,
# === NEW: Fisher Signal Strength ===
'fisher_min_signal_strength': 2.0,
'fisher_require_momentum': False,
'fisher_momentum_lookback': 2,
# === NEW: Fisher Entry Timing ===
'fisher_entry_wait_candles': 0,
'fisher_require_reversal_candle': False,
# === NEW: Fisher Risk Management ===
'fisher_stop_atr_mult': 3.5,
'fisher_profit_target_mult': 1.5,
# ============================================
# OTHER SIGNAL INDICATORS
# ============================================
'macd_fast': 12,
'macd_slow': 26,
'macd_signal': 9,
'stoch_k': 14,
'stoch_d': 3,
'willr_period': 14,
'cci_period': 20,
'mom_period': 10,
'cmf_period': 20,
'natr_period': 14,
'chop_period': 14,
# ============================================
# GLOBAL EXIT OPTIMIZATION
# ============================================
# Session Multipliers
'session_multiplier_overlap': 1.3,
'session_multiplier_major': 1.15,
'volatility_sensitivity': 40,
'high_signal_multiplier': 1.2,
'low_signal_multiplier': 0.8,
# Base Targets
'medium_term_base_target': 0.05,
'trend_profit_target': 0.045,
# RSI Exit Conditions
'rsi_exit_overbought': 78,
'rsi_exit_oversold': 22,
'rsi_extreme_min_profit': 0.015,
# Momentum Exit
'momentum_fade_min_profit': 0.012,
'momentum_fade_threshold': 0.02,
# Extended Duration Management
'extended_2hr_target': 0.025,
'extended_3hr_target': 0.015,
'extended_5hr_target': 0.008,
# Market Protection
'friday_close_min_profit': 0.012,
'overnight_min_profit': 0.008,
'low_liquidity_min_profit': 0.01,
# Hybrid Exit Parameters
'band_exit_tolerance': 0.998,
'atr_trailing_multiplier': 2.0,
'atr_trail_min_profit': 0.015,
'emergency_exit_pct': 0.15,
# Sideways Market Detection
'sideways_adx_max': 25,
'sideways_kc_width_max': 0.020,
'sideways_ema_flat_max': 0.015,
'sideways_range_max': 0.06,
'sideways_min_conditions': 2,
# Reversal Exits
'reversal_min_profit_long': 0.008,
'reversal_min_profit_short': 0.008,
# Trend Following Exit
'trend_ema_break_periods': 3,
'atr_multiplier': 3.0,
# ============================================
# SIGNAL LOCKING (Keep your existing logic)
# ============================================
'signal_lock_enabled': True,
'signal_performance_window': 15,
'signal_lock_duration_hours': 12,
'signal_min_win_rate': 0.35,
'signal_min_avg_profit': -0.015,
'signal_min_trades_for_eval': 3,
}
def create_objective_function(self, pair: str):
"""Create objective function that optimizes on LIVE trade performance"""
def objective(trial):
params = {
# ============================================
# CORE SHARED INDICATORS (Used by multiple signals)
# ============================================
'rsi_period': trial.suggest_int('rsi_period', 10, 20),
'atr_period': trial.suggest_int('atr_period', 10, 20),
'volume_sma_period': trial.suggest_int('volume_sma_period', 15, 25),
'ema_short_period': trial.suggest_int('ema_short_period', 6, 12),
'ema_long_period': trial.suggest_int('ema_long_period', 18, 25),
'adx_threshold': trial.suggest_int('adx_threshold', 15, 30),
'bb_period': trial.suggest_int('bb_period', 15, 25),
'bb_std': trial.suggest_float('bb_std', 1.8, 2.5),
# ============================================
# GLOBAL FILTERS (Apply to all signals)
# ============================================
'min_signal_strength': trial.suggest_int('min_signal_strength', 1, 5),
'volume_threshold': trial.suggest_float('volume_threshold', 1.0, 1.5),
'rsi_overbought': trial.suggest_float('rsi_overbought', 65.0, 85.0),
'rsi_oversold': trial.suggest_float('rsi_oversold', 15.0, 35.0),
'max_volatility': trial.suggest_float('max_volatility', 0.015, 0.035),
'min_volatility': trial.suggest_float('min_volatility', 0.003, 0.008),
'volume_factor': trial.suggest_float('volume_factor', 1.2, 2.0),
# ============================================
# DIVERGENCE OPTIMIZATION (Enhanced)
# ============================================
# Core Divergence Detection
'window': trial.suggest_int('window', 3, 6),
'index_range': trial.suggest_int('index_range', 20, 50),
'divergence_lookback_period': trial.suggest_int('divergence_lookback_period', 3, 10),
'divergence_min_rsi_delta': trial.suggest_float('divergence_min_rsi_delta', 2.0, 8.0, step=0.5),
'divergence_min_price_delta': trial.suggest_float('divergence_min_price_delta', 0.5, 3.0, step=0.25),
# Divergence Thresholds
'divergence_rsi_low_threshold': trial.suggest_int('divergence_rsi_low_threshold', 20, 35),
'divergence_rsi_high_threshold': trial.suggest_int('divergence_rsi_high_threshold', 65, 80),
# === NEW: Market Regime Filters for Divergence ===
'divergence_min_adx': trial.suggest_int('divergence_min_adx', 15, 30),
'divergence_max_adx': trial.suggest_int('divergence_max_adx', 35, 50),
'divergence_min_atr_mult': trial.suggest_float('divergence_min_atr_mult', 0.5, 1.5),
'divergence_max_atr_mult': trial.suggest_float('divergence_max_atr_mult', 2.0, 4.0),
# === NEW: Divergence Confirmation Requirements ===
'divergence_volume_multiplier': trial.suggest_float('divergence_volume_multiplier', 1.0, 2.5, step=0.1),
'divergence_require_volume_increase': trial.suggest_categorical('divergence_require_volume_increase', [True, False]),
'divergence_require_trend_context': trial.suggest_categorical('divergence_require_trend_context', [True, False]),
'divergence_require_1h_alignment': trial.suggest_categorical('divergence_require_1h_alignment', [True, False]),
'divergence_require_bands': trial.suggest_categorical('divergence_require_bands', [True, False]),
# === NEW: Divergence Quality Scoring ===
'divergence_price_strength_weight': trial.suggest_float('divergence_price_strength_weight', 0.5, 2.0),
'divergence_rsi_strength_weight': trial.suggest_float('divergence_rsi_strength_weight', 0.5, 2.0),
'divergence_timespan_weight': trial.suggest_float('divergence_timespan_weight', 0.5, 2.0),
'divergence_min_quality_score': trial.suggest_float('divergence_min_quality_score', 3.0, 8.0),
# === NEW: Entry Timing for Divergence ===
'divergence_entry_wait_candles': trial.suggest_int('divergence_entry_wait_candles', 0, 3),
'divergence_require_pullback': trial.suggest_categorical('divergence_require_pullback', [True, False]),
# HQ Divergence Filters (LONG)
'hq_adx_max': trial.suggest_int('hq_adx_max', 25, 35),
'hq_momentum_lookback': trial.suggest_int('hq_momentum_lookback', 1, 3),
'hq_require_green_candle': trial.suggest_categorical('hq_require_green_candle', [True, False]),
'hq_ema_trend_filter': trial.suggest_categorical('hq_ema_trend_filter', [True, False]),
# HQ Divergence Filters (SHORT)
'hq_adx_max_short': trial.suggest_int('hq_adx_max_short', 25, 35),
'hq_momentum_lookback_short': trial.suggest_int('hq_momentum_lookback_short', 1, 3),
'hq_require_red_candle': trial.suggest_categorical('hq_require_red_candle', [True, False]),
'hq_ema_trend_filter_short': trial.suggest_categorical('hq_ema_trend_filter_short', [True, False]),
# === NEW: Quality-Based Risk Management ===
'divergence_stop_atr_mult_super': trial.suggest_float('divergence_stop_atr_mult_super', 2.5, 4.0),
'divergence_stop_atr_mult_hq': trial.suggest_float('divergence_stop_atr_mult_hq', 3.0, 5.0),
'divergence_stop_atr_mult_regular': trial.suggest_float('divergence_stop_atr_mult_regular', 3.5, 6.0),
# === NEW: Quality-Based Profit Targets ===
'divergence_target_mult_super': trial.suggest_float('divergence_target_mult_super', 1.5, 3.0),
'divergence_target_mult_hq': trial.suggest_float('divergence_target_mult_hq', 1.2, 2.5),
'divergence_target_mult_regular': trial.suggest_float('divergence_target_mult_regular', 1.0, 2.0),
# ============================================
# FISHER TRANSFORM OPTIMIZATION (Enhanced)
# ============================================
# Core Fisher Parameters
'fisher_period': trial.suggest_int('fisher_period', 5, 20),
'fisher_oversold': trial.suggest_float('fisher_oversold', -4.0, -1.0, step=0.5),
'fisher_overbought': trial.suggest_float('fisher_overbought', 1.0, 4.0, step=0.5),
'fisher_rsi_max_long': trial.suggest_int('fisher_rsi_max_long', 35, 50),
'fisher_rsi_min_short': trial.suggest_int('fisher_rsi_min_short', 50, 65),
# === NEW: Fisher Market Regime Filters ===
'fisher_min_adx': trial.suggest_int('fisher_min_adx', 15, 30),
'fisher_max_adx': trial.suggest_int('fisher_max_adx', 35, 50),
'fisher_min_volatility': trial.suggest_float('fisher_min_volatility', 0.003, 0.008),
'fisher_max_volatility': trial.suggest_float('fisher_max_volatility', 0.015, 0.035),
# === NEW: Fisher Confirmation Requirements ===
'fisher_require_volume': trial.suggest_categorical('fisher_require_volume', [True, False]),
'fisher_volume_mult': trial.suggest_float('fisher_volume_mult', 1.0, 2.5),
'fisher_require_1h_alignment': trial.suggest_categorical('fisher_require_1h_alignment', [True, False]),
'fisher_require_price_action': trial.suggest_categorical('fisher_require_price_action', [True, False]),
# === NEW: Fisher Signal Strength ===
'fisher_min_signal_strength': trial.suggest_float('fisher_min_signal_strength', 1.5, 3.5),
'fisher_require_momentum': trial.suggest_categorical('fisher_require_momentum', [True, False]),
'fisher_momentum_lookback': trial.suggest_int('fisher_momentum_lookback', 1, 3),
# === NEW: Fisher Entry Timing ===
'fisher_entry_wait_candles': trial.suggest_int('fisher_entry_wait_candles', 0, 2),
'fisher_require_reversal_candle': trial.suggest_categorical('fisher_require_reversal_candle', [True, False]),
# === NEW: Fisher Risk Management ===
'fisher_stop_atr_mult': trial.suggest_float('fisher_stop_atr_mult', 2.5, 5.0),
'fisher_profit_target_mult': trial.suggest_float('fisher_profit_target_mult', 1.2, 2.5),
# ============================================
# OTHER SIGNAL INDICATORS (Keep minimal)
# ============================================
'macd_fast': trial.suggest_int('macd_fast', 8, 16),
'macd_slow': trial.suggest_int('macd_slow', 22, 30),
'macd_signal': trial.suggest_int('macd_signal', 6, 12),
'stoch_k': trial.suggest_int('stoch_k', 10, 18),
'stoch_d': trial.suggest_int('stoch_d', 2, 5),
'willr_period': trial.suggest_int('willr_period', 10, 20),
'cci_period': trial.suggest_int('cci_period', 15, 25),
'mom_period': trial.suggest_int('mom_period', 8, 15),
'cmf_period': trial.suggest_int('cmf_period', 15, 25),
'natr_period': trial.suggest_int('natr_period', 10, 18),
'chop_period': trial.suggest_int('chop_period', 10, 18),
# ============================================
# GLOBAL EXIT OPTIMIZATION
# ============================================
# Session Multipliers
'session_multiplier_overlap': trial.suggest_float('session_multiplier_overlap', 1.1, 1.5),
'session_multiplier_major': trial.suggest_float('session_multiplier_major', 1.0, 1.3),
'volatility_sensitivity': trial.suggest_int('volatility_sensitivity', 25, 60),
'high_signal_multiplier': trial.suggest_float('high_signal_multiplier', 1.0, 1.4),
'low_signal_multiplier': trial.suggest_float('low_signal_multiplier', 0.6, 1.0),
# Base Targets
'medium_term_base_target': trial.suggest_float('medium_term_base_target', 0.035, 0.065),
'trend_profit_target': trial.suggest_float('trend_profit_target', 0.03, 0.06),
# RSI Exit Conditions
'rsi_exit_overbought': trial.suggest_float('rsi_exit_overbought', 72, 85),
'rsi_exit_oversold': trial.suggest_float('rsi_exit_oversold', 15, 28),
'rsi_extreme_min_profit': trial.suggest_float('rsi_extreme_min_profit', 0.01, 0.025),
# Momentum Exit
'momentum_fade_min_profit': trial.suggest_float('momentum_fade_min_profit', 0.008, 0.02),
'momentum_fade_threshold': trial.suggest_float('momentum_fade_threshold', 0.015, 0.03),
# Extended Duration Management
'extended_2hr_target': trial.suggest_float('extended_2hr_target', 0.015, 0.035),
'extended_3hr_target': trial.suggest_float('extended_3hr_target', 0.008, 0.025),
'extended_5hr_target': trial.suggest_float('extended_5hr_target', 0.005, 0.015),
# Market Protection
'friday_close_min_profit': trial.suggest_float('friday_close_min_profit', 0.008, 0.02),
'overnight_min_profit': trial.suggest_float('overnight_min_profit', 0.005, 0.015),
'low_liquidity_min_profit': trial.suggest_float('low_liquidity_min_profit', 0.006, 0.018),
# Hybrid Exit Parameters
'band_exit_tolerance': trial.suggest_float('band_exit_tolerance', 0.995, 1.005, step=0.001),
'atr_trailing_multiplier': trial.suggest_float('atr_trailing_multiplier', 1.5, 3.5, step=0.1),
'atr_trail_min_profit': trial.suggest_float('atr_trail_min_profit', 0.01, 0.03, step=0.005),
'emergency_exit_pct': trial.suggest_float('emergency_exit_pct', 0.12, 0.20, step=0.01),
# Sideways Market Detection
'sideways_adx_max': trial.suggest_int('sideways_adx_max', 20, 30),
'sideways_kc_width_max': trial.suggest_float('sideways_kc_width_max', 0.015, 0.029, step=0.002),
'sideways_ema_flat_max': trial.suggest_float('sideways_ema_flat_max', 0.010, 0.024, step=0.002),
'sideways_range_max': trial.suggest_float('sideways_range_max', 0.04, 0.08, step=0.005),
'sideways_min_conditions': trial.suggest_int('sideways_min_conditions', 2, 3),
# Reversal Exits
'reversal_min_profit_long': trial.suggest_float('reversal_min_profit_long', 0.005, 0.015),
'reversal_min_profit_short': trial.suggest_float('reversal_min_profit_short', 0.005, 0.015),
# Trend Following Exit
'trend_ema_break_periods': trial.suggest_int('trend_ema_break_periods', 2, 5),
'atr_multiplier': trial.suggest_float('atr_multiplier', 2.0, 4.0),
}
# ============================================
# LIVE TRADE PERFORMANCE EVALUATION
# ============================================
recent_trades = self.get_recent_trade_performance(pair)
# === FIX: Allow startup optimization with 0 trades ===
MIN_TRADES_FOR_OPTIMIZATION = 2
if len(recent_trades) < MIN_TRADES_FOR_OPTIMIZATION:
# Not enough trades yet - return neutral score to allow parameter exploration
logger.debug(f"⏳ [OPTUNA] Insufficient trades for {pair}: {len(recent_trades)}/{MIN_TRADES_FOR_OPTIMIZATION} - using exploration mode")
