# --- Do not remove these libs ---
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date
import logging
import pickle
from pathlib import Path
import datetime
from datetime import datetime, timedelta, timezone
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
from pandas import DataFrame, Series
from technical.util import resample_to_interval, resampled_merge
from freqtrade.strategy import (IStrategy, BooleanParameter, CategoricalParameter, DecimalParameter,
IntParameter, RealParameter, merge_informative_pair, stoploss_from_open,
stoploss_from_absolute)
from freqtrade.persistence import Trade
from typing import List, Tuple, Optional, Dict
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from collections import deque
import sys
from importlib import metadata
from functools import lru_cache
from scipy.fft import fft, fftfreq
from scipy.stats import skew, kurtosis
import warnings
warnings.simplefilter(action="ignore", category=pd.errors.PerformanceWarning)
logger = logging.getLogger(__name__)
# === SKLEARN IMPORTS FOR CONFIDENCE SCORING ===
try:
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.preprocessing import RobustScaler
from sklearn.metrics import accuracy_score
SKLEARN_AVAILABLE = True
logger.info("Signal Confidence Scoring enabled (sklearn available)")
except ImportError:
SKLEARN_AVAILABLE = False
logger.warning("Signal Confidence Scoring disabled (sklearn not available)")
logger = logging.getLogger(__name__)
# === SIGNAL CONFIDENCE SCORING SYSTEM ===
class SignalConfidenceScorer:
"""
Scores your existing signals based on historical success patterns
Doesn't change signal logic - only adds confidence scores
"""
def __init__(self):
self.models = {}
self.scalers = {}
self.is_trained = {}
self.models_dir = Path("user_data/strategies/confidence_models")
self.models_dir.mkdir(parents=True, exist_ok=True)
# Confidence thresholds (optimizable in strategy)
self.min_confidence_threshold = 65 # Only take signals with 65%+ confidence
self.high_confidence_threshold = 80 # Premium signals
self._load_models()
def create_signal_features(self, dataframe: DataFrame, signal_row_idx: int) -> dict:
"""
Create features that describe the context around a signal
This captures what makes your signals successful
"""
if signal_row_idx < 20: # Need history
return None
row = dataframe.iloc[signal_row_idx]
features = {
# Market context when signal fired
'rsi_value': row['rsi'],
'rsi_position': (row['rsi'] - 30) / (70 - 30), # Normalized RSI position
'volume_ratio': row['volume'] / (dataframe['volume'].iloc[signal_row_idx-20:signal_row_idx].mean() + 1e-10),
# Divergence strength
'bull_div_count': row.get('total_bullish_divergences_count', 0),
'bear_div_count': row.get('total_bearish_divergences_count', 0),
'has_strong_divergence': 1 if row.get('total_bullish_divergences_count', 0) >= 2 or row.get('total_bearish_divergences_count', 0) >= 2 else 0,
# Price position in bands
'kc_position': (row['close'] - row['kc_lowerband']) / (row['kc_upperband'] - row['kc_lowerband'] + 1e-10),
'distance_from_ema20': (row['close'] - row['ema20']) / row['close'],
'distance_from_ema50': (row['close'] - row['ema50']) / row['close'],
# Momentum context
'momentum_2c': (row['close'] - dataframe['close'].iloc[signal_row_idx-2]) / dataframe['close'].iloc[signal_row_idx-2],
'momentum_5c': (row['close'] - dataframe['close'].iloc[signal_row_idx-5]) / dataframe['close'].iloc[signal_row_idx-5],
# Volatility context
'atr_percentile': dataframe['atr'].iloc[signal_row_idx-50:signal_row_idx].rank(pct=True).iloc[-1] if signal_row_idx >= 50 else 0.5,
'volatility_regime': row['atr'] / (dataframe['atr'].iloc[signal_row_idx-20:signal_row_idx].mean() + 1e-10),
# Market structure
'adx_strength': row['adx'],
'trend_alignment': 1 if row['ema20'] > row['ema50'] > row['ema200'] else (-1 if row['ema20'] < row['ema50'] < row['ema200'] else 0),
# Time context (if available)
'hour': 12, # Default mid-day
# Recent price action
'consolidation_score': dataframe['high'].iloc[signal_row_idx-10:signal_row_idx].max() / dataframe['low'].iloc[signal_row_idx-10:signal_row_idx].min(),
# Signal strength
'signal_strength': row.get('signal_strength', 3),
}
return features
def calculate_signal_outcome(self, dataframe: DataFrame, signal_row_idx: int,