return 0.0 # ← THIS IS THE KEY! Returns 0.0 instead of raising TrialPruned
# Extract profit percentages from trades
profits = [trade['profit_pct'] for trade in recent_trades]
# Calculate score (average profit)
score = sum(profits) / len(profits)
return score
return objective
def get_recent_trade_performance(self, pair: str) -> List[float]:
"""Get recent trade performance for optimization"""
try:
from freqtrade.persistence import Trade
trades = Trade.get_trades_proxy(pair=pair)
if not trades:
return []
# Get last 5 trades for this pair
recent_trades = trades[-5:] if len(trades) >= 5 else trades
# Calculate profit ratios
# Extract performance data
performance = []
for trade in recent_trades:
if trade.close_date: # Only closed trades
performance.append({
'profit_pct': trade.close_profit * 100,
'trade_id': trade.id,
'open_date': trade.open_date,
'close_date': trade.close_date
})
logger.debug(f"📊 [OPTUNA] Found {len(performance)} completed trades for {pair}")
return performance
except Exception as e:
logger.error(f"❌ [OPTUNA] Failed to get trade performance for {pair}: {e}")
return []
def daily_optimization_check(self):
"""Enhanced optimization check with smarter scheduling"""
try:
if not self.optuna_manager:
return
current_time = time.time()
all_pairs = list(self.coin_params.keys())
if not all_pairs:
logger.info("No pairs available for optimization yet")
return
optimized_count = 0
for pair in all_pairs:
try:
if self.should_retrain_pair(pair, current_time):
logger.info(f"🔄 Optimizing {pair}")
self.optuna_manager.optimize_coin(pair, self.create_objective_function(pair), n_trials=60)
# Track last optimization time
setattr(self, f'last_optimization_{pair.replace("/", "_").replace(":", "_")}', current_time)
optimized_count += 1
except Exception as e:
logger.error(f"❌ Optimization failed for {pair}: {e}")
logger.info(f"✅ Optimization check completed: {optimized_count}/{len(all_pairs)} pairs optimized")
except Exception as e:
logger.error(f"❌ Daily optimization check failed: {e}")
def should_retrain_pair(self, pair: str, current_time: float) -> bool:
"""Smart retraining logic with multiple safeguards"""
from datetime import datetime
last_opt_time = getattr(self, f'last_optimization_{pair.replace("/", "_").replace(":", "_")}', 0)
trades_since_last = self.get_trades_since_optimization(pair, last_opt_time)
# === TIMING RULES ===
# 1. Get current hour (UTC)
current_hour = datetime.utcnow().hour
# 2. Only optimize during QUIET hours (2 AM - 6 AM UTC)
is_quiet_time = 2 <= current_hour <= 6
if not is_quiet_time:
return False # Don't optimize during active trading hours
# 3. Minimum time between optimizations: 48 hours (2 days)
time_since_last = current_time - last_opt_time
min_time_between_opts = 172800 # 48 hours in seconds
if time_since_last < min_time_between_opts:
return False # Too soon since last optimization
# === DATA REQUIREMENTS ===
# 4. Need at least 10 completed trades for meaningful optimization
if trades_since_last < 10:
return False # Not enough data yet
# === PERFORMANCE CHECK ===
# 5. Check if performance is degrading
recent_performance = self.get_recent_trade_performance(pair)
if len(recent_performance) >= 5:
avg_profit = sum(recent_performance) / len(recent_performance)
win_rate = sum(1 for p in recent_performance if p > 0) / len(recent_performance)
# Only optimize if performance is poor OR it's been a long time
is_performing_poorly = (avg_profit < -0.005) or (win_rate < 0.4)
been_very_long_time = time_since_last > 604800 # 7 days
if is_performing_poorly:
logger.info(f"🔴 {pair} performing poorly - triggering optimization (avg: {avg_profit:.4f}, WR: {win_rate:.2%})")
return True
elif been_very_long_time:
logger.info(f"⏰ {pair} hasn't been optimized in 7+ days - triggering maintenance optimization")
return True
else:
logger.debug(f"✅ {pair} performing well - skipping optimization (avg: {avg_profit:.4f}, WR: {win_rate:.2%})")
return False
# 6. If we don't have enough performance data but have trades, optimize
if trades_since_last >= 10:
logger.info(f"📊 {pair} has {trades_since_last} trades - triggering optimization")
return True
return False
def get_trades_since_optimization(self, pair: str, last_opt_time: float) -> int:
"""Count trades since last optimization"""
try:
from freqtrade.persistence import Trade
from datetime import datetime
last_opt_datetime = datetime.fromtimestamp(last_opt_time) if last_opt_time > 0 else datetime.min
trades = Trade.get_trades_proxy(pair=pair)
if not trades:
return 0
recent_trades = [t for t in trades if t.open_date_utc > last_opt_datetime]
return len(recent_trades)
except Exception as e:
logger.debug(f"Failed to count recent trades for {pair}: {e}")
return 0
def load_signal_performance(self):
"""Load signal performance history from CSV file"""
if os.path.exists(self.signal_performance_file):
try:
all_trades = []
with open(self.signal_performance_file, 'r', encoding='utf-8') as f:
reader = csv.DictReader(f, delimiter=';')
for row in reader:
# Convert profit to float and win to boolean
trade_record = {
'pair': row['pair'],
'entry_tag': row['entry_tag'],
'profit': float(row['profit']),
'timestamp': row['timestamp'],
'win': row['win'].lower() == 'true'
}
all_trades.append(trade_record)
# Store loaded trades
self.all_signal_trades = all_trades
# Rebuild the nested format for signal locking
signal_perf = {}
for trade in all_trades:
perf_key = f"{trade['pair']}_{trade['entry_tag']}"
if perf_key not in signal_perf:
signal_perf[perf_key] = []
signal_perf[perf_key].append({
'profit': trade['profit'],
'timestamp': trade['timestamp'],
'win': trade['win']
})
print(f"✅ Loaded {len(all_trades)} trades from CSV")
return signal_perf
except Exception as e:
print(f"Failed to load CSV: {e}")
return {}
return {}
def save_signal_performance(self):
"""Save signal performance to CSV file - Excel friendly"""
try:
os.makedirs(os.path.dirname(self.signal_performance_file), exist_ok=True)
# Get all trades
all_trades = getattr(self, 'all_signal_trades', [])
if not all_trades:
print("No trades to save")
return
# Write to CSV
with open(self.signal_performance_file, 'w', newline='', encoding='utf-8') as f:
# Define headers with semicolon separator
fieldnames = ['pair', 'entry_tag', 'profit', 'timestamp', 'win']
writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter=';')
# Write header
writer.writeheader()
# Write all trades
for trade in all_trades:
writer.writerow(trade)
print(f"✅ Saved {len(all_trades)} trades to CSV")
except Exception as e:
print(f"Failed to save signal performance: {e}")
def is_signal_locked(self, entry_tag: str, pair: str) -> bool:
"""Check if a signal is locked due to poor performance"""
if not hasattr(self, 'signal_locks'):
return False
lock_key = f"{pair}_{entry_tag}"
if lock_key not in self.signal_locks:
return False
# Check if lock has expired (12 hours)
from datetime import timezone
if self.signal_locks[lock_key] > datetime.now(timezone.utc):
return True
def update_signal_performance(self, entry_tag, pair, profit_ratio):
"""Update performance tracking for a signal - Excel-friendly format + locking support"""
# Create trade record with all info in one line (Excel-friendly)
trade_record = {
'pair': pair,
'entry_tag': entry_tag,
'profit': profit_ratio,
'timestamp': datetime.now().isoformat(),
'win': profit_ratio > 0
}
# Store in flat list for Excel analysis
if not hasattr(self, 'all_signal_trades'):
self.all_signal_trades = []
self.all_signal_trades.append(trade_record)
# Keep only recent window
if len(self.all_signal_trades) > 500:
self.all_signal_trades = self.all_signal_trades[-500:]
# Keep the nested format for the locking logic to work
perf_key = f"{pair}_{entry_tag}"
if perf_key not in self.signal_performance:
self.signal_performance[perf_key] = []
self.signal_performance[perf_key].append({
'profit': profit_ratio,
'timestamp': datetime.now().isoformat(),
'win': profit_ratio > 0
})
if len(self.signal_performance[perf_key]) > self.performance_window:
self.signal_performance[perf_key] = self.signal_performance[perf_key][-self.performance_window:]
# Evaluate if signal should be locked
self.evaluate_signal_lock(entry_tag, pair)
# Save to file
self.save_signal_performance()
def evaluate_signal_lock(self, entry_tag, pair):
"""Evaluate if signal should be locked based on recent performance"""
perf_key = f"{pair}_{entry_tag}"
recent_trades = self.signal_performance.get(perf_key, [])
# Need minimum trades for evaluation
if len(recent_trades) < self.min_trades_for_eval:
return
# Calculate performance metrics
profits = [trade['profit'] for trade in recent_trades]
wins = sum(1 for profit in profits if profit > 0)
win_rate = wins / len(profits)
avg_profit = sum(profits) / len(profits)
# Check if signal should be locked
should_lock = (
win_rate < self.min_win_rate or
avg_profit < self.min_avg_profit
)
if should_lock:
lock_until = datetime.now() + timedelta(hours=self.lock_duration_hours)
lock_key = f"{pair}_{entry_tag}"
self.signal_locks[lock_key] = lock_until
print(f"LOCKED SIGNAL: {entry_tag} for {pair}")
print(f" Win Rate: {win_rate:.1%} (min: {self.min_win_rate:.1%})")
print(f" Avg Profit: {avg_profit:.2%} (min: {self.min_avg_profit:.2%})")
print(f" Locked until: {lock_until.strftime('%Y-%m-%d %H:%M')}")
def get_signal_status(self, pair):
"""Get status of all signals for a pair - for debugging"""
status = {}
# Complete list of entry tags from your trading script
entry_tags = [
'Bull_E1', 'Bear_E1',
'Bull_E2', 'Bear_E2',
'Bull_Div_AGG', 'Bear_Div_AGG',
'Bull_Div_CONS', 'Bear_Div_CONS',
'Bull_Div_PB', 'Bear_Div_PB',
'Bull_Div_1H', 'Bear_Div_1H',
'Bull_HDiv_AGG', 'Bear_HDiv_AGG',
'Bull_HDiv_CONS', 'Bear_HDiv_CONS',
'Bull_HDiv_PB', 'Bear_HDiv_PB',
'Bull_HDiv_1H', 'Bear_HDiv_1H',
'Bull_RSV1', 'Bear_RSV1',
'Bull_Scalp_EMA', 'Bear_Scalp_EMA',
'Bull_Scalp_Retest', 'Bear_Scalp_Retest',
]
for entry_tag in entry_tags:
perf_key = f"{pair}_{entry_tag}"
recent_trades = self.signal_performance.get(perf_key, [])
is_locked = self.is_signal_locked(entry_tag, pair)
if recent_trades:
profits = [trade['profit'] for trade in recent_trades]
wins = sum(1 for profit in profits if profit > 0)
win_rate = wins / len(profits) if profits else 0
avg_profit = sum(profits) / len(profits) if profits else 0
status[entry_tag] = {
'locked': is_locked,
'trades_count': len(recent_trades),
'win_rate': win_rate,
'avg_profit': avg_profit,
'last_profit': profits[-1] if profits else None
}
else:
status[entry_tag] = {
'locked': is_locked,
'trades_count': 0,
'win_rate': 0,
'avg_profit': 0,
'last_profit': None
}
return status
def informative_pairs(self):
"""Define additional timeframes to download"""
pairs = self.dp.current_whitelist()
return [(pair, '1h') for pair in pairs]
def _get_historical_data_settings(self) -> Tuple[Path, str, str]:
"""Resolve exchange data location and format for local historical loads."""
exchange_name = self.config.get('exchange', {}).get('name', 'bybit')
data_dir = Path(f"user_data/data/{exchange_name}")
data_format = self.config.get('dataformat_ohlcv', 'feather')
trading_mode = str(self.config.get('trading_mode', '')).lower()
candle_type = self.config.get('candle_type_def')
if not candle_type:
candle_type = 'futures' if trading_mode == 'futures' else 'spot'
return data_dir, str(data_format), str(candle_type)
def _historical_pair_glob_pattern(self, timeframe: str, data_format: str, candle_type: str) -> str:
"""Build the filename pattern used by freqtrade historical pair files."""
if candle_type == 'spot':
return f"*-{timeframe}.{data_format}"
return f"*-{timeframe}-{candle_type}.{data_format}"
def _scan_historical_pairs(self, timeframe: str, data_dir: Path, data_format: str, candle_type: str) -> List[str]:
"""Scan locally downloaded candles and rebuild pair names from filenames."""
scan_dir = data_dir / candle_type if candle_type != 'spot' else data_dir
if not scan_dir.exists():
logger.warning(f"⚠️ [ML BACKTEST] Historical data directory missing: {scan_dir}")
return []
pattern = self._historical_pair_glob_pattern(timeframe, data_format, candle_type)
pair_files = sorted(scan_dir.glob(pattern))
pairs: List[str] = []
for path in pair_files:
stem = path.stem
suffix = f"-{timeframe}" if candle_type == 'spot' else f"-{timeframe}-{candle_type}"
if not stem.endswith(suffix):
continue
pair_token = stem[:-len(suffix)]
parts = pair_token.split("_")
if candle_type == 'spot':
if len(parts) >= 2:
base = parts[0]
quote = "_".join(parts[1:])
pairs.append(f"{base}/{quote}")
elif len(parts) >= 3:
base = parts[0]
quote = parts[1]
settle = "_".join(parts[2:])
pairs.append(f"{base}/{quote}:{settle}")
return sorted(set(pairs))
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""Enhanced indicator population with Optuna optimization and live adaptation"""
pair = metadata['pair']
logger.debug(f"📊 [INDICATORS] Processing {pair} with {len(dataframe)} candles")
# ===== GET OPTIMIZED PARAMETERS =====
try:
coin_params = self.get_coin_params(pair)
logger.debug(f"📈 [OPTUNA] Applied optimized parameters for {pair}")
except Exception as e:
logger.error(f"❌ [OPTUNA] Failed to get coin parameters for {pair}: {e}")
coin_params = self.get_default_params()
# ===== PERIODIC OPTIMIZATION CHECK (24h + Performance) =====
if hasattr(self, 'optuna_manager') and self.optuna_manager:
if self.optuna_manager.should_optimize(pair):
from datetime import datetime
# Check if this is a time-based trigger (24-hour periodic)
if pair in self.optuna_manager.last_optimization_time:
hours_since_last = (datetime.now() - self.optuna_manager.last_optimization_time[pair]).total_seconds() / 3600
if hours_since_last >= self.optuna_manager.optimization_interval_hours:
logger.info(f"⏰ [OPTUNA] {pair} is due for {self.optuna_manager.optimization_interval_hours}h periodic optimization!")
logger.info(f"📊 [OPTUNA] Last optimization: {hours_since_last:.1f}h ago")
# Trigger optimization in background (non-blocking)
try:
self.maybe_optimize_coin(pair, force_startup=False)
# Reload parameters after optimization
updated_params = self.optuna_manager.get_best_params(pair)
if updated_params:
coin_params = updated_params # Update local variable
logger.info(f"✅ [OPTUNA] Reloaded fresh parameters after periodic optimization for {pair}")
except Exception as e:
logger.error(f"❌ [OPTUNA] Periodic optimization failed for {pair}: {e}")
# ===== EXTRACT ALL PARAMETERS =====
# ===== EXTRACT ALL PARAMETERS =====
# ... rest of your code continues
# ===== EXTRACT ALL PARAMETERS =====
# Core indicators
rsi_period = coin_params.get('rsi_period', 14)
atr_period = coin_params.get('atr_period', 14)
volume_sma_period = coin_params.get('volume_sma_period', 20)
ema_short_period = coin_params.get('ema_short_period', 8)
ema_long_period = coin_params.get('ema_long_period', 21)
bb_period = coin_params.get('bb_period', 20)
bb_std = coin_params.get('bb_std', 2.0)
# MACD
macd_fast = coin_params.get('macd_fast', 12)
macd_slow = coin_params.get('macd_slow', 26)
macd_signal = coin_params.get('macd_signal', 9)
# Other oscillators
willr_period = coin_params.get('willr_period', 14)
cci_period = coin_params.get('cci_period', 20)
mom_period = coin_params.get('mom_period', 10)
stoch_k = coin_params.get('stoch_k', 14)
stoch_d = coin_params.get('stoch_d', 3)
cmf_period = coin_params.get('cmf_period', 20)
natr_period = coin_params.get('natr_period', 14)
chop_period = coin_params.get('chop_period', 14)
# Divergence parameters
window = coin_params.get('window', 4)
index_range = coin_params.get('index_range', 30)
divergence_lookback = coin_params.get('divergence_lookback_period', 5)
divergence_min_adx = coin_params.get('divergence_min_adx', 20)
divergence_max_adx = coin_params.get('divergence_max_adx', 40)
divergence_min_atr_mult = coin_params.get('divergence_min_atr_mult', 0.8)
divergence_max_atr_mult = coin_params.get('divergence_max_atr_mult', 3.0)
# Fisher parameters
fisher_period = coin_params.get('fisher_period', 9)
fisher_min_adx = coin_params.get('fisher_min_adx', 20)
fisher_max_adx = coin_params.get('fisher_max_adx', 40)
fisher_min_volatility = coin_params.get('fisher_min_volatility', 0.005)
fisher_max_volatility = coin_params.get('fisher_max_volatility', 0.025)
# ===== MULTI-TIMEFRAME (1H) =====
try:
# Try to get 1h data from DataProvider (normal live/dry-run mode)
if hasattr(self, 'dp') and self.dp is not None:
informative_1h = self.dp.get_pair_dataframe(pair=pair, timeframe='1h')
else:
# During ML training, load from disk
from freqtrade.data.history import load_pair_history
data_dir, data_format, candle_type = self._get_historical_data_settings()
informative_1h = load_pair_history(
datadir=data_dir,
timeframe='1h',
pair=pair,
data_format=data_format,
candle_type=candle_type
)
if informative_1h is not None and len(informative_1h) > 50 and not informative_1h.empty:
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)
else:
self._add_dummy_1h_columns(dataframe)
except Exception as e:
logger.warning(f"1h data unavailable for {pair}: {e}")
self._add_dummy_1h_columns(dataframe)
# ===== VOLUME ANALYSIS =====
dataframe['volume_sma'] = ta.SMA(dataframe['volume'], timeperiod=volume_sma_period)
dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_sma']
dataframe['volume_ratio'] = dataframe['volume_ratio'].fillna(1.0)
dataframe['volume_mean'] = dataframe['volume'].rolling(window=volume_sma_period).mean().fillna(dataframe['volume'])
# ===== VOLATILITY ANALYSIS =====
dataframe['atr'] = ta.ATR(dataframe, timeperiod=atr_period)
dataframe['volatility'] = dataframe['atr'] / dataframe['close']
dataframe['volatility'] = dataframe['volatility'].fillna(0.01)
dataframe['natr'] = ta.NATR(dataframe, timeperiod=natr_period)
# ===== MOMENTUM INDICATORS =====
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=rsi_period)
dataframe['willr'] = ta.WILLR(dataframe, timeperiod=willr_period)
dataframe['cci'] = ta.CCI(dataframe, timeperiod=cci_period)
dataframe['mom'] = ta.MOM(dataframe, timeperiod=mom_period)
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['adx'] = ta.ADX(dataframe)
# MACD
macd = ta.MACD(dataframe, fastperiod=macd_fast, slowperiod=macd_slow, signalperiod=macd_signal)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# Stochastic
stoch = ta.STOCH(dataframe, fastk_period=stoch_k, slowk_period=stoch_d, slowd_period=stoch_d)
dataframe['stoch'] = stoch['slowk']
# Other oscillators
dataframe['roc'] = ta.ROC(dataframe)
dataframe['uo'] = ta.ULTOSC(dataframe)
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
dataframe['cmf'] = chaikin_money_flow(dataframe, cmf_period)
dataframe['obv'] = ta.OBV(dataframe)
# Fill NaN values
oscillator_cols = ['rsi', 'stoch', 'mfi', 'roc', 'uo', 'ao', 'macd', 'macdsignal', 'macdhist',
'cci', 'cmf', 'obv', 'adx', 'willr', 'mom']
for col in oscillator_cols:
if col in dataframe.columns:
dataframe[col] = dataframe[col].bfill().fillna(50 if col in ['rsi', 'mfi', 'stoch'] else 0)
# ===== BOLLINGER BANDS =====
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=bb_period, stds=bb_std)
dataframe['bb_lower'] = bollinger['lower']
dataframe['bb_middle'] = bollinger['mid']
dataframe['bb_upper'] = bollinger['upper']
dataframe['bb_percent'] = (dataframe['close'] - dataframe['bb_lower']) / (dataframe['bb_upper'] - dataframe['bb_lower'])
dataframe['bollinger_lowerband'] = bollinger['lower']
dataframe['bollinger_upperband'] = bollinger['upper']
# ===== KELTNER CHANNEL =====
keltner = emaKeltner(dataframe)
dataframe["kc_upperband"] = keltner["upper"]
dataframe["kc_middleband"] = keltner["mid"]
dataframe["kc_lowerband"] = keltner["lower"]
# ===== EMA =====
dataframe['ema_short'] = ta.EMA(dataframe, timeperiod=ema_short_period)
dataframe['ema_long'] = ta.EMA(dataframe, timeperiod=ema_long_period)
dataframe['ema9'] = ta.EMA(dataframe, timeperiod=9)
dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
# Price position vs EMAs
dataframe['price_vs_ema21'] = (dataframe['close'] - dataframe['ema21']) / dataframe['ema21'] * 100
dataframe['price_vs_ema50'] = (dataframe['close'] - dataframe['ema50']) / dataframe['ema50'] * 100
dataframe['price_vs_ema200'] = (dataframe['close'] - dataframe['ema200']) / dataframe['ema200'] * 100
# Fill NaN
ema_cols = ['ema_short', 'ema_long', 'ema9', 'ema20', 'ema21', 'ema50', 'ema200']
for col in ema_cols:
dataframe[col] = dataframe[col].bfill().fillna(dataframe['close'])
price_pos_cols = ['price_vs_ema21', 'price_vs_ema50', 'price_vs_ema200']
for col in price_pos_cols:
dataframe[col] = dataframe[col].bfill().fillna(0)
# ===== FISHER TRANSFORM (ENHANCED) =====
try:
high_low_range = dataframe['high'].rolling(window=fisher_period).max() - dataframe['low'].rolling(window=fisher_period).min()
high_low_range = high_low_range.replace(0, 0.001)
value = 2 * ((dataframe['close'] - dataframe['low'].rolling(window=fisher_period).min()) / high_low_range) - 1
value = value.clip(-0.999, 0.999)
dataframe['fisher'] = 0.5 * np.log((1 + value) / (1 - value))
dataframe['fisher_signal'] = dataframe['fisher'].shift(1)
dataframe['fisher_rising'] = dataframe['fisher'] > dataframe['fisher_signal']
dataframe['fisher_falling'] = dataframe['fisher'] < dataframe['fisher_signal']
# === NEW: Fisher regime filters ===
dataframe['fisher_adx_ok'] = (dataframe['adx'] >= fisher_min_adx) & (dataframe['adx'] <= fisher_max_adx)
dataframe['fisher_volatility_ok'] = (dataframe['volatility'] >= fisher_min_volatility) & (dataframe['volatility'] <= fisher_max_volatility)
except Exception as e:
logger.warning(f"Fisher Transform error: {e}")
dataframe['fisher'] = 0
dataframe['fisher_signal'] = 0
dataframe['fisher_rising'] = False
dataframe['fisher_falling'] = False
dataframe['fisher_adx_ok'] = True
dataframe['fisher_volatility_ok'] = True
# ===== PIVOT POINTS =====
try:
pivots = pivot_points(dataframe, window)
dataframe['pivot_lows'] = pivots['pivot_lows']
dataframe['pivot_highs'] = pivots['pivot_highs']
except Exception as e:
logger.warning(f"Pivot points error: {e}")
dataframe['pivot_lows'] = np.nan
dataframe['pivot_highs'] = np.nan
# ===== DIVERGENCE ANALYSIS (ENHANCED) =====
try:
self.initialize_divergences_lists(dataframe)
(high_iterator, low_iterator) = self.get_iterators(dataframe)
#indicators = ['rsi', 'stoch', 'roc', 'uo', 'ao', 'macd', 'cci', 'cmf', 'obv', 'mfi']
indicators = ['rsi', 'stoch', 'mfi', 'cci', 'macd', 'willr', 'fisher']
for indicator in indicators:
if indicator in dataframe.columns:
try:
# === PASS coin_params to divergence detection ===
(bearish_divs, bearish_lines, bullish_divs, bullish_lines) = self.divergence_finder_dataframe(
dataframe, indicator, high_iterator, low_iterator, coin_params)
dataframe['bearish_divergence_' + indicator + '_occurence'] = bearish_divs
dataframe['bullish_divergence_' + indicator + '_occurence'] = bullish_divs
except:
continue
# Fill NaN values
dataframe['total_bullish_divergences'] = dataframe['total_bullish_divergences'].fillna(0)
dataframe['total_bearish_divergences'] = dataframe['total_bearish_divergences'].fillna(0)
dataframe['total_bullish_divergences_count'] = dataframe['total_bullish_divergences_count'].fillna(0)
dataframe['total_bearish_divergences_count'] = dataframe['total_bearish_divergences_count'].fillna(0)
# === NEW: Divergence regime filters ===
dataframe['divergence_adx_ok'] = (dataframe['adx'] >= divergence_min_adx) & (dataframe['adx'] <= divergence_max_adx)
# Calculate ATR multiplier (current ATR / average ATR)
atr_sma = dataframe['atr'].rolling(window=20).mean()
dataframe['atr_multiplier'] = dataframe['atr'] / atr_sma
dataframe['atr_multiplier'] = dataframe['atr_multiplier'].fillna(1.0)
dataframe['divergence_atr_ok'] = (dataframe['atr_multiplier'] >= divergence_min_atr_mult) & (dataframe['atr_multiplier'] <= divergence_max_atr_mult)
except Exception as e:
logger.warning(f"Divergence analysis error: {e}")
dataframe['total_bullish_divergences'] = 0
dataframe['total_bearish_divergences'] = 0
dataframe['total_bullish_divergences_count'] = 0
dataframe['total_bearish_divergences_count'] = 0
dataframe['divergence_adx_ok'] = True
dataframe['divergence_atr_ok'] = True
# ===== MARKET STRUCTURE =====
dataframe['chop'] = choppiness_index(dataframe['high'], dataframe['low'], dataframe['close'], window=chop_period)
# Support/Resistance
dataframe['swing_high'] = dataframe['high'].rolling(window=50).max()
dataframe['swing_low'] = dataframe['low'].rolling(window=50).min()
dataframe['distance_to_resistance'] = (dataframe['swing_high'] - dataframe['close']) / dataframe['close']
dataframe['distance_to_support'] = (dataframe['close'] - dataframe['swing_low']) / dataframe['close']
# Recent highs/lows
dataframe['recent_high_5'] = dataframe['high'].rolling(window=5).max()
dataframe['recent_low_5'] = dataframe['low'].rolling(window=5).min()
dataframe['recent_high_10'] = dataframe['high'].rolling(window=10).max()
dataframe['recent_low_10'] = dataframe['low'].rolling(window=10).min()
# Price position
dataframe['high_20'] = dataframe['high'].rolling(window=20).max()
dataframe['low_20'] = dataframe['low'].rolling(window=20).min()
dataframe['price_position'] = (dataframe['close'] - dataframe['low_20']) / (dataframe['high_20'] - dataframe['low_20'])
dataframe['price_position'] = dataframe['price_position'].fillna(0.5)
# ===== PATTERN DETECTION =====
# MA alignment
dataframe['ma_alignment_bull'] = (dataframe['ema21'] > dataframe['ema50']) & (dataframe['ema50'] > dataframe['ema200'])
dataframe['ma_alignment_bear'] = (dataframe['ema21'] < dataframe['ema50']) & (dataframe['ema50'] < dataframe['ema200'])
# Rejection patterns
dataframe['rejected_at_ema21'] = (dataframe['high'].shift(1) > dataframe['ema21'].shift(1)) & (dataframe['close'].shift(1) < dataframe['ema21'].shift(1))
dataframe['rejected_at_ema50'] = (dataframe['high'].shift(1) > dataframe['ema50'].shift(1)) & (dataframe['close'].shift(1) < dataframe['ema50'].shift(1))
dataframe['bounced_from_ema21'] = (dataframe['low'].shift(1) < dataframe['ema21'].shift(1)) & (dataframe['close'].shift(1) > dataframe['ema21'].shift(1))
dataframe['bounced_from_ema50'] = (dataframe['low'].shift(1) < dataframe['ema50'].shift(1)) & (dataframe['close'].shift(1) > dataframe['ema50'].shift(1))
# Candle patterns
dataframe['red_candle'] = dataframe['close'] < dataframe['close'].shift(1)
dataframe['green_candle'] = dataframe['close'] > dataframe['close'].shift(1)
dataframe['strong_red'] = (dataframe['close'] < dataframe['close'].shift(1)) & (dataframe['volume_ratio'] > 1.2)
dataframe['strong_green'] = (dataframe['close'] > dataframe['close'].shift(1)) & (dataframe['volume_ratio'] > 1.2)
# ===== SIGNAL STRENGTH =====
try:
dataframe['signal_strength'] = self.calculate_signal_strength(dataframe)
except:
dataframe['signal_strength'] = 0
# ===== APPLY COIN-SPECIFIC FILTERS =====
dataframe['coin_min_signal'] = coin_params.get('min_signal_strength', 3)
dataframe['coin_volume_threshold'] = coin_params.get('volume_threshold', 1.2)
dataframe['coin_adx_threshold'] = coin_params.get('adx_threshold', 20)
# ===== PLOT CONFIG =====
try:
self.plot_config = PlotConfig().add_total_divergences_in_config(dataframe).config
except:
self.plot_config = None
logger.debug(f"✅ [INDICATORS] Completed {pair}")
# Daily optimization check
if not hasattr(self, 'last_daily_check'):
self.last_daily_check = 0
current_time = time.time()
if current_time - self.last_daily_check > 86400: # 24 hours
self.daily_optimization_check()
self.last_daily_check = current_time
# Daily optimization check
if not hasattr(self, 'last_daily_check'):
self.last_daily_check = 0
current_time = time.time()
if current_time - self.last_daily_check > 86400: # 24 hours
self.daily_optimization_check()
self.last_daily_check = current_time
# Generate ML predictions if model is trained
if self.ml_enabled and self.ml_model_trained:
try:
# Get ML confidence for both directions
should_enter_long, conf_long = self.ml_predict_entry(dataframe, pair, 'long')
should_enter_short, conf_short = self.ml_predict_entry(dataframe, pair, 'short')
# Store predictions (use long confidence as default)
dataframe['ml_confidence_long'] = conf_long
dataframe['ml_confidence_short'] = conf_short
dataframe['ml_prediction'] = conf_long # Default to long confidence
except Exception as e:
logger.error(f"❌ [ML] Prediction error: {str(e)}")
dataframe['ml_prediction'] = 0.5
dataframe['ml_confidence_long'] = 0.5
dataframe['ml_confidence_short'] = 0.5
else:
dataframe['ml_prediction'] = 0.5
dataframe['ml_confidence_long'] = 0.5
dataframe['ml_confidence_short'] = 0.5
return dataframe
def _add_dummy_1h_columns(self, dataframe):
"""Add dummy 1h columns when higher timeframe data is unavailable"""
# Use current 15m data to simulate 1h trend
try:
dataframe['ema50_1h_1h'] = ta.EMA(dataframe, timeperiod=200) # Use longer period on 15m
dataframe['ema200_1h_1h'] = ta.EMA(dataframe, timeperiod=800) # Use much longer period
dataframe['trend_1h_1h'] = ta.EMA(dataframe, timeperiod=84) # 21 * 4 (4x 15m = 1h)
dataframe['trend_strength_1h_1h'] = ta.ADX(dataframe)
dataframe['rsi_1h_1h'] = ta.RSI(dataframe, timeperiod=56) # Adjusted for timeframe
# Fill NaN values
columns_1h = ['ema50_1h_1h', 'ema200_1h_1h', 'trend_1h_1h', 'trend_strength_1h_1h', 'rsi_1h_1h']
for col in columns_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"Error creating dummy 1h columns: {e}")
# Absolute fallback
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:
"""
Calculate overall signal strength based on multiple factors
"""
strength = pd.Series(0, index=dataframe.index)
# Divergence strength
strength += dataframe['total_bullish_divergences_count'] * 2
strength += dataframe['total_bearish_divergences_count'] * 2
# Volume strength
volume_strength = np.where(dataframe['volume_ratio'] > 1.5, 2,
np.where(dataframe['volume_ratio'] > 1.2, 1, 0))