is_long: bool, forward_periods: int = 10) -> dict:
"""
Calculate how successful this signal was
"""
if signal_row_idx + forward_periods >= len(dataframe):
return None
entry_price = dataframe['close'].iloc[signal_row_idx]
# Look forward to see what happened
future_slice = dataframe.iloc[signal_row_idx+1:signal_row_idx+forward_periods+1]
if is_long:
max_profit = (future_slice['high'].max() - entry_price) / entry_price
max_loss = (future_slice['low'].min() - entry_price) / entry_price
final_return = (future_slice['close'].iloc[-1] - entry_price) / entry_price
else:
max_profit = (entry_price - future_slice['low'].min()) / entry_price
max_loss = (entry_price - future_slice['high'].max()) / entry_price
final_return = (entry_price - future_slice['close'].iloc[-1]) / entry_price
# Define success criteria for 15m timeframe
profit_target = 0.012 # 1.2% profit target (reasonable for 15m)
stop_loss = -0.008 # 0.8% stop loss
success_score = 0
if max_profit >= profit_target:
success_score += 40 # Hit profit target
if max_loss <= stop_loss:
success_score -= 30 # Hit stop loss
if final_return > 0:
success_score += 20 # Positive at end
if final_return > profit_target * 0.5:
success_score += 20 # Good final return
# Risk-adjusted score
if max_loss != 0:
risk_reward = max_profit / abs(max_loss)
if risk_reward > 2:
success_score += 20
return {
'success_score': max(0, min(100, success_score)),
'max_profit': max_profit,
'max_loss': max_loss,
'final_return': final_return,
'hit_target': max_profit >= profit_target,
'hit_stop': max_loss <= stop_loss
}
def train_confidence_model(self, dataframe: DataFrame, pair: str) -> dict:
"""
Train a model to predict signal confidence based on historical outcomes
"""
if not SKLEARN_AVAILABLE:
return {'status': 'sklearn_not_available'}
# Find all your historical signals
long_signals = dataframe[dataframe['enter_long'] == 1].index
short_signals = dataframe[dataframe['enter_short'] == 1].index
if len(long_signals) + len(short_signals) < 30:
return {'status': 'insufficient_historical_signals'}
# Extract features and outcomes for each signal
training_data = []
# Process long signals
for signal_idx in long_signals:
if signal_idx >= len(dataframe) - 15: # Skip recent signals (need forward data)
continue
features = self.create_signal_features(dataframe, signal_idx)
outcome = self.calculate_signal_outcome(dataframe, signal_idx, is_long=True)
if features and outcome:
features['signal_type'] = 'long'
training_data.append({
'features': features,
'confidence': outcome['success_score'],
'success': outcome['success_score'] >= 60
})
# Process short signals
for signal_idx in short_signals:
if signal_idx >= len(dataframe) - 15:
continue
features = self.create_signal_features(dataframe, signal_idx)
outcome = self.calculate_signal_outcome(dataframe, signal_idx, is_long=False)
if features and outcome:
features['signal_type'] = 'short'
training_data.append({
'features': features,
'confidence': outcome['success_score'],
'success': outcome['success_score'] >= 60
})
if len(training_data) < 20:
return {'status': 'insufficient_training_data'}
# Prepare training dataset
feature_names = list(training_data[0]['features'].keys())
X = []
y_confidence = []
for sample in training_data:
feature_vector = []
for fname in feature_names:
val = sample['features'].get(fname, 0)
# Convert string features to numeric
if fname == 'signal_type':
val = 1 if val == 'long' else 0
feature_vector.append(float(val))
X.append(feature_vector)
y_confidence.append(sample['confidence'])
X = np.array(X)
y_confidence = np.array(y_confidence)
# Clean data
valid_mask = ~(np.isnan(X).any(axis=1) | np.isnan(y_confidence) | np.isinf(X).any(axis=1))
X = X[valid_mask]
y_confidence = y_confidence[valid_mask]
if len(X) < 15:
return {'status': 'insufficient_clean_data'}
# Scale features
scaler = RobustScaler()
X_scaled = scaler.fit_transform(X)
# Train confidence prediction model
confidence_model = RandomForestRegressor(
n_estimators=100,
max_depth=10,
min_samples_split=5,
min_samples_leaf=3,
random_state=42,
n_jobs=-1
)
confidence_model.fit(X_scaled, y_confidence)
# Store models
self.models[pair] = {
'confidence_model': confidence_model,
'feature_names': feature_names