strength += volume_strength
# Trend alignment strength
ema_bullish = (dataframe['ema20'] > dataframe['ema50']) & (dataframe['ema50'] > dataframe['ema200'])
ema_bearish = (dataframe['ema20'] < dataframe['ema50']) & (dataframe['ema50'] < dataframe['ema200'])
strength += np.where(ema_bullish | ema_bearish, 1, 0)
# ADX strength
strength += np.where(dataframe['adx'] > 30, 1, 0)
return strength
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Enhanced Entry Strategy with Optuna-Optimized Divergence & Fisher Signals
Non-repainting: all triggers use closed candle data via df_prev
Entry Priority Order:
1. Bull/Bear_Super (3+ divergences)
2. Bull/Bear_Extreme (high-quality divergence with strict filters)
3. Bull/Bear_HQ (2-bar divergence confirmation)
4. Bull/Bear_Div (basic divergence with bands)
5. Bull/Bear_Early (single divergence signal)
6. Bull/Bear_Weak (2 divergences)
7. Bull/Bear_Fisher (Fisher Transform crossover)
"""
pair = metadata['pair']
if self.ml_enabled and not self.ml_model_trained:
logger.info(f"⏳ [ML] Waiting for model training before generating entries for {pair}")
# Still need to initialize columns
dataframe.loc[:, 'enter_long'] = 0
dataframe.loc[:, 'enter_short'] = 0
dataframe.loc[:, 'enter_tag'] = ''
dataframe.loc[:, 'entry_type'] = 0
return dataframe
logger.info(f"🎯 [ENTRY] Processing entry signals for {pair}")
# Initialize
dataframe.loc[:, 'enter_long'] = 0
dataframe.loc[:, 'enter_short'] = 0
dataframe.loc[:, 'enter_tag'] = ''
dataframe.loc[:, 'entry_type'] = 0
# Non-repainting: use closed candle
df_prev = dataframe.shift(1)
# Get optimized parameters
coin_params = self.get_coin_params(pair)
signal_lock_enabled = coin_params.get('signal_lock_enabled', False)
# ========================================================================
# BASIC FILTERS
# ========================================================================
has_volume = (df_prev['volume'] > 0)
# ========================================================================
# TREND DETECTION
# ========================================================================
in_uptrend = (
(df_prev['ema20'] > df_prev['ema50']) &
(df_prev['ema50'] > df_prev['ema200'])
)
in_downtrend = (
(df_prev['ema20'] < df_prev['ema50']) &
(df_prev['ema50'] < df_prev['ema200'])
)
# Trend blocking (prevents counter-trend trades)
block_long_in_downtrend = (
in_downtrend &
(df_prev['adx'] > coin_params.get('adx_threshold', 20))
)
block_short_in_uptrend = (
in_uptrend &
(df_prev['adx'] > coin_params.get('adx_threshold', 20))
)
# ========================================================================
# DIVERGENCE DETECTION (ENHANCED WITH REGIME FILTERS)
# ========================================================================
# Get divergence-specific parameters
divergence_require_volume_increase = coin_params.get('divergence_require_volume_increase', False)
divergence_volume_multiplier = coin_params.get('divergence_volume_multiplier', 1.5)
divergence_require_1h_alignment = coin_params.get('divergence_require_1h_alignment', False)
divergence_require_bands = coin_params.get('divergence_require_bands', False)
# === NEW: Market Regime Filters for Divergence ===
divergence_regime_ok = (
df_prev['divergence_adx_ok'] & # ADX in acceptable range
df_prev['divergence_atr_ok'] # ATR multiplier in acceptable range
)
# === NEW: Volume Confirmation (Optional) ===
if divergence_require_volume_increase:
divergence_volume_ok = (
df_prev['volume'] > df_prev['volume_sma'] * divergence_volume_multiplier
)
else:
divergence_volume_ok = pd.Series([True] * len(dataframe), index=dataframe.index)
# === NEW: 1H Alignment (Optional) ===
if divergence_require_1h_alignment:
divergence_1h_ok_long = (df_prev['trend_1h_1h'] > df_prev['ema200_1h_1h'])
divergence_1h_ok_short = (df_prev['trend_1h_1h'] < df_prev['ema200_1h_1h'])
else:
divergence_1h_ok_long = pd.Series([True] * len(dataframe), index=dataframe.index)
divergence_1h_ok_short = pd.Series([True] * len(dataframe), index=dataframe.index)
# === NEW: Bands Confirmation (Optional) ===
if divergence_require_bands:
at_lower_band = (
(df_prev['low'] <= df_prev['kc_lowerband']) |
(df_prev['close'] <= df_prev['kc_lowerband']) |
(df_prev['low'] < df_prev['bollinger_lowerband'])
)
at_upper_band = (
(df_prev['high'] >= df_prev['kc_upperband']) |
(df_prev['close'] >= df_prev['kc_upperband']) |
(df_prev['high'] > df_prev['bollinger_upperband'])
)
else:
at_lower_band = two_bands_check_long(df_prev)
at_upper_band = two_bands_check_short(df_prev)
# Base divergence signals
bullish_divergence = (df_prev['total_bullish_divergences'] > 0)
bearish_divergence = (df_prev['total_bearish_divergences'] > 0)
dataframe['bull_div_strength'] = df_prev['total_bullish_divergences_count'].fillna(0)
dataframe['bear_div_strength'] = df_prev['total_bearish_divergences_count'].fillna(0)
dataframe['bull_div_signal'] = df_prev['total_bullish_divergences']
dataframe['bear_div_signal'] = df_prev['total_bearish_divergences']
# ========================================================================
# FISHER TRANSFORM (ENHANCED WITH REGIME FILTERS)
# ========================================================================
# Get Fisher-specific parameters
fisher_oversold = coin_params.get('fisher_oversold', -2.0)
fisher_overbought = coin_params.get('fisher_overbought', 2.0)
fisher_rsi_max_long = coin_params.get('fisher_rsi_max_long', 40)
fisher_rsi_min_short = coin_params.get('fisher_rsi_min_short', 60)
fisher_require_volume = coin_params.get('fisher_require_volume', False)
fisher_volume_mult = coin_params.get('fisher_volume_mult', 1.5)
fisher_require_1h_alignment = coin_params.get('fisher_require_1h_alignment', False)
fisher_require_momentum = coin_params.get('fisher_require_momentum', False)
fisher_momentum_lookback = coin_params.get('fisher_momentum_lookback', 2)
# === NEW: Market Regime Filters for Fisher ===
fisher_regime_ok = (
df_prev['fisher_adx_ok'] & # ADX in acceptable range
df_prev['fisher_volatility_ok'] # Volatility in acceptable range
)
# === NEW: Volume Confirmation for Fisher (Optional) ===
if fisher_require_volume:
fisher_volume_ok = (
df_prev['volume'] > df_prev['volume_sma'] * fisher_volume_mult
)
else:
fisher_volume_ok = pd.Series([True] * len(dataframe), index=dataframe.index)
# === NEW: 1H Alignment for Fisher (Optional) ===
if fisher_require_1h_alignment:
fisher_1h_ok_long = (df_prev['trend_1h_1h'] > df_prev['ema200_1h_1h'])
fisher_1h_ok_short = (df_prev['trend_1h_1h'] < df_prev['ema200_1h_1h'])
else:
fisher_1h_ok_long = pd.Series([True] * len(dataframe), index=dataframe.index)
fisher_1h_ok_short = pd.Series([True] * len(dataframe), index=dataframe.index)
# === NEW: Momentum Confirmation for Fisher (Optional) ===
if fisher_require_momentum:
fisher_momentum_ok_long = (
df_prev['close'] > df_prev['close'].shift(fisher_momentum_lookback)
)
fisher_momentum_ok_short = (
df_prev['close'] < df_prev['close'].shift(fisher_momentum_lookback)
)
else:
fisher_momentum_ok_long = pd.Series([True] * len(dataframe), index=dataframe.index)
fisher_momentum_ok_short = pd.Series([True] * len(dataframe), index=dataframe.index)
# ========================================================================
# ENTRY CONDITIONS (Priority Order)
# ========================================================================
# 1. SUPER - Highest Priority (3+ divergences)
super_long = (
(dataframe['bull_div_strength'] >= 3) &
(df_prev['rsi'] < 45) &
divergence_regime_ok &
divergence_volume_ok &
~block_long_in_downtrend &
has_volume
)
super_short = (
(dataframe['bear_div_strength'] >= 3) &
(df_prev['rsi'] > 55) &
divergence_regime_ok &
divergence_volume_ok &
~block_short_in_uptrend &
has_volume
)
# 2. EXTREME - Enhanced Divergence with Strict Filters
long_condition_extreme = (
bullish_divergence &
(df_prev['rsi'] < coin_params.get('divergence_rsi_low_threshold', 30)) &
divergence_regime_ok &
divergence_volume_ok &
divergence_1h_ok_long &
at_lower_band &
~block_long_in_downtrend &
has_volume
)
short_condition_extreme = (
bearish_divergence &
(df_prev['rsi'] > coin_params.get('divergence_rsi_high_threshold', 70)) &
divergence_regime_ok &
divergence_volume_ok &
divergence_1h_ok_short &
at_upper_band &
~block_short_in_uptrend &
has_volume
)
# 3. HQ - High Quality (2-bar confirmation)
recent_bull_div_hq = (
(df_prev['total_bullish_divergences'].fillna(0) > 0) |
(df_prev['total_bullish_divergences'].shift(1).fillna(0) > 0)
)
recent_bear_div_hq = (
(df_prev['total_bearish_divergences'].fillna(0) > 0) |
(df_prev['total_bearish_divergences'].shift(1).fillna(0) > 0)
)
long_condition_hq = (
recent_bull_div_hq &
divergence_regime_ok &
divergence_volume_ok &
two_bands_check_long(df_prev) &
~block_long_in_downtrend &
has_volume
)
short_condition_hq = (
recent_bear_div_hq &
divergence_regime_ok &
divergence_volume_ok &
two_bands_check_short(df_prev) &
~block_short_in_uptrend &
has_volume
)
# 4. DIV - Basic Divergence with Bands
long_condition = (
bullish_divergence &
divergence_regime_ok &
two_bands_check_long(df_prev) &
~block_long_in_downtrend &
has_volume
)
short_condition = (
bearish_divergence &
divergence_regime_ok &
two_bands_check_short(df_prev) &
~block_short_in_uptrend &
has_volume
)
# 5. EARLY - Single Divergence Signal
early_long = (
(dataframe['bull_div_signal'].fillna(0) > 0) &
(df_prev['rsi'] < 50) &
divergence_regime_ok &
has_volume
)
early_short = (
(dataframe['bear_div_signal'].fillna(0) > 0) &
(df_prev['rsi'] > 50) &
divergence_regime_ok &
has_volume
)
# 6. WEAK - Two Divergences
weak_long = (
(dataframe['bull_div_strength'] == 2) &
(df_prev['rsi'] < 45) &
divergence_regime_ok &
has_volume
)
weak_short = (
(dataframe['bear_div_strength'] == 2) &
(df_prev['rsi'] > 55) &
divergence_regime_ok &
has_volume
)
# 7. FISHER - Lowest Priority (Enhanced with Regime Filters)
long_condition_fisher = (
# Crossover: current above, previous below
(df_prev['fisher'] > df_prev['fisher_signal']) &
(df_prev['fisher'].shift(1) <= df_prev['fisher_signal'].shift(1)) &
# Was oversold recently (check last 3 candles)
(
(df_prev['fisher'].shift(1) < fisher_oversold) |
(df_prev['fisher'].shift(2) < fisher_oversold) |
(df_prev['fisher'].shift(3) < fisher_oversold)
) &
# === NEW: Regime filters ===
fisher_regime_ok &
fisher_volume_ok &
fisher_1h_ok_long &
fisher_momentum_ok_long &
# RSI filter
(df_prev['rsi'] < fisher_rsi_max_long) &
~block_long_in_downtrend &
has_volume
)
long_condition2 = (
(
(dataframe['total_bullish_divergences'].shift(1).fillna(0) > 0) |
(dataframe['total_bullish_divergences'].shift(2).fillna(0) > 0) |
(dataframe['total_bullish_divergences'].shift(3).fillna(0) > 0) # NEW!
) &
(df_prev['rsi'] < 45) & # Was 40, now 45 (more entries)
two_bands_check_long(df_prev) &
~block_long_in_downtrend &
has_volume
)
short_condition2 = (
(
(dataframe['total_bearish_divergences'].shift(1).fillna(0) > 0) |
(dataframe['total_bearish_divergences'].shift(2).fillna(0) > 0) |
(dataframe['total_bearish_divergences'].shift(3).fillna(0) > 0) # NEW!
) &
(df_prev['rsi'] > 55) & # Was 60, now 55 (more entries)
two_bands_check_short(df_prev) &
~block_short_in_uptrend &
has_volume
)
# ========================================================================
# APPLY ENTRIES IN PRIORITY ORDER
# ========================================================================
# === DEBUG: Check why no divergence signals ===
# 1. Super
if not signal_lock_enabled or not self.is_signal_locked('Bull_Super', pair):
dataframe.loc[super_long, 'enter_long'] = 1
dataframe.loc[super_long, 'enter_tag'] = 'Bull_Super'
else:
print(f"Signal LOCKED: Bull_Super for {pair}")
if not signal_lock_enabled or not self.is_signal_locked('Bear_Super', pair):
dataframe.loc[super_short, 'enter_short'] = 1
dataframe.loc[super_short, 'enter_tag'] = 'Bear_Super'
else:
print(f"Signal LOCKED: Bear_Super for {pair}")
# 2. Extreme
if not signal_lock_enabled or not self.is_signal_locked('Bull_Extreme', pair):
long_condition_extreme_filtered = long_condition_extreme & (dataframe['enter_tag'] == "")
dataframe.loc[long_condition_extreme_filtered, 'enter_long'] = 1
dataframe.loc[long_condition_extreme_filtered, 'enter_tag'] = 'Bull_Extreme'
else:
print(f"Signal LOCKED: Bull_Extreme for {pair}")
if not signal_lock_enabled or not self.is_signal_locked('Bear_Extreme', pair):
short_condition_extreme_filtered = short_condition_extreme & (dataframe['enter_tag'] == "")
dataframe.loc[short_condition_extreme_filtered, 'enter_short'] = 1
dataframe.loc[short_condition_extreme_filtered, 'enter_tag'] = 'Bear_Extreme'
else:
print(f"Signal LOCKED: Bear_Extreme for {pair}")
# 3. HQ
if not signal_lock_enabled or not self.is_signal_locked('Bull_HQ', pair):
long_condition_hq_filtered = long_condition_hq & (dataframe['enter_tag'] == "")
dataframe.loc[long_condition_hq_filtered, 'enter_long'] = 1
dataframe.loc[long_condition_hq_filtered, 'enter_tag'] = 'Bull_HQ'
else:
print(f"Signal LOCKED: Bull_HQ for {pair}")
if not signal_lock_enabled or not self.is_signal_locked('Bear_HQ', pair):
short_condition_hq_filtered = short_condition_hq & (dataframe['enter_tag'] == "")
dataframe.loc[short_condition_hq_filtered, 'enter_short'] = 1
dataframe.loc[short_condition_hq_filtered, 'enter_tag'] = 'Bear_HQ'
else:
print(f"Signal LOCKED: Bear_HQ for {pair}")
# 4. Div
if not signal_lock_enabled or not self.is_signal_locked('Bull_Div', pair):
long_condition_filtered = long_condition & (dataframe['enter_tag'] == "")
dataframe.loc[long_condition_filtered, 'enter_long'] = 1
dataframe.loc[long_condition_filtered, 'enter_tag'] = 'Bull_Div'
else:
print(f"Signal LOCKED: Bull_Div for {pair}")
if not signal_lock_enabled or not self.is_signal_locked('Bear_Div', pair):
short_condition_filtered = short_condition & (dataframe['enter_tag'] == "")
dataframe.loc[short_condition_filtered, 'enter_short'] = 1
dataframe.loc[short_condition_filtered, 'enter_tag'] = 'Bear_Div'
else:
print(f"Signal LOCKED: Bear_Div for {pair}")
# 5. Early
if not signal_lock_enabled or not self.is_signal_locked('Bull_Early', pair):
early_long_filtered = early_long & (dataframe['enter_tag'] == "")
dataframe.loc[early_long_filtered, 'enter_long'] = 1
dataframe.loc[early_long_filtered, 'enter_tag'] = 'Bull_Early'
else:
print(f"Signal LOCKED: Bull_Early for {pair}")
if not signal_lock_enabled or not self.is_signal_locked('Bear_Early', pair):
early_short_filtered = early_short & (dataframe['enter_tag'] == "")
dataframe.loc[early_short_filtered, 'enter_short'] = 1
dataframe.loc[early_short_filtered, 'enter_tag'] = 'Bear_Early'
else:
print(f"Signal LOCKED: Bear_Early for {pair}")
# 6. Weak
if not signal_lock_enabled or not self.is_signal_locked('Bull_Weak', pair):
weak_long_filtered = weak_long & (dataframe['enter_tag'] == "")
dataframe.loc[weak_long_filtered, 'enter_long'] = 1
dataframe.loc[weak_long_filtered, 'enter_tag'] = 'Bull_Weak'
else:
print(f"Signal LOCKED: Bull_Weak for {pair}")
if not signal_lock_enabled or not self.is_signal_locked('Bull_Confirmed', pair):
confirmed_long_filtered = long_condition2 & (dataframe['enter_tag'] == "")
dataframe.loc[confirmed_long_filtered, 'enter_long'] = 1
dataframe.loc[confirmed_long_filtered, 'enter_tag'] = 'Bull_Confirmed'
else:
print(f"Signal LOCKED: Bull_Confirmed for {pair}")
if not signal_lock_enabled or not self.is_signal_locked('Bear_Confirmed', pair):
confirmed_short_filtered = short_condition2 & (dataframe['enter_tag'] == "")
dataframe.loc[confirmed_short_filtered, 'enter_long'] = 1
dataframe.loc[confirmed_short_filtered, 'enter_tag'] = 'Bear_Confirmed'
else:
print(f"Signal LOCKED: Bull_Confirmed for {pair}")
if not signal_lock_enabled or not self.is_signal_locked('Bear_Weak', pair):
weak_short_filtered = weak_short & (dataframe['enter_tag'] == "")
dataframe.loc[weak_short_filtered, 'enter_short'] = 1
dataframe.loc[weak_short_filtered, 'enter_tag'] = 'Bear_Weak'
else:
print(f"Signal LOCKED: Bear_Weak for {pair}")
# 7. Fisher
if not signal_lock_enabled or not self.is_signal_locked('Bull_Fisher', pair):
fisher_long_filtered = long_condition_fisher & (dataframe['enter_tag'] == "")
dataframe.loc[fisher_long_filtered, 'enter_long'] = 1
dataframe.loc[fisher_long_filtered, 'enter_tag'] = 'Bull_Fisher'
else:
print(f"Signal LOCKED: Bull_Fisher for {pair}")
# ========================================================================
# LOGGING
# ========================================================================
if len(dataframe) > 0:
last_row = dataframe.iloc[-1]
if last_row.get('enter_long', 0) == 1 or last_row.get('enter_short', 0) == 1:
direction = "LONG 🟢" if last_row.get('enter_long', 0) == 1 else "SHORT 🔴"
logger.info(f"🚀 {pair} ENTRY DETECTED - {direction}")
logger.info(f" Tag: {last_row.get('enter_tag', 'N/A')}")
logger.info(f" RSI: {last_row.get('rsi', 0):.1f} | ADX: {last_row.get('adx', 0):.1f}")
logger.info(f" Volume Ratio: {last_row.get('volume_ratio', 1):.2f}")
logger.info(f" BullDivs: {last_row.get('bull_div_strength', 0)} | BearDivs: {last_row.get('bear_div_strength', 0)}")
ema20, ema50, ema200 = last_row.get('ema20', 0), last_row.get('ema50', 0), last_row.get('ema200', 0)
if ema20 > ema50 > ema200:
trend_icon = "⬆️ UPTREND"
elif ema20 < ema50 < ema200:
trend_icon = "⬇️ DOWNTREND"
else:
trend_icon = "↔️ SIDEWAYS"
logger.info(f" Market: {trend_icon} | EMA20: {ema20:.2f} | EMA50: {ema50:.2f}")
# ========================================================================
# HYBRID ML PREDICTIONS - Add ML-based entries
# ========================================================================
# ========================================================================
# HYBRID ML PREDICTIONS - Add ML-based entries
# ========================================================================
if self.ml_enabled and ML_AVAILABLE and self.ml_trained:
try:
# Check last candle only (real-time prediction)
if len(dataframe) > 0:
ml_should_enter_long, ml_confidence_long = self.ml_predict_entry(dataframe, pair, 'long')
ml_should_enter_short, ml_confidence_short = self.ml_predict_entry(dataframe, pair, 'short')
# ML predicts LONG entry
if ml_should_enter_long and dataframe['enter_tag'].iloc[-1] == '':
# Determine which signal would have triggered
last_row = dataframe.iloc[-1]
base_signal = "Unknown"
# Check each signal condition to find what ML is predicting on
if last_row.get('total_bullish_divergences', 0) >= 2:
base_signal = "Bull_Super"
elif last_row.get('total_bullish_divergences', 0) == 1 and last_row.get('rsi', 50) < 35:
base_signal = "Bull_Extreme"
elif last_row.get('total_bullish_divergences', 0) == 1:
base_signal = "Bull_HQ"
elif last_row.get('bull_div_strength', 0) > 0:
base_signal = "Bull_Div"
elif last_row.get('rsi', 50) < 25:
base_signal = "Bull_Early"
elif last_row.get('rsi', 50) < 30:
base_signal = "Bull_Weak"
# SKIP ML ENTRY IF NO KNOWN SIGNAL MATCHED
if base_signal == "Unknown":
logger.debug(f"⚠️ [ML] Skipping unknown pattern for {pair} - no matching signal")
else:
dataframe.loc[dataframe.index[-1], 'enter_long'] = 1
dataframe.loc[dataframe.index[-1], 'enter_tag'] = f'ML_{base_signal}_{ml_confidence_long:.0%}'
logger.info(f"🤖 [ML ENTRY] {pair} - LONG predicted")
logger.info(f" Signal: {base_signal} | Confidence: {ml_confidence_long:.1%}")
logger.info(f" RSI: {dataframe['rsi'].iloc[-1]:.1f} | ADX: {dataframe['adx'].iloc[-1]:.1f}")
# ML predicts SHORT entry
if ml_should_enter_short and dataframe['enter_tag'].iloc[-1] == '':
# Determine which signal would have triggered
last_row = dataframe.iloc[-1]
base_signal = "Unknown"
# Check each signal condition to find what ML is predicting on
if last_row.get('total_bearish_divergences', 0) >= 2:
base_signal = "Bear_Super"
elif last_row.get('total_bearish_divergences', 0) == 1 and last_row.get('rsi', 50) > 65:
base_signal = "Bear_Extreme"
elif last_row.get('total_bearish_divergences', 0) == 1:
base_signal = "Bear_HQ"
elif last_row.get('bear_div_strength', 0) > 0:
base_signal = "Bear_Div"
elif last_row.get('rsi', 50) > 75:
base_signal = "Bear_Early"
elif last_row.get('rsi', 50) > 70:
base_signal = "Bear_Weak"
# SKIP ML ENTRY IF NO KNOWN SIGNAL MATCHED
if base_signal == "Unknown":
logger.debug(f"⚠️ [ML] Skipping unknown pattern for {pair} - no matching signal")
else:
dataframe.loc[dataframe.index[-1], 'enter_short'] = 1
dataframe.loc[dataframe.index[-1], 'enter_tag'] = f'ML_{base_signal}_{ml_confidence_short:.0%}'
logger.info(f"🤖 [ML ENTRY] {pair} - SHORT predicted")
logger.info(f" Signal: {base_signal} | Confidence: {ml_confidence_short:.1%}")
logger.info(f" RSI: {dataframe['rsi'].iloc[-1]:.1f} | ADX: {dataframe['adx'].iloc[-1]:.1f}")
except Exception as e:
logger.error(f"❌ [ML] Entry prediction error for {pair}: {e}")
# ========================================================================
# FINAL RETURN
# ========================================================================
return dataframe
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:
"""
Adaptive leverage based on signal strength
"""
try:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
if len(dataframe) > 0:
current_signal_strength = dataframe['signal_strength'].iloc[-1]
# Calculate leverage based on signal strength
if current_signal_strength >= 8:
calculated_leverage = self.leverage_value # Full leverage for strong signals
elif current_signal_strength >= 6:
calculated_leverage = self.leverage_value * 0.8 # 80% leverage
elif current_signal_strength >= 4:
calculated_leverage = self.leverage_value * 0.6 # 60% leverage
else:
calculated_leverage = self.leverage_value * 0.4 # 40% leverage for weak signals
# Round to 1 decimal place (or use 0 for whole numbers)
return round(calculated_leverage, 1)
except:
pass
# Conservative fallback - also rounded
return round(self.leverage_value * 0.5, 1)
def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, after_fill: bool, **kwargs) -> float:
"""
FIXED DYNAMIC STOPLOSS - Now properly widens in volatile markets
Features:
- ATR-based dynamic stops adapted to volatility
- Signal-type specific stop distances
- Breakeven protection at realistic levels
- Trailing stops that adapt to signal quality
- Time-based stop tightening for stagnant trades
Entry Tags:
- Bull_Super / Bear_Super (highest quality) → Widest stops
- Bull_Extreme / Bear_Extreme → Wide stops
- Bull_HQ / Bear_HQ → Medium stops
- Bull_Div / Bear_Div → Medium stops
- Bull_Early / Bear_Early → Tighter stops
- Bull_Weak / Bear_Weak (lowest quality) → Tightest stops
- Bull_Fisher / Bear_Fisher → Medium stops
"""
try:
# Get market data
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
if len(dataframe) < 1:
return self.stoploss
last_row = dataframe.iloc[-1]
atr = last_row.get('atr', 0)
current_price = last_row['close']
volatility = last_row.get('volatility', 0.02)
adx = last_row.get('adx', 20)
# Get entry tag
entry_tag = trade.enter_tag or ""
trade_duration_minutes = (current_time - trade.open_date_utc).total_seconds() / 60
# =======================================================================
# STEP 1: BREAKEVEN LOGIC (Signal-Type Specific)
# =======================================================================
# Determine breakeven trigger based on signal quality
if 'Super' in entry_tag:
breakeven_trigger = 0.020 # 2% for Super signals
elif 'Extreme' in entry_tag or 'HQ' in entry_tag:
breakeven_trigger = 0.015 # 1.5% for Extreme/HQ
elif 'Div' in entry_tag:
breakeven_trigger = 0.012 # 1.2% for divergence
elif 'Fisher' in entry_tag:
breakeven_trigger = 0.015 # 1.5% for Fisher
elif 'Early' in entry_tag:
breakeven_trigger = 0.010 # 1% for Early
elif 'Weak' in entry_tag:
breakeven_trigger = 0.008 # 0.8% for Weak
else:
breakeven_trigger = 0.015 # Default 1.5%
# Move to breakeven once threshold hit
if current_profit >= breakeven_trigger:
breakeven_stop = 0.003 # 0.3% positive (covers fees)
return stoploss_from_open(breakeven_stop, current_profit, is_short=trade.is_short)
# =======================================================================
# STEP 2: TRAILING LOGIC (Starts at 3.5% profit)
# =======================================================================
trailing_trigger = 0.035 # Start trailing at 3.5% profit
if current_profit >= trailing_trigger:
# Base trailing distance by signal quality
if 'Super' in entry_tag:
trailing_distance = 0.025 # 2.5% - wide trail
elif 'Extreme' in entry_tag or 'HQ' in entry_tag:
trailing_distance = 0.020 # 2.0% - normal trail
elif 'Div' in entry_tag or 'Fisher' in entry_tag:
trailing_distance = 0.015 # 1.5% - tighter trail
elif 'Early' in entry_tag or 'Weak' in entry_tag:
trailing_distance = 0.012 # 1.2% - very tight trail
else:
trailing_distance = 0.018 # Default 1.8%
# Adjust for volatility
volatility_adj = min(1.5, max(0.7, volatility / 0.02))
trailing_distance *= volatility_adj
# Adjust for ADX (trend strength)
if adx > 40:
trailing_distance *= 1.2 # Strong trend - wider
elif adx < 20:
trailing_distance *= 0.85 # Weak trend - tighter
return stoploss_from_open(trailing_distance, current_profit, is_short=trade.is_short)
# =======================================================================
# STEP 3: INITIAL DYNAMIC STOPLOSS (ATR-Based) 🔧 FIXED!
# =======================================================================
# Base ATR multiplier by signal quality
if 'Super' in entry_tag:
atr_multiplier = 3.5 # Widest stop (was 3.0)
elif 'Extreme' in entry_tag or 'HQ' in entry_tag:
atr_multiplier = 3.0 # Wide (was 2.5)
elif 'Div' in entry_tag or 'Fisher' in entry_tag:
atr_multiplier = 2.5 # Medium (was 2.0)
elif 'Early' in entry_tag:
atr_multiplier = 2.0 # Tighter (was 1.8)
elif 'Weak' in entry_tag:
atr_multiplier = 1.5 # Tightest
else:
atr_multiplier = 2.5 # Default (was 2.0)
# Adjust for volatility (wider in volatile markets)
volatility_adjustment = min(1.5, max(0.8, volatility / 0.02)) # Changed min from 0.7 to 0.8
atr_multiplier *= volatility_adjustment
# Calculate stop distance
if atr > 0:
stop_distance = (atr * atr_multiplier) / current_price
else:
# Fallback if ATR not available
stop_distance = 0.06 + (volatility * 2)
# Signal-specific fine-tuning
if 'Weak' in entry_tag or 'Early' in entry_tag:
stop_distance *= 0.85 # Tighter for lower quality
elif 'Super' in entry_tag or 'Extreme' in entry_tag:
stop_distance *= 1.15 # Wider for highest quality
# Safety limits (adjusted for better range)
min_stop = 0.035 # 3.5% minimum
max_stop = 0.15 # 15% maximum (was 0.12) - Allow wider stops
stop_distance = max(min_stop, min(max_stop, stop_distance))
dynamic_stoploss = -stop_distance
# =======================================================================
# STEP 4: TIME-BASED TIGHTENING (For Stagnant Trades)
# =======================================================================
# Tighten stop progressively for trades that aren't moving
if trade_duration_minutes > 90: # After 1.5 hours
if current_profit < 0.005: # Less than 0.5% profit
# Tighten by 10% every 30 minutes
time_factor = min(0.3, (trade_duration_minutes - 90) / 180)
dynamic_stoploss *= (1 - time_factor)
# 🔧 CRITICAL FIX: Only enforce minimum stop, don't prevent widening!
# Never tighter than 3.5%, but ALLOW wider than -7% in volatile markets
if dynamic_stoploss > -0.035: # If calculated stop is tighter than 3.5%
dynamic_stoploss = -0.035 # Enforce minimum 3.5%
# =======================================================================
# STEP 5: LOGGING (Every 15 checks for better visibility)
# =======================================================================
if not hasattr(self, '_stoploss_log_counter'):
self._stoploss_log_counter = {}
if pair not in self._stoploss_log_counter:
self._stoploss_log_counter[pair] = 0
self._stoploss_log_counter[pair] += 1
if self._stoploss_log_counter[pair] % 15 == 0: # More frequent logging (was 20)
if current_profit >= trailing_trigger:
logger.info(f"🔄 Trailing {pair}: Profit={current_profit:.3f}, Stop={dynamic_stoploss:.3f}, Tag={entry_tag}")
elif current_profit >= breakeven_trigger:
logger.info(f"⚖️ Breakeven {pair}: Profit={current_profit:.3f}, Tag={entry_tag}")
else:
logger.info(f"🛡️ Dynamic SL {pair}: ATR={atr:.4f}, Mult={atr_multiplier:.2f}x, Stop={dynamic_stoploss:.3f}, Tag={entry_tag}")
return dynamic_stoploss
except Exception as e:
logger.error(f"❌ Error in custom_stoploss for {pair}: {e}")
return self.stoploss
def initialize_divergences_lists(self, dataframe: DataFrame):
"""Initialize divergence tracking columns"""
# Bullish Divergences
dataframe["total_bullish_divergences"] = np.nan
dataframe["total_bullish_divergences_count"] = 0
dataframe["total_bullish_divergences_names"] = ''
# Bearish Divergences
dataframe["total_bearish_divergences"] = np.nan
dataframe["total_bearish_divergences_count"] = 0
dataframe["total_bearish_divergences_names"] = ''
def get_iterators(self, dataframe):
"""Get pivot point iterators for divergence detection"""
low_iterator = []
high_iterator = []
for index, row in enumerate(dataframe.itertuples(index=True, name='Pandas')):
if np.isnan(row.pivot_lows):
low_iterator.append(0 if len(low_iterator) == 0 else low_iterator[-1])
else:
low_iterator.append(index)
if np.isnan(row.pivot_highs):
high_iterator.append(0 if len(high_iterator) == 0 else high_iterator[-1])
else:
high_iterator.append(index)
return high_iterator, low_iterator
def add_divergences(self, dataframe: DataFrame, indicator: str, high_iterator, low_iterator):
"""Add divergence detection for a specific indicator"""
(bearish_divergences, bearish_lines, bullish_divergences, bullish_lines) = self.divergence_finder_dataframe(
dataframe, indicator, high_iterator, low_iterator)
dataframe['bearish_divergence_' + indicator + '_occurence'] = bearish_divergences
dataframe['bullish_divergence_' + indicator + '_occurence'] = bullish_divergences
def divergence_finder_dataframe(self, dataframe: DataFrame, indicator_source: str, high_iterator, low_iterator, coin_params: dict = None) -> Tuple[pd.Series, pd.Series]:
"""Enhanced divergence finder with Optuna-optimized parameters"""
# Get optimized parameters (pass from populate_indicators)
if coin_params is None:
coin_params = {}
# === NEW: Use optimized thresholds ===
min_price_delta_pct = coin_params.get('divergence_min_price_delta', 1.0) / 100 # Convert to decimal
min_rsi_delta = coin_params.get('divergence_min_rsi_delta', 4.0)
min_time_separation = coin_params.get('divergence_lookback_period', 5)
bearish_lines = [np.empty(len(dataframe['close'])) * np.nan]
bearish_divergences = np.empty(len(dataframe['close'])) * np.nan
bullish_lines = [np.empty(len(dataframe['close'])) * np.nan]
bullish_divergences = np.empty(len(dataframe['close'])) * np.nan
for index, row in enumerate(dataframe.itertuples(index=True, name='Pandas')):
# Bearish divergence detection
bearish_occurence = self.bearish_divergence_finder(
dataframe, dataframe[indicator_source], high_iterator, index, coin_params)
if bearish_occurence is not None:
(prev_pivot, current_pivot) = bearish_occurence
bearish_prev_pivot = dataframe['close'][prev_pivot]
bearish_current_pivot = dataframe['close'][current_pivot]
bearish_ind_prev_pivot = dataframe[indicator_source][prev_pivot]
bearish_ind_current_pivot = dataframe[indicator_source][current_pivot]
# === ENHANCED: Use Optuna-optimized validation ===
price_diff_pct = abs(bearish_current_pivot - bearish_prev_pivot) / bearish_prev_pivot
indicator_diff = abs(bearish_ind_current_pivot - bearish_ind_prev_pivot)
time_diff = current_pivot - prev_pivot
# Validate with optimized parameters
if (price_diff_pct >= min_price_delta_pct and
indicator_diff >= min_rsi_delta and
time_diff >= min_time_separation):
bearish_divergences[index] = row.close
dataframe.loc[index, "total_bearish_divergences"] = row.close
dataframe.loc[index, "total_bearish_divergences_count"] += 1
dataframe.loc[index, "total_bearish_divergences_names"] += indicator_source.upper() + '
'
# Bullish divergence detection
bullish_occurence = self.bullish_divergence_finder(