}
self.scalers[pair] = scaler
self.is_trained[pair] = True
# Save models
self._save_models(pair)
# Evaluate performance
conf_score = confidence_model.score(X_scaled, y_confidence)
return {
'status': 'success',
'training_samples': len(X),
'confidence_r2': conf_score,
'avg_historical_confidence': y_confidence.mean(),
}
def score_current_signals(self, dataframe: DataFrame, pair: str) -> pd.Series:
"""
Score current signals based on trained model
Returns confidence scores (0-100) for each signal
"""
if pair not in self.is_trained or not self.is_trained[pair]:
return pd.Series(75, index=dataframe.index, dtype=float) # Default neutral-high confidence
confidence_scores = pd.Series(75, index=dataframe.index, dtype=float)
# Find current signals
current_long_signals = dataframe[dataframe['enter_long'] == 1].index
current_short_signals = dataframe[dataframe['enter_short'] == 1].index
models = self.models[pair]
scaler = self.scalers[pair]
feature_names = models['feature_names']
# Score long signals
for signal_idx in current_long_signals:
features = self.create_signal_features(dataframe, signal_idx)
if features:
features['signal_type'] = 'long'
# Convert to feature vector
feature_vector = []
for fname in feature_names:
val = features.get(fname, 0)
if fname == 'signal_type':
val = 1 if val == 'long' else 0
feature_vector.append(float(val))
try:
X_scaled = scaler.transform([feature_vector])
confidence = models['confidence_model'].predict(X_scaled)[0]
confidence_scores.iloc[signal_idx] = max(20, min(100, confidence))
except:
confidence_scores.iloc[signal_idx] = 75 # Fallback
# Score short signals
for signal_idx in current_short_signals:
features = self.create_signal_features(dataframe, signal_idx)
if features:
features['signal_type'] = 'short'
feature_vector = []
for fname in feature_names:
val = features.get(fname, 0)
if fname == 'signal_type':
val = 1 if val == 'short' else 0
feature_vector.append(float(val))
try:
X_scaled = scaler.transform([feature_vector])
confidence = models['confidence_model'].predict(X_scaled)[0]
confidence_scores.iloc[signal_idx] = max(20, min(100, confidence))
except:
confidence_scores.iloc[signal_idx] = 75 # Fallback
return confidence_scores
def _save_models(self, pair: str):
"""Save models to disk"""
try:
safe_pair = pair.replace('/', '_').replace(':', '_')
model_path = self.models_dir / f"{safe_pair}_confidence.pkl"
with open(model_path, 'wb') as f:
pickle.dump({
'models': self.models[pair],
'scaler': self.scalers[pair],
'is_trained': self.is_trained[pair]
}, f)
except Exception as e:
logger.warning(f"Failed to save confidence models for {pair}: {e}")
def _load_models(self):
"""Load existing models from disk"""
try:
for model_file in self.models_dir.glob("*_confidence.pkl"):
pair = model_file.stem.replace('_confidence', '').replace('_', '/')
with open(model_file, 'rb') as f:
data = pickle.load(f)
self.models[pair] = data['models']
self.scalers[pair] = data['scaler']
self.is_trained[pair] = data['is_trained']
logger.info(f"Loaded confidence model for {pair}")
except Exception as e:
logger.debug(f"Could not load confidence models: {e}")
# Initialize global confidence scorer
if SKLEARN_AVAILABLE:
confidence_scorer = SignalConfidenceScorer()
else:
confidence_scorer = None
logger = logging.getLogger(__name__)
class PlotConfig():
def __init__(self):
self.config = {
'main_plot': {
resample('bollinger_upperband') : {'color': 'rgba(4,137,122,0.7)'},
resample('kc_upperband') : {'color': 'rgba(4,146,250,0.7)'},
resample('kc_middleband') : {'color': 'rgba(4,146,250,0.7)'},
resample('kc_lowerband') : {'color': 'rgba(4,146,250,0.7)'},
resample('bollinger_lowerband') : {
'color': 'rgba(4,137,122,0.7)',
'fill_to': resample('bollinger_upperband'),
'fill_color': 'rgba(4,137,122,0.07)'
},
resample('ema9') : {'color': 'purple'},
resample('ema20') : {'color': 'yellow'},
resample('ema50') : {'color': 'red'},
resample('ema200') : {'color': 'white'},
resample('rsi') : {'color': 'green'},
'trend_1h_1h': {'color': 'orange'},
},
'subplots': {
"ATR" : {
resample('atr'):{'color':'firebrick'}
},
"Signal Strength": {
resample('signal_strength'):{'color':'blue'}
},
"Confidence Score": {
resample('signal_confidence'):{'color':'orange'}
}
}
}