dataframe, dataframe[indicator_source], low_iterator, index, coin_params)
if bullish_occurence is not None:
(prev_pivot, current_pivot) = bullish_occurence
bullish_prev_pivot = dataframe['close'][prev_pivot]
bullish_current_pivot = dataframe['close'][current_pivot]
bullish_ind_prev_pivot = dataframe[indicator_source][prev_pivot]
bullish_ind_current_pivot = dataframe[indicator_source][current_pivot]
# === ENHANCED: Use Optuna-optimized validation ===
price_diff_pct = abs(bullish_current_pivot - bullish_prev_pivot) / bullish_prev_pivot
indicator_diff = abs(bullish_ind_current_pivot - bullish_ind_prev_pivot)
time_diff = current_pivot - prev_pivot
# Validate with optimized parameters
if (price_diff_pct >= min_price_delta_pct and
indicator_diff >= min_rsi_delta and
time_diff >= min_time_separation):
bullish_divergences[index] = row.close
dataframe.loc[index, "total_bullish_divergences"] = row.close
dataframe.loc[index, "total_bullish_divergences_count"] += 1
dataframe.loc[index, "total_bullish_divergences_names"] += indicator_source.upper() + '
'
return (bearish_divergences, bearish_lines, bullish_divergences, bullish_lines)
def bullish_divergence_finder(self, dataframe, indicator, low_iterator, index, coin_params: dict = None):
"""Enhanced bullish divergence detection - ONLY CLASSIC DIVERGENCE with Optuna parameters"""
# Get optimized window parameter
if coin_params is None:
window = getattr(self, 'window', type('obj', (), {'value': 4})).value
else:
window = coin_params.get('window', 4)
try:
if low_iterator[index] == index:
current_pivot = low_iterator[index]
occurences = list(dict.fromkeys(low_iterator))
current_index = occurences.index(low_iterator[index])
for i in range(current_index-1, current_index - window - 1, -1):
if i < 0 or i >= len(occurences):
continue
prev_pivot = occurences[i]
if np.isnan(prev_pivot):
continue
# Classic bullish divergence
price_lower = dataframe['pivot_lows'][current_pivot] < dataframe['pivot_lows'][prev_pivot]
indicator_higher = indicator[current_pivot] > indicator[prev_pivot]
if price_lower and indicator_higher:
# Additional validation: check trend consistency
if self.validate_divergence_trend(dataframe, prev_pivot, current_pivot, 'bullish', coin_params):
return (prev_pivot, current_pivot)
except:
pass
return None
def bearish_divergence_finder(self, dataframe, indicator, high_iterator, index, coin_params: dict = None):
"""Enhanced bearish divergence detection - ONLY CLASSIC DIVERGENCES with Optuna parameters"""
# Get optimized window parameter
if coin_params is None:
window = getattr(self, 'window', type('obj', (), {'value': 4})).value
else:
window = coin_params.get('window', 4)
try:
if high_iterator[index] == index:
current_pivot = high_iterator[index]
occurences = list(dict.fromkeys(high_iterator))
current_index = occurences.index(high_iterator[index])
for i in range(current_index-1, current_index - window - 1, -1):
if i < 0 or i >= len(occurences):
continue
prev_pivot = occurences[i]
if np.isnan(prev_pivot):
continue
# Classic bearish divergence
price_higher = dataframe['pivot_highs'][current_pivot] > dataframe['pivot_highs'][prev_pivot]
indicator_lower = indicator[current_pivot] < indicator[prev_pivot]
if price_higher and indicator_lower:
# Additional validation: check trend consistency
if self.validate_divergence_trend(dataframe, prev_pivot, current_pivot, 'bearish', coin_params):
return (prev_pivot, current_pivot)
except:
pass
return None
def validate_divergence_trend(self, dataframe, prev_pivot, current_pivot, divergence_type, coin_params: dict = None):
"""Validate divergence by checking intermediate trend with optional Optuna filter"""
# === NEW: Optional trend context requirement ===
if coin_params and not coin_params.get('divergence_require_trend_context', True):
return True # Skip trend validation if not required
try:
# Check if there's a clear trend between pivots
mid_point = (prev_pivot + current_pivot) // 2
if divergence_type == 'bearish':
# For bearish divergence, expect uptrend in between
return dataframe['ema20'][mid_point] > dataframe['ema20'][prev_pivot]
else:
# For bullish divergence, expect downtrend in between
return dataframe['ema20'][mid_point] < dataframe['ema20'][prev_pivot]
except:
return True # Default to accepting divergence if validation fails
@property
def protections(self):
"""Enhanced protection configuration"""
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
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Initialize
dataframe['exit_long'] = 0
dataframe['exit_short'] = 0
dataframe['exit_tag'] = ''
# === YOUR OTHER EXIT CONDITIONS FIRST ===
# (Add any other exit conditions you have here)
# any_long_exit = (some_other_condition)
# any_short_exit = (some_other_condition)
# === EXIT ON OPPOSITE SIGNALS (your previous approach) ===
if 'enter_long' in dataframe.columns and 'enter_short' in dataframe.columns:
# Exit longs on short signals
reversal_long_exit = (dataframe['enter_short'] == 1)
dataframe.loc[reversal_long_exit, 'exit_long'] = 1
dataframe.loc[reversal_long_exit, 'exit_tag'] = 'Reversal_Short_Signal'
# Exit shorts on long signals
if self.can_short:
reversal_short_exit = (dataframe['enter_long'] == 1)
dataframe.loc[reversal_short_exit, 'exit_short'] = 1
dataframe.loc[reversal_short_exit, 'exit_tag'] = 'Reversal_Long_Signal'
return dataframe
def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime',
current_rate: float, current_profit: float, **kwargs):
"""
ENHANCED EXIT WITH 5 TIERS:
- Under 1.0%: Minimal interference
- 1.0%-1.5%: Light protection (0.8% pullback)
- 1.5%-3%: Medium protection (1.2% pullback)
- 3%-5%: Strong protection (1.0% pullback)
- 5%+: Tight protection (1.2% pullback)
"""
from logging import getLogger
from datetime import timezone
logger = getLogger(__name__)
# Get actual profit
actual_profit = trade.calc_profit_ratio(current_rate)
current_profit = actual_profit
# Basic info
entry_tag = getattr(trade, 'enter_tag', 'Unknown')
trade_duration_minutes = (current_time - trade.open_date_utc).total_seconds() / 60
# Profit peak tracking
if not hasattr(self, 'profit_peaks'):
self.profit_peaks = {}
trade_id = str(trade.id)
if trade_id not in self.profit_peaks:
self.profit_peaks[trade_id] = current_profit
else:
self.profit_peaks[trade_id] = max(self.profit_peaks[trade_id], current_profit)
profit_peak = self.profit_peaks[trade_id]
# Status logging (every check when profitable)
if current_profit >= 0.01:
logger.info(f"📊 {pair} Status Check:")
logger.info(f" 💰 Current: {current_profit*100:.2f}% | Peak: {profit_peak*100:.2f}%")
logger.info(f" ⚖️ Leverage: {trade.leverage}x | Duration: {int(trade_duration_minutes)}min")
logger.info(f" 🏷️ Entry: {entry_tag}")
# Get market data
rsi = 50
prev_rsi = 50
atr = 0
momentum_score = 0
has_opposite_div = False
last_row = None
adx = 20
ema20 = 0
ema50 = 0
close_price = current_rate
try:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
if dataframe is not None and not dataframe.empty:
last_rows = dataframe.tail(5)
last_row = last_rows.iloc[-1]
prev_row = last_rows.iloc[-2] if len(last_rows) > 1 else last_row
rsi = last_row.get('rsi', 50)
prev_rsi = prev_row.get('rsi', 50)
atr = last_row.get('atr', 0)
adx = last_row.get('adx', 20)
ema20 = last_row.get('ema20', last_row.get('close', current_rate))
ema50 = last_row.get('ema50', last_row.get('close', current_rate))
close_price = last_row.get('close', current_rate)
# Divergence check
if not trade.is_short:
has_opposite_div = last_rows['total_bearish_divergences'].fillna(0).gt(0).any()
else:
has_opposite_div = last_rows['total_bullish_divergences'].fillna(0).gt(0).any()
# Momentum
if len(dataframe) >= 5:
momentum_score = (close_price - dataframe.iloc[-5]['close']) / dataframe.iloc[-5]['close']
if current_profit >= 0.01:
logger.info(f" 📊 RSI: {rsi:.1f} | Momentum: {momentum_score:.3f} | ADX: {adx:.1f}")
logger.info(f" 📈 EMA20: {ema20:.2f} | EMA50: {ema50:.2f}")
logger.info(f" 🔄 Opposite Div: {'⚠️ YES' if has_opposite_div else 'No'}")
except Exception as e:
logger.debug(f"[{pair}] custom_exit data fetch error: {e}")
# Logging helper
def log_and_exit(reason: str):
logger.info(f"🚪 {pair} EXIT [{reason}]")
logger.info(f" 💰 Profit: {current_profit * 100:.2f}%")
logger.info(f" 🕐 Duration: {int(trade_duration_minutes)} min")
logger.info(f" 📊 RSI: {rsi:.1f} | ADX: {adx:.1f}")
logger.info(f" 🏷️ Tag: {entry_tag}")
logger.info(f" ⚖️ Leverage: {trade.leverage}x")
logger.info(f" 📈 Peak: {profit_peak * 100:.2f}%")
if trade_id in self.profit_peaks:
del self.profit_peaks[trade_id]
return reason
# =======================================================================
# 2️⃣ EXTREME TSUNAMI PROTECTION
# =======================================================================
if last_row is not None and current_profit < -0.02:
try:
if not trade.is_short:
if adx > 60 and close_price < ema50 * 0.97:
if trade_duration_minutes >= 120:
return log_and_exit(f"Extreme_Tsunami_{current_profit*100:.1f}%")
else:
if adx > 60 and close_price > ema50 * 1.03:
if trade_duration_minutes >= 120:
return log_and_exit(f"Extreme_Tsunami_Short_{current_profit*100:.1f}%")
except Exception as e:
logger.debug(f"[{pair}] Tsunami check error: {e}")
# =======================================================================
# 3️⃣ UNDER 1.0% ZONE - MINIMAL INTERFERENCE
# =======================================================================
if current_profit < 0.010:
# Only extreme crashes
if not trade.is_short and momentum_score < -0.045:
if current_profit >= 0.005:
return log_and_exit(f"Extreme_Crash_{current_profit*100:.1f}%")
elif trade.is_short and momentum_score > 0.045:
if current_profit >= 0.005:
return log_and_exit(f"Extreme_Crash_Short_{current_profit*100:.1f}%")
# Very long timeout - 12 hours
if trade_duration_minutes >= 1440:
if current_profit < 0.000:
return log_and_exit(f"Extended_Timeout_12HR_{current_profit*100:.1f}%")
# Hold - let it develop
return None
# =======================================================================
# 4️⃣ NEW: 1.0% - 1.5% ZONE - LIGHT PROTECTION
# =======================================================================
if current_profit >= 0.010 and current_profit < 0.015:
very_light_trail_distance = 0.008 # 0.8% pullback
very_light_trail_stop = profit_peak - very_light_trail_distance
logger.info(f" 🎯 VERY LIGHT Trail: Peak {profit_peak*100:.1f}% | Stop at {very_light_trail_stop*100:.2f}%")
logger.info(f" 📉 Pullback: {(profit_peak - current_profit)*100:.2f}% (exit if >= 0.8%)")
# Only exit if peak was meaningful (1.2%+) and we've pulled back
if current_profit < very_light_trail_stop and profit_peak >= 0.012:
return log_and_exit(f"VeryLight_Trail_{profit_peak*100:.1f}%_to_{current_profit*100:.1f}%")
return None
# =======================================================================
# 5️⃣ TIER 1: LIGHT TRAILING (1.5% - 3%)
# =======================================================================
if current_profit >= 0.015 and current_profit < 0.03:
light_trail_distance = 0.012 # 1.2% pullback
light_trail_stop = profit_peak - light_trail_distance
logger.info(f" 🎯 LIGHT Trail: Peak {profit_peak*100:.1f}% | Stop at {light_trail_stop*100:.2f}%")
logger.info(f" 📉 Pullback: {(profit_peak - current_profit)*100:.2f}% (exit if >= 1.2%)")
if current_profit < light_trail_stop:
return log_and_exit(f"Light_Trail_{profit_peak*100:.1f}%_to_{current_profit*100:.1f}%")
# =======================================================================
# 6️⃣ TIER 2: MEDIUM TRAILING (3% - 5%)
# =======================================================================
if current_profit >= 0.03 and current_profit < 0.05:
medium_trail_distance = 0.010 # 1.0% pullback
medium_trail_stop = profit_peak - medium_trail_distance
logger.info(f" 🎯 MEDIUM Trail: Peak {profit_peak*100:.1f}% | Stop at {medium_trail_stop*100:.2f}%")
logger.info(f" 📉 Pullback: {(profit_peak - current_profit)*100:.2f}% (exit if >= 1.0%)")
if current_profit < medium_trail_stop:
return log_and_exit(f"Medium_Trail_{profit_peak*100:.1f}%_to_{current_profit*100:.1f}%")
# =======================================================================
# 7️⃣ TIER 3: TIGHT TRAILING (5%+ profit)
# =======================================================================
if current_profit >= 0.05:
# Adaptive trailing based on peak
if profit_peak >= 0.10:
trail_distance = 0.020 # 2.0% at 10%+
elif profit_peak >= 0.08:
trail_distance = 0.015 # 1.5% at 8%+
else:
trail_distance = 0.012 # 1.2% at 5-8%
trailing_stop = profit_peak - trail_distance
pullback_from_peak = profit_peak - current_profit
logger.info(f" 🎯 TIGHT Trail: Distance {trail_distance*100:.1f}% | Stop at {trailing_stop*100:.2f}%")
logger.info(f" 📉 Pullback: {pullback_from_peak*100:.2f}% (exit if >= {trail_distance*100:.1f}%)")
if current_profit < trailing_stop:
pullback_pct = pullback_from_peak * 100
return log_and_exit(f"TRAIL_from_{profit_peak*100:.1f}%_pulled_{pullback_pct:.1f}%")
# =======================================================================
# 8️⃣ RSI EXTREME EXITS
# =======================================================================
if not trade.is_short:
if rsi >= 85 and current_profit >= 0.05:
return log_and_exit(f"RSI_85_{current_profit*100:.1f}%")
if rsi >= 80 and current_profit >= 0.08:
return log_and_exit(f"RSI_80_{current_profit*100:.1f}%")
else:
if rsi <= 15 and current_profit >= 0.05:
return log_and_exit(f"RSI_15_SHORT_{current_profit*100:.1f}%")
if rsi <= 20 and current_profit >= 0.08:
return log_and_exit(f"RSI_20_SHORT_{current_profit*100:.1f}%")
# =======================================================================
# 9️⃣ STAGNANT TRADE EXITS
# =======================================================================
if 'Weak' in entry_tag or 'Early' in entry_tag:
timeout = 360 # 6 hours
elif 'Super' in entry_tag or 'Extreme' in entry_tag:
timeout = 720 # 12 hours
else:
timeout = 540 # 9 hours
if trade_duration_minutes >= timeout:
if current_profit < 0.005:
return log_and_exit(f"Stagnant_{int(timeout/60)}HR_{current_profit*100:.1f}%")
# Force exit deeply losing trades after 16 hours
if trade_duration_minutes >= 960 and current_profit < -0.04:
return log_and_exit(f"Cut_Loss_16HR_{current_profit*100:.1f}%")
# =======================================================================
# 🔟 FRIDAY CLOSE
# =======================================================================
if current_time.weekday() == 4 and current_time.hour >= 22:
if current_profit >= 0.015:
return log_and_exit(f"Friday_Close_{current_profit*100:.1f}%")
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: Optional[str],
side: str, **kwargs) -> bool:
"""
Capture market state when trade opens for ML training
Called when a trade is about to be opened
"""
if self.ml_enabled and ML_AVAILABLE:
try:
# Get current dataframe state
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
if len(dataframe) > 0:
# Extract features AT ENTRY TIME
features = self.extract_ml_features(dataframe, pair)
# Initialize storage if needed
if not hasattr(self, 'trade_entry_features'):
self.trade_entry_features = {}
# Generate temporary ID (use pair + timestamp as key)
temp_id = f"{pair}_{current_time.timestamp()}"
# Store features for this trade
self.trade_entry_features[temp_id] = {
'features': features.tolist(),
'entry_time': current_time.isoformat(),
'entry_tag': entry_tag or 'Unknown',
'pair': pair,
'entry_rate': rate,
'timestamp': current_time.timestamp()
}
logger.debug(f"📸 [ML] Captured entry features for {pair} at {entry_tag}")
except Exception as e:
logger.error(f"❌ [ML] Error capturing entry features: {e}")
return True
def order_filled(self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs) -> None:
"""
Called right after an order fills.
Will be called for all order types (entry, exit, stoploss, position adjustment).
LEARNS FROM ALL TRADES - Records performance for signal locking and Optuna optimization.
:param pair: Pair for trade
:param trade: trade object
:param order: Order object
:param current_time: datetime object, containing the current datetime
:param kwargs: Ensure to keep this here so updates to this won't break your strategy
"""
from logging import getLogger
logger = getLogger(__name__)
# Only process when trade is completely closed (exit order filled)
if not trade.is_open and order.ft_order_side == 'sell':
try:
# Get trade details
entry_tag = getattr(trade, 'enter_tag', 'Unknown')
exit_reason = getattr(trade, 'exit_reason', 'Unknown')
profit_ratio = trade.close_profit or 0.0
profit_abs = trade.close_profit_abs or 0.0
# Calculate trade duration
duration = (trade.close_date_utc - trade.open_date_utc).total_seconds() / 60
# Log trade close
logger.info(f"🔒 TRADE CLOSED: {pair}")
logger.info(f" 🏷️ Entry: {entry_tag} | Exit: {exit_reason}")
logger.info(f" 💰 Profit: {profit_ratio*100:.2f}% ({profit_abs:.4f} USDT)")
logger.info(f" ⏱️ Duration: {duration:.0f} minutes")
logger.info(f" ⚖️ Leverage: {trade.leverage}x")
# Record trade for signal performance tracking
if not hasattr(self, 'trade_history'):
self.trade_history = {}
if entry_tag not in self.trade_history:
self.trade_history[entry_tag] = []
trade_record = {
'pair': pair,
'entry_tag': entry_tag,
'exit_reason': exit_reason,
'profit': profit_ratio,
'profit_abs': profit_abs,
'duration': duration,
'open_date': trade.open_date_utc,
'close_date': trade.close_date_utc,
'leverage': trade.leverage
}
self.trade_history[entry_tag].append(trade_record)
# Keep only last 50 trades per signal (memory management)
if len(self.trade_history[entry_tag]) > 50:
self.trade_history[entry_tag] = self.trade_history[entry_tag][-50:]
# Trigger Optuna retraining if enabled
if self.optuna_manager and hasattr(self, 'maybe_optimize_coin'):
# Check if we should retrain based on trade count
total_trades_for_pair = len([t for tag_trades in self.trade_history.values()
for t in tag_trades if t['pair'] == pair])
# Retrain every 20 trades
if total_trades_for_pair > 0 and total_trades_for_pair % 20 == 0:
logger.info(f"🔄 Triggering Optuna retraining for {pair} (trade #{total_trades_for_pair})")
self.maybe_optimize_coin(pair, force_startup=False)
# Check signal performance for locking
coin_params = self.coin_params.get(pair, {})
signal_lock_enabled = coin_params.get('signal_lock_enabled', True)
if signal_lock_enabled and entry_tag != 'Unknown':
recent_trades = self.trade_history[entry_tag][-15:] # Last 15 trades
if len(recent_trades) >= 8: # Need at least 8 trades to evaluate
profits = [t['profit'] for t in recent_trades]
wins = sum(1 for p in profits if p > 0)
win_rate = wins / len(profits)
avg_profit = sum(profits) / len(profits)
# Lock signal if performance is poor
if win_rate < 0.35 or avg_profit < -0.015:
logger.warning(f"⚠️ SIGNAL PERFORMANCE DEGRADED: {entry_tag}")
logger.warning(f" Win Rate: {win_rate*100:.1f}% | Avg Profit: {avg_profit*100:.2f}%")
logger.warning(f" 🔒 Locking signal for 12 hours")
if not hasattr(self, 'signal_locks'):
self.signal_locks = {}
from datetime import timezone
self.signal_locks[lock_key] = datetime.now(timezone.utc)
logger.info(f"✅ Trade close processing complete for {pair}")
except Exception as e:
logger.error(f"❌ Error in order_filled for {pair}: {e}")
import traceback
logger.error(traceback.format_exc())
def maybe_optimize_coin(self, pair: str, force_startup: bool = False):
"""ENHANCED optimization trigger with profit protection before optimization"""
if not self.optuna_manager:
logger.debug(f"🚫 [OPTUNA] OptunaManager not available for {pair}")
return
# =======================================================================
# 🆕 STEP 1: EXIT ALL PROFITABLE TRADES BEFORE OPTIMIZATION
# =======================================================================
try:
open_trades = Trade.get_trades_proxy(pair=pair, is_open=True)
profitable_trades = []
for trade in open_trades:
try:
# Calculate current profit
current_rate = trade.close_rate_requested if trade.close_rate_requested else trade.open_rate
current_profit = trade.calc_profit_ratio(current_rate)
if current_profit > 0:
profitable_trades.append((trade, current_profit))
except Exception as e:
logger.debug(f"[PRE-OPT] Error checking trade profit: {e}")
if profitable_trades:
logger.info(f"💰 [PRE-OPTIMIZATION] Found {len(profitable_trades)} profitable trades for {pair}")
for trade, profit in profitable_trades:
logger.info(f" 🚪 Marking {pair} for exit at {profit*100:.2f}% profit before optimization")
trade.exit_reason = f"Pre_Opt_Profit_{profit*100:.1f}%"
logger.info(f"✅ [PRE-OPTIMIZATION] Marked {len(profitable_trades)} trades for exit")
# Give time for exits to process
import time
time.sleep(10) # 10 seconds for orders to fill
else:
logger.info(f"ℹ️ [PRE-OPTIMIZATION] No profitable trades to exit for {pair}")
except Exception as e:
logger.error(f"❌ [PRE-OPTIMIZATION] Error exiting trades for {pair}: {e}")
import traceback
logger.debug(f"🔍 [PRE-OPT] Full traceback: {traceback.format_exc()}")
# =======================================================================
# STEP 2: PROCEED WITH OPTIMIZATION
# =======================================================================
# ENHANCED: Force startup optimization or intelligent check
if force_startup:
logger.info(f"🚀 [OPTUNA] FORCED STARTUP OPTIMIZATION for {pair}")
should_optimize = True
else:
should_optimize = self.optuna_manager.should_optimize(pair)
if not should_optimize:
return
logger.info(f"🔄 [OPTUNA] Checking optimization conditions for {pair}")
try:
trades_count = self.get_coin_trades_count(pair)
optimization_count = self.optuna_manager.optimization_trigger_count.get(pair, 0)
logger.info(f"📊 [OPTUNA] {pair} has {trades_count} trades, {optimization_count} optimizations done")
# ENHANCED: More flexible trade requirements
min_trades_required = max(0, optimization_count * 5) # Require more trades for subsequent optimizations
if not force_startup and trades_count < min_trades_required:
logger.info(f"⏳ [OPTUNA] Not enough trades for {pair} optimization ({trades_count}/{min_trades_required})")
return
# Track optimization type
if force_startup:
opt_type = "STARTUP"
elif self.optuna_manager.should_optimize_based_on_performance(pair):
opt_type = "PERFORMANCE-TRIGGERED"
else:
opt_type = "PERIODIC"
logger.info(f"✨ [OPTUNA] {opt_type} OPTIMIZATION for {pair} (trades: {trades_count})")
objective_func = self.create_objective_function(pair)
# ENHANCED: More trials for performance-triggered optimizations
n_trials = 100 if opt_type == "PERFORMANCE-TRIGGERED" else 60
# Start optimization
self.optuna_manager.optimize_coin(pair, objective_func, n_trials=n_trials)
# Track optimization count
self.optuna_manager.optimization_trigger_count[pair] = optimization_count + 1
logger.info(f"✅ [OPTUNA] Completed {opt_type} optimization for {pair}")
except Exception as e:
logger.error(f"❌ [OPTUNA] Optimization failed for {pair}: {e}")
import traceback
logger.debug(f"🔍 [OPTUNA] Full traceback: {traceback.format_exc()}")
def get_coin_trades_count(self, pair: str) -> int:
"""Get number of trades for specific coin"""
# ADDED: Hilfsmethode für Trade-Anzahl pro Coin
try:
from freqtrade.persistence import Trade
trades = Trade.get_trades_proxy(pair=pair)
count = len(trades) if trades else 0
logger.debug(f"📊 [OPTUNA] Trade count for {pair}: {count}")
return count
except Exception as e:
logger.error(f"❌ [OPTUNA] Failed to get trade count for {pair}: {e}")
return 0
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
rate: float, time_in_force: str, exit_reason: str,
current_time: datetime, **kwargs) -> bool:
"""Track trade exits and update performance - works in backtesting"""
try:
profit_ratio = trade.calc_profit_ratio(rate)
# Update Optuna performance if available
if self.optuna_manager:
self.optuna_manager.update_performance(pair, profit_ratio)
logger.debug(f"📈 [OPTUNA] Updated performance for {pair}: {profit_ratio:.4f}")
if pair not in self.coin_performance:
self.coin_performance[pair] = 0.0
self.coin_performance[pair] += profit_ratio
# Update signal performance tracking for backtesting
enter_tag = getattr(trade, 'enter_tag', None)
if enter_tag:
self.update_signal_performance(enter_tag, pair, profit_ratio)
# Debug output
result_type = "WIN" if profit_ratio > 0 else "LOSS"
print(f"📊 Signal Performance Tracked: {pair} {enter_tag} - {result_type}")
print(f" Profit: {profit_ratio:.4f} ({profit_ratio*100:.2f}%)")
print(f" Exit reason: {exit_reason}")
# Log significant performance updates
if abs(profit_ratio) > 0.02:
logger.info(f"💰 [OPTUNA] Significant trade result for {pair}: {profit_ratio:.2%} (cumulative: {self.coin_performance[pair]:.2%})")
except Exception as e:
logger.error(f"⛔ [OPTUNA] Failed to update performance for {pair}: {e}")
# ML: Collect training data when trade closes - FIXED VERSION
if self.ml_enabled and ML_AVAILABLE:
try:
profit_ratio = trade.calc_profit_ratio(rate)
# Try to find stored entry features by pair
if hasattr(self, 'trade_entry_features'):
# Find matching entry by pair (most recent for this pair)
matching_entries = {k: v for k, v in self.trade_entry_features.items()
if v['pair'] == pair}
if matching_entries:
# Get most recent entry for this pair
latest_key = max(matching_entries.keys(),
key=lambda k: matching_entries[k]['timestamp'])
entry_data = self.trade_entry_features[latest_key]
# Store complete trade data
trade_data = {
'pair': pair,
'features': entry_data['features'],
'signal_type': entry_data['entry_tag'],
'profit': profit_ratio,
'win': profit_ratio > 0,
'entry_time': entry_data['entry_time'],
'exit_time': current_time.isoformat(),
'exit_reason': exit_reason,
'timestamp': datetime.now().isoformat()
}
self.ml_training_data.append(trade_data)
# Cleanup
del self.trade_entry_features[latest_key]
if len(self.ml_training_data) % 10 == 0:
self.save_ml_training_data()
logger.info(f"✅ [ML] Collected data: {pair} {entry_data['entry_tag']} → {profit_ratio*100:.2f}%")
else:
logger.warning(f"⚠️ [ML] No entry features found for {pair}")
except Exception as e:
logger.error(f"❌ [ML] Error collecting data: {e}")
return True
def on_trade_close(self, trade: Trade, **kwargs) -> None:
"""Called when a trade is closed - captures ALL exits including stoploss"""
try:
entry_tag = getattr(trade, 'enter_tag', '')
if entry_tag: # Only track if we have an entry tag
pair = trade.pair
profit_ratio = trade.calc_profit_ratio()
# Update signal performance for ALL exits (wins and losses)
self.update_signal_performance(entry_tag, pair, profit_ratio)
# Log the trade result
result_type = "WIN" if profit_ratio > 0 else "LOSS"
exit_reason = getattr(trade, 'exit_reason', 'unknown')
print(f"Trade closed: {pair} {entry_tag} - {result_type}")
print(f" Profit: {profit_ratio:.4f} ({profit_ratio*100:.2f}%)")
print(f" Exit reason: {exit_reason}")
except Exception as e:
print(f"Error in on_trade_close: {e}")
# ============================================================================
# ML PREDICTIVE ENTRY METHODS
# ============================================================================
def extract_ml_features(self, dataframe: pd.DataFrame, pair: str) -> np.ndarray:
"""Extract features for ML prediction - ENHANCED VERSION"""
try:
last = dataframe.iloc[-1]
prev = dataframe.iloc[-2] if len(dataframe) > 1 else last
# Calculate recent profit average for this pair
recent_profits = []
if hasattr(self, 'ml_training_data'):
pair_trades = [t for t in self.ml_training_data if t.get('pair') == pair]
recent_profits = [t['profit'] for t in pair_trades[-10:]]
recent_profit_avg = np.mean(recent_profits) if recent_profits else 0.0
# NEW: Get divergence count
div_count = last.get('total_bullish_divergences_count', 0) + last.get('total_bearish_divergences_count', 0)
# NEW: Determine trend direction
ema20 = last.get('ema20', last['close'])
ema50 = last.get('ema50', last['close'])
ema200 = last.get('ema200', last['close'])
if ema20 > ema50 > ema200:
trend_direction = 1.0 # Uptrend
elif ema20 < ema50 < ema200:
trend_direction = -1.0 # Downtrend
else:
trend_direction = 0.0 # Sideways
features = np.array([
last.get('rsi', 50),
last.get('adx', 20),
last.get('atr', 0) / last['close'] if last['close'] > 0 else 0,
last.get('volume_ratio', 1.0),
last.get('volatility', 0.01),
1 if last.get('ema21', 0) > last.get('ema50', 0) else 0,
(last['close'] - last.get('bollinger_lowerband', last['close'])) /
(last.get('bollinger_upperband', last['close']) - last.get('bollinger_lowerband', last['close']) + 0.0001),
last.get('signal_strength', 0),
(last['close'] - last.get('ema21', last['close'])) / last['close'],
(last['close'] - last.get('ema50', last['close'])) / last['close'],
last.get('adx', 20) / 100.0,
last.get('rsi', 50) / 100.0,
1 if (last.get('total_bullish_divergences', 0) > 0 or last.get('total_bearish_divergences', 0) > 0) else 0,
recent_profit_avg,
div_count, # NEW
trend_direction # NEW
])
return np.nan_to_num(features, nan=0.0, posinf=1.0, neginf=0.0)
except Exception as e:
logger.error(f"❌ [ML] Feature extraction error: {e}")
return np.zeros(len(self.ml_feature_names))
def ml_predict_entry(self, dataframe: pd.DataFrame, pair: str, direction: str) -> tuple:
"""ML predicts if we should enter now (long or short)"""
if not self.ml_trained or self.ml_model is None:
return False, 0.5
try:
features = self.extract_ml_features(dataframe, pair).reshape(1, -1)
proba = self.ml_model.predict_proba(features)[0]
confidence = proba[1]
last_candle = dataframe.iloc[-1]
rsi = last_candle.get('rsi', 50)
if direction == 'long':
direction_ok = rsi < 55
threshold = 0.70
else:
direction_ok = rsi > 45
threshold = 0.70
should_enter = (confidence >= threshold) and direction_ok
return should_enter, confidence
except Exception as e:
logger.error(f"❌ [ML] Prediction error: {e}")
return False, 0.5
def collect_ml_training_data(self, pair: str, trade: Trade, dataframe: pd.DataFrame, profit: float):
"""Collect data when trade closes"""
try:
features = self.extract_ml_features(dataframe, pair)
trade_data = {
'pair': pair,
'features': features.tolist(),
'profit': profit,
'win': profit > 0,
'timestamp': datetime.now().isoformat()
}
self.ml_training_data.append(trade_data)
if len(self.ml_training_data) % 10 == 0:
self.save_ml_training_data()
if len(self.ml_training_data) >= 100 and len(self.ml_training_data) % 50 == 0:
logger.info(f"🎯 [ML] Auto-retraining triggered ({len(self.ml_training_data)} trades)")
self.train_ml_model()
except Exception as e:
logger.error(f"❌ [ML] Data collection error: {e}")
def train_ml_model(self):
"""Train the ML model"""
try:
if len(self.ml_training_data) < 100:
logger.warning(f"⚠️ [ML] Need 100+ trades, have {len(self.ml_training_data)}")
return False
logger.info(f"🎯 [ML] Training model with {len(self.ml_training_data)} trades...")
X = np.array([t['features'] for t in self.ml_training_data])
y = np.array([1 if t['profit'] > 0 else 0 for t in self.ml_training_data])
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
self.ml_model = RandomForestClassifier(
n_estimators=100,
max_depth=8,
min_samples_split=10,
random_state=42,
n_jobs=-1
)
self.ml_model.fit(X_train, y_train)
train_acc = self.ml_model.score(X_train, y_train)
val_acc = self.ml_model.score(X_val, y_val)
logger.info(f"✅ [ML] Training complete!")
logger.info(f" 📊 Train accuracy: {train_acc:.1%}")
logger.info(f" 📊 Val accuracy: {val_acc:.1%}")
importances = self.ml_model.feature_importances_
top_features = sorted(zip(self.ml_feature_names, importances), key=lambda x: x[1], reverse=True)[:5]
logger.info(f" 🔍 Top 5 features:")
for feat, imp in top_features:
logger.info(f" • {feat}: {imp:.3f}")
self.ml_trained = True
self.save_ml_model()
return True
except Exception as e:
logger.error(f"❌ [ML] Training failed: {e}")
return False
def save_ml_model(self):
"""Save ML model to disk"""
try:
from pathlib import Path
model_dir = Path("user_data/strategies/ml_models")
model_dir.mkdir(parents=True, exist_ok=True)
model_path = model_dir / "ml_entry_predictor.pkl"
joblib.dump(self.ml_model, model_path)
logger.info(f"💾 [ML] Model saved to {model_path}")
except Exception as e:
logger.error(f"❌ [ML] Failed to save model: {e}")
def validate_and_load_ml_model(self):
"""Load ML model and validate feature compatibility"""
try:
from pathlib import Path
model_path = Path("user_data/strategies/ml_models/ml_entry_predictor.pkl")
if model_path.exists():
temp_model = joblib.load(model_path)
# Check if model expects correct number of features
if hasattr(temp_model, 'n_features_in_'):
expected_features = temp_model.n_features_in_
current_features = len(self.ml_feature_names)
if expected_features != current_features:
logger.warning(f"⚠️ [ML] Model mismatch: expects {expected_features}, code has {current_features}")
logger.warning(f"⚠️ [ML] Deleting old model - will retrain with new features")
model_path.unlink()
self.ml_model = None
self.ml_trained = False
return
else:
self.ml_model = temp_model
self.ml_trained = True
logger.info(f"📂 [ML] Model loaded ({expected_features} features)")
else:
# Old sklearn version - just load it
self.ml_model = temp_model
self.ml_trained = True
logger.info(f"📂 [ML] Model loaded")
else:
logger.info(f"📂 [ML] No saved model found - will train from backtest")
self.ml_model = None
self.ml_trained = False
except Exception as e:
logger.error(f"❌ [ML] Failed to load model: {e}")
self.ml_model = None
self.ml_trained = False
def save_ml_training_data(self):
"""Save training data to disk"""
try:
from pathlib import Path
import json
import numpy as np
data_dir = Path("user_data/strategies/ml_models")
data_dir.mkdir(parents=True, exist_ok=True)
# Convert numpy arrays and bools to native Python types
cleaned_data = []
for sample in self.ml_training_data:
cleaned_sample = {}
for key, value in sample.items():
if isinstance(value, np.ndarray):
cleaned_sample[key] = value.tolist()
elif isinstance(value, (np.bool_, bool)):
cleaned_sample[key] = bool(value)
elif isinstance(value, (np.integer, np.floating)):
cleaned_sample[key] = float(value)
else:
cleaned_sample[key] = value
cleaned_data.append(cleaned_sample)
data_path = data_dir / "ml_training_data.json"
with open(data_path, 'w') as f:
json.dump(cleaned_data, f, indent=2)
logger.debug(f"💾 [ML] Training data saved ({len(self.ml_training_data)} trades)")
except Exception as e:
logger.error(f"❌ [ML] Failed to save training data: {e}")
def load_ml_training_data(self):
"""Load training data from disk"""
try:
from pathlib import Path
import json
data_path = Path("user_data/strategies/ml_models/ml_training_data.json")
if data_path.exists():
with open(data_path, 'r') as f:
self.ml_training_data = json.load(f)
logger.info(f"📂 [ML] Loaded {len(self.ml_training_data)} training samples")
if not self.ml_trained and len(self.ml_training_data) >= 100:
logger.info(f"🎯 [ML] Starting initial training...")