def add_total_divergences_in_config(self, dataframe):
self.config['main_plot'][resample("total_bullish_divergences")] = {
"plotly": {
'mode': 'markers+text',
'text': resample("total_bullish_divergences_count"),
'hovertext': resample("total_bullish_divergences_names"),
'textfont':{'size': 11, 'color':'green'},
'textposition':'bottom center',
'marker': {
'symbol': 'diamond',
'size': 11,
'line': {
'width': 2
},
'color': 'green'
}
}
}
self.config['main_plot'][resample("total_bearish_divergences")] = {
"plotly": {
'mode': 'markers+text',
'text': resample("total_bearish_divergences_count"),
'hovertext': resample("total_bearish_divergences_names"),
'textfont':{'size': 11, 'color':'crimson'},
'textposition':'top center',
'marker': {
'symbol': 'diamond',
'size': 11,
'line': {
'width': 2
},
'color': 'crimson'
}
}
}
return self
class AlexBandSniperAI(IStrategy):
"""
Alex BandSniperAi on 15m Timeframe - OPTIMIZED VERSION
Version 86AI - Claude optimized Entry & Exit
Key improvements:
- Signal Confidence Scoring
- 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
"""
INTERFACE_VERSION = 3
def version(self) -> str:
return "v34C-confidence-optimized"
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": 100 # Disables ROI completely - let custom_exit handle everything
}
# Optimal stoploss designed for the strategy.
stoploss = -0.20
can_short = True
use_custom_stoploss = True
leverage_value = 10.0 # Reduced leverage for better risk management
trailing_stop = True
trailing_stop_positive = 0.40 # Only trail after 40% profit (very high)
trailing_stop_positive_offset = 0.45 # Start trailing at 45% profit
trailing_only_offset_is_reached = True
# 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
# Your existing hyperopt parameters
min_divergence_count = IntParameter(1, 3, default=1, space='buy', optimize=True, load=True)
min_signal_strength = IntParameter(1, 5, default=1, space='buy', optimize=True, load=True)
volume_threshold = DecimalParameter(1.0, 1.5, default=1.0, decimals=1, space='buy', optimize=True, load=True)
adx_threshold = IntParameter(15, 30, default=15, space='buy', optimize=True, load=True)
# 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)
# === NEW CONFIDENCE SCORING PARAMETERS ===
confidence_threshold = IntParameter(50, 85, default=65, space='buy', optimize=True, load=True)
high_confidence_threshold = IntParameter(75, 95, default=80, space='buy', optimize=True, load=True)
enable_confidence_training = BooleanParameter(default=True, space='buy', optimize=False, load=True)
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 10
# Protection parameters (your existing ones)
cooldown_lookback = IntParameter(2, 48, default=5, space="protection", optimize=True)
stop_duration = IntParameter(12, 200, default=20, space="protection", optimize=True)
use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True)
use_cooldown_protection = BooleanParameter(default=True, space="protection", optimize=True)
use_max_drawdown_protection = BooleanParameter(default=True, space="protection", optimize=True)
max_drawdown_lookback = IntParameter(100, 300, default=200, space="protection", optimize=True)
max_drawdown_trade_limit = IntParameter(5, 15, default=10, space="protection", optimize=True)
max_drawdown_stop_duration = IntParameter(1, 5, default=2, space="protection", optimize=True)
max_allowed_drawdown = DecimalParameter(0.08, 0.25, default=0.15, decimals=2, space="protection", optimize=True)
stoploss_guard_lookback = IntParameter(30, 80, default=50, space="protection", optimize=True)
stoploss_guard_trade_limit = IntParameter(2, 6, default=3, space="protection", optimize=True)
stoploss_guard_only_per_pair = BooleanParameter(default=True, space="protection", optimize=True)
# 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 get_ticker_indicator(self):
return int(self.timeframe[:-1])
def informative_pairs(self):
"""Define additional timeframes to download"""
pairs = self.dp.current_whitelist()
return [(pair, '1h') for pair in pairs]
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Enhanced indicator population with multi-timeframe analysis - Fixed for dry run
"""
# === MULTI-TIMEFRAME ANALYSIS ===
# Get 1h timeframe for trend confirmation with improved error handling
try:
# Check if we're in backtesting mode or if pair supports 1h data
if hasattr(self.dp, 'runmode') and self.dp.runmode.value in ['backtest', 'hyperopt']:
# In backtesting, try to get 1h data but don't fail if unavailable
informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1h')
else:
# In live/dry run, be more cautious about data availability
try:
informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1h')
except Exception:
informative_1h = None
# Enhanced data validation
if (informative_1h is not None and
len(informative_1h) > 50 and # Reduced minimum requirement
not informative_1h.empty and
'close' in informative_1h.columns):
try:
# 1h Trend indicators with additional error checking
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)
# Fill NaN values before merging
informative_1h = informative_1h.bfill().ffill()
# Safe merge with additional error handling
dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, '1h', ffill=True)
logger.info(f"Successfully merged 1h data for {metadata['pair']}")
except Exception as merge_error:
logger.warning(f"Failed to merge 1h data for {metadata['pair']}: {merge_error}")
self._add_dummy_1h_columns(dataframe)
else:
logger.info(f"Using fallback 1h indicators for {metadata['pair']} (insufficient data)")
self._add_dummy_1h_columns(dataframe)
except Exception as e:
logger.warning(f"Error accessing 1h data for {metadata['pair']}: {e}")
self._add_dummy_1h_columns(dataframe)
# === 15M TIMEFRAME INDICATORS ===
informative = dataframe.copy()
# Volume analysis with safe calculations
try:
informative['volume_sma'] = informative['volume'].rolling(window=20, min_periods=1).mean()
informative['volume_ratio'] = informative['volume'] / informative['volume_sma']
informative['volume_ratio'] = informative['volume_ratio'].fillna(1.0)
except:
informative['volume_sma'] = informative['volume']
informative['volume_ratio'] = 1.0
# Volatility analysis with safe calculations
try:
informative['atr'] = qtpylib.atr(informative, window=14, exp=False)
informative['volatility'] = informative['atr'] / informative['close']
informative['volatility'] = informative['volatility'].fillna(0.01)
except:
informative['atr'] = informative['close'] * 0.02
informative['volatility'] = 0.01
# Momentum Indicators with error handling
try:
informative['rsi'] = ta.RSI(informative)
informative['stoch'] = ta.STOCH(informative)['slowk']
informative['roc'] = ta.ROC(informative)
informative['uo'] = ta.ULTOSC(informative)
informative['ao'] = qtpylib.awesome_oscillator(informative)
informative['macd'] = ta.MACD(informative)['macd']
informative['cci'] = ta.CCI(informative)
informative['cmf'] = chaikin_money_flow(informative, 20)
informative['obv'] = ta.OBV(informative)
informative['mfi'] = ta.MFI(informative)
informative['adx'] = ta.ADX(informative)
# Fill NaN values for all indicators
indicator_columns = ['rsi', 'stoch', 'roc', 'uo', 'ao', 'macd', 'cci', 'cmf', 'obv', 'mfi', 'adx']
for col in indicator_columns:
if col in informative.columns:
informative[col] = informative[col].bfill().fillna(50 if col in ['rsi', 'mfi'] else 0)
except Exception as e:
logger.warning(f"Error calculating momentum indicators: {e}")
# Provide fallback values
informative['rsi'] = 50
informative['stoch'] = 50
informative['roc'] = 0
informative['uo'] = 50
informative['ao'] = 0
informative['macd'] = 0
informative['cci'] = 0
informative['cmf'] = 0
informative['obv'] = informative['volume'].cumsum()
informative['mfi'] = 50
informative['adx'] = 25
# Keltner Channel with error handling
try:
keltner = emaKeltner(informative)
informative["kc_upperband"] = keltner["upper"]
informative["kc_middleband"] = keltner["mid"]
informative["kc_lowerband"] = keltner["lower"]
except:
informative["kc_upperband"] = informative['close'] * 1.02
informative["kc_middleband"] = informative['close']
informative["kc_lowerband"] = informative['close'] * 0.98
# Bollinger Bands with error handling
try:
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=20, stds=2)
informative['bollinger_upperband'] = bollinger['upper']
informative['bollinger_lowerband'] = bollinger['lower']
except:
informative['bollinger_upperband'] = informative['close'] * 1.02
informative['bollinger_lowerband'] = informative['close'] * 0.98
# EMA