self.train_ml_model()
else:
logger.info(f"📂 [ML] No training data found - starting fresh")
self.ml_training_data = []
except Exception as e:
logger.error(f"❌ [ML] Failed to load training data: {e}")
self.ml_training_data = []
# ============================================================================
# ML BACKTEST TRAINING SYSTEM
# ============================================================================
def train_ml_from_backtest(self) -> bool:
"""
Generate ML training data from backtesting historical data
Runs on startup if no trained model exists
"""
try:
logger.info("=" * 80)
logger.info("🎯 [ML BACKTEST] Starting backtest-based training from disk files")
logger.info("=" * 80)
import time
from freqtrade.data.history import load_pair_history
start_time = time.time()
training_samples = []
data_dir, data_format, candle_type = self._get_historical_data_settings()
logger.info(
f"📂 [ML BACKTEST] Loading data from: {data_dir} "
f"(format={data_format}, candle_type={candle_type})"
)
# Get list of pairs - try whitelist first, then scan directory
try:
pairs = self.dp.current_whitelist() if hasattr(self, 'dp') and self.dp else []
except:
pairs = []
# If no pairs from whitelist, scan data directory
if not pairs:
logger.info("📂 [ML BACKTEST] Scanning data directory for available pairs...")
pairs = self._scan_historical_pairs(self.timeframe, data_dir, data_format, candle_type)
logger.info(f"📂 [ML BACKTEST] Found {len(pairs)} pairs with data files")
if not pairs:
logger.error("❌ [ML BACKTEST] No pairs found - check your data directory!")
return False
logger.info(f"📊 [ML BACKTEST] Training on {len(pairs)} pairs")
# For each pair, generate training data
for idx, pair in enumerate(pairs):
logger.info(f"🔄 [ML BACKTEST] Processing {pair} ({idx+1}/{len(pairs)})")
try:
# Load data from disk
dataframe = load_pair_history(
datadir=data_dir,
timeframe=self.timeframe,
pair=pair,
data_format=data_format,
candle_type=candle_type
)
if dataframe is None or len(dataframe) < 500:
logger.warning(f"⚠️ [ML BACKTEST] Not enough data for {pair} ({len(dataframe) if dataframe is not None else 0} candles), skipping")
continue
# Process this pair's data with indicators
logger.debug(f"📊 [ML BACKTEST] Populating indicators for {pair}...")
dataframe = self.populate_indicators(dataframe, {'pair': pair})
# Extract training samples
pair_samples = self.extract_backtest_training_data(dataframe, pair)
training_samples.extend(pair_samples)
logger.info(f"✅ [ML BACKTEST] {pair}: {len(pair_samples)} samples")
except Exception as e:
logger.error(f"❌ [ML BACKTEST] Error processing {pair}: {e}")
import traceback
logger.debug(traceback.format_exc())
continue
# Check if we have enough data
if len(training_samples) < 100:
logger.error(f"❌ [ML BACKTEST] Only {len(training_samples)} samples - need 100+")
return False
logger.info(f"🎯 [ML BACKTEST] Collected {len(training_samples)} total samples")
# Save training data
self.ml_training_data = training_samples
self.save_ml_training_data()
# Train the model
success = self.train_ml_model()
elapsed = time.time() - start_time
logger.info(f"⏱️ [ML BACKTEST] Training completed in {elapsed:.1f} seconds")
return success
except Exception as e:
logger.error(f"❌ [ML BACKTEST] Training failed: {e}")
import traceback
logger.error(traceback.format_exc())
return False
def extract_backtest_training_data(self, dataframe: pd.DataFrame, pair: str) -> list:
"""
Extract training samples from historical data for one pair
For each candle where a signal would have triggered:
- Capture market state (features)
- Look ahead 15-50 candles
- Label: Did it profit >1%?
"""
samples = []
try:
# Need enough candles to look ahead
lookback_window = 50 # Check next 50 candles (12.5 hours on 15m)
profit_threshold = 0.01 # 1% profit target
# Process each candle (except last 50, can't look ahead)
for i in range(100, len(dataframe) - lookback_window):
current_candle = dataframe.iloc[i]
# Check if ANY signal would trigger at this candle
signal_type = self.detect_signal_at_candle(dataframe, i)
if signal_type: # Signal detected!
# Extract features at THIS moment
features = self.extract_features_at_index(dataframe, i, pair)
# Look ahead: would this trade be profitable?
is_profitable = self.check_future_profitability(
dataframe, i, lookback_window, profit_threshold, signal_type
)
# Store sample
sample = {
'pair': pair,
'features': features.tolist(),
'signal_type': signal_type,
'profit': 0.015 if is_profitable else -0.01, # Approximate
'win': is_profitable,
'timestamp': str(current_candle.name),
'candle_index': i
}
samples.append(sample)
return samples
except Exception as e:
logger.error(f"❌ [ML BACKTEST] Error extracting data: {e}")
return []
def detect_signal_at_candle(self, dataframe: pd.DataFrame, index: int) -> str:
"""
Check if any signal would trigger at this candle
Returns signal name or None
"""
try:
row = dataframe.iloc[index]
prev_row = dataframe.iloc[index-1] if index > 0 else row
# Check for divergence signals (simplified)
if row.get('total_bullish_divergences', 0) > 0:
div_count = row.get('total_bullish_divergences_count', 0)
if div_count >= 3:
return 'Bull_Super'
elif div_count == 2:
return 'Bull_HQ'
else:
return 'Bull_Div'
if row.get('total_bearish_divergences', 0) > 0:
div_count = row.get('total_bearish_divergences_count', 0)
if div_count >= 3:
return 'Bear_Super'
elif div_count == 2:
return 'Bear_HQ'
else:
return 'Bear_Div'
# Check Fisher crossover
fisher = row.get('fisher', 0)
fisher_signal = row.get('fisher_signal', 0)
prev_fisher = prev_row.get('fisher', 0)
prev_fisher_signal = prev_row.get('fisher_signal', 0)
if (fisher > fisher_signal and prev_fisher <= prev_fisher_signal):
if row.get('rsi', 50) < 40:
return 'Bull_Fisher'
if (fisher < fisher_signal and prev_fisher >= prev_fisher_signal):
if row.get('rsi', 50) > 60:
return 'Bear_Fisher'
# Check Early signals
if row.get('total_bullish_divergences', 0) > 0 and row.get('rsi', 50) < 50:
return 'Bull_Early'
if row.get('total_bearish_divergences', 0) > 0 and row.get('rsi', 50) > 50:
return 'Bear_Early'
return None # No signal
except Exception as e:
return None
def check_future_profitability(self, dataframe: pd.DataFrame, entry_index: int,
lookback: int, threshold: float, signal_type: str) -> bool:
"""
Look ahead and check if trade would have been profitable
"""
try:
entry_price = dataframe.iloc[entry_index]['close']
is_long = 'Bull' in signal_type
# Look at next N candles
future_candles = dataframe.iloc[entry_index+1:entry_index+lookback+1]
if len(future_candles) < 10:
return False
if is_long:
# For longs: did price go up >threshold?
max_profit = (future_candles['high'].max() - entry_price) / entry_price
return max_profit >= threshold
else:
# For shorts: did price go down >threshold?
max_profit = (entry_price - future_candles['low'].min()) / entry_price
return max_profit >= threshold
except Exception as e:
return False
def extract_features_at_index(self, dataframe: pd.DataFrame, index: int, pair: str) -> np.ndarray:
"""Extract features at a specific candle index (for backtesting)"""
try:
row = dataframe.iloc[index]
prev_row = dataframe.iloc[index-1] if index > 0 else row
# Get divergence count
div_count = (row.get('total_bullish_divergences_count', 0) +
row.get('total_bearish_divergences_count', 0))
# Determine trend
ema20 = row.get('ema20', row['close'])
ema50 = row.get('ema50', row['close'])
ema200 = row.get('ema200', row['close'])
if ema20 > ema50 > ema200:
trend_direction = 1.0
elif ema20 < ema50 < ema200:
trend_direction = -1.0
else:
trend_direction = 0.0
features = np.array([
row.get('rsi', 50),
row.get('adx', 20),
row.get('atr', 0) / row['close'] if row['close'] > 0 else 0,
row.get('volume_ratio', 1.0),
row.get('volatility', 0.01),
1 if row.get('ema21', 0) > row.get('ema50', 0) else 0,
(row['close'] - row.get('bollinger_lowerband', row['close'])) /
(row.get('bollinger_upperband', row['close']) - row.get('bollinger_lowerband', row['close']) + 0.0001),
row.get('signal_strength', 0),
(row['close'] - row.get('ema21', row['close'])) / row['close'],
(row['close'] - row.get('ema50', row['close'])) / row['close'],
row.get('adx', 20) / 100.0,
row.get('rsi', 50) / 100.0,
1 if (row.get('total_bullish_divergences', 0) > 0 or
row.get('total_bearish_divergences', 0) > 0) else 0,
0.0, # recent_profit_avg (not available in backtest)
div_count,
trend_direction
])
return np.nan_to_num(features, nan=0.0, posinf=1.0, neginf=0.0)
except Exception as e:
logger.error(f"❌ [ML] Feature extraction error at index {index}: {e}")
return np.zeros(len(self.ml_feature_names))
def ema_cross_check(dataframe):
dataframe['ema20_50_cross'] = qtpylib.crossed_below(dataframe[resample('ema20')],dataframe[resample('ema50')])
dataframe['ema20_200_cross'] = qtpylib.crossed_below(dataframe[resample('ema20')],dataframe[resample('ema200')])
dataframe['ema50_200_cross'] = qtpylib.crossed_below(dataframe[resample('ema50')],dataframe[resample('ema200')])
return ~(
dataframe['ema20_50_cross']
| dataframe['ema20_200_cross']
| dataframe['ema50_200_cross']
)
def green_candle(dataframe):
return dataframe[resample('open')] < dataframe[resample('close')]
def red_candle(dataframe):
return dataframe[resample('open')] > dataframe[resample('close')]
def keltner_middleband_check(dataframe):
return (dataframe[resample('low')] < dataframe[resample('kc_middleband')]) & (dataframe[resample('high')] > dataframe[resample('kc_middleband')])
def keltner_lowerband_check(dataframe):
return (dataframe[resample('low')] < dataframe[resample('kc_lowerband')]) & (dataframe[resample('high')] > dataframe[resample('kc_lowerband')])
def bollinger_lowerband_check(dataframe):
return (dataframe[resample('low')] < dataframe[resample('bollinger_lowerband')]) & (dataframe[resample('high')] > dataframe[resample('bollinger_lowerband')])
def bollinger_keltner_check(dataframe):
return (dataframe[resample('bollinger_lowerband')] < dataframe[resample('kc_lowerband')]) & (dataframe[resample('bollinger_upperband')] > dataframe[resample('kc_upperband')])
def keltner_upperband_check(dataframe):
return (dataframe[resample('high')] > dataframe[resample('kc_upperband')]) & (dataframe[resample('low')] < dataframe[resample('kc_upperband')])
def bollinger_upperband_check(dataframe):
return (dataframe[resample('high')] > dataframe[resample('bollinger_upperband')]) & (dataframe[resample('low')] < dataframe[resample('bollinger_upperband')])
def ema_check(dataframe):
check = (
(dataframe[resample('ema9')] < dataframe[resample('ema20')])
& (dataframe[resample('ema20')] < dataframe[resample('ema50')])
& (dataframe[resample('ema50')] < dataframe[resample('ema200')]))
return ~check
from enum import Enum
class PivotSource(Enum):
HighLow = 0
Close = 1
def pivot_points(dataframe: DataFrame, window: int = 5, pivot_source: PivotSource = PivotSource.Close) -> DataFrame:
high_source = None
low_source = None
if pivot_source == PivotSource.Close:
high_source = 'close'
low_source = 'close'
elif pivot_source == PivotSource.HighLow:
high_source = 'high'
low_source = 'low'
pivot_points_lows = np.empty(len(dataframe['close'])) * np.nan
pivot_points_highs = np.empty(len(dataframe['close'])) * np.nan
last_values = deque()
# find pivot points
for index, row in enumerate(dataframe.itertuples(index=True, name='Pandas')):
last_values.append(row)
if len(last_values) >= window * 2 + 1:
current_value = last_values[window]
is_greater = True
is_less = True
for window_index in range(0, window):
left = last_values[window_index]
right = last_values[2 * window - window_index]
local_is_greater, local_is_less = check_if_pivot_is_greater_or_less(current_value, high_source, low_source, left, right)
is_greater &= local_is_greater
is_less &= local_is_less
if is_greater:
pivot_points_highs[index - window] = getattr(current_value, high_source)
if is_less:
pivot_points_lows[index - window] = getattr(current_value, low_source)
last_values.popleft()
# find last one
if len(last_values) >= window + 2:
current_value = last_values[-2]
is_greater = True
is_less = True
for window_index in range(0, window):
left = last_values[-2 - window_index - 1]
right = last_values[-1]
local_is_greater, local_is_less = check_if_pivot_is_greater_or_less(current_value, high_source, low_source, left, right)
is_greater &= local_is_greater
is_less &= local_is_less
if is_greater:
pivot_points_highs[index - 1] = getattr(current_value, high_source)
if is_less:
pivot_points_lows[index - 1] = getattr(current_value, low_source)
return pd.DataFrame(index=dataframe.index, data={
'pivot_lows': pivot_points_lows,
'pivot_highs': pivot_points_highs
})
def check_if_pivot_is_greater_or_less(current_value, high_source: str, low_source: str, left, right) -> Tuple[bool, bool]:
is_greater = True
is_less = True
if (getattr(current_value, high_source) < getattr(left, high_source) or
getattr(current_value, high_source) < getattr(right, high_source)):
is_greater = False
if (getattr(current_value, low_source) > getattr(left, low_source) or
getattr(current_value, low_source) > getattr(right, low_source)):
is_less = False
return (is_greater, is_less)
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:
"""Chaikin Money Flow (CMF)
It measures the amount of Money Flow Volume over a specific period.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf
Args:
dataframe(pandas.Dataframe): dataframe containing ohlcv
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
df = dataframe.copy()
mfv = ((df['close'] - df['low']) - (df['high'] - df['close'])) / (df['high'] - df['low'])
mfv = mfv.fillna(0.0) # float division by zero
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')
def is_sideways(dataframe, coin_params=None):
"""
Comprehensive sideways market detection - OPTUNA OPTIMIZED
Uses coin-specific thresholds for best performance per pair
"""
if coin_params is None:
coin_params = {}
# Method 1: ADX (trend strength)
adx_threshold = coin_params.get('sideways_adx_max', 25)
weak_trend = (dataframe['adx'] < adx_threshold)
# Method 2: Band Width (volatility)
kc_width = (dataframe['kc_upperband'] - dataframe['kc_lowerband']) / dataframe['kc_middleband']
kc_threshold = coin_params.get('sideways_kc_width_max', 0.02)
narrow_bands = (kc_width < kc_threshold)
# Method 3: EMA Flatness (direction)
ema_change = abs(dataframe['ema_long'] - dataframe['ema_long'].shift(10)) / dataframe['ema_long'].shift(10)
ema_threshold = coin_params.get('sideways_ema_flat_max', 0.015)
flat_ema = (ema_change < ema_threshold)
# Method 4: Price Range (consolidation)
recent_high = dataframe['high'].rolling(20).max()
recent_low = dataframe['low'].rolling(20).min()
price_range = (recent_high - recent_low) / dataframe['close']
range_threshold = coin_params.get('sideways_range_max', 0.06)
tight_range = (price_range < range_threshold)
# Combine: Count how many conditions are met
sideways_score = (
weak_trend.astype(int) +
narrow_bands.astype(int) +
flat_ema.astype(int) +
tight_range.astype(int)
)
# Require minimum number of conditions (2 or 3)
min_conditions = coin_params.get('sideways_min_conditions', 2)
return (sideways_score >= min_conditions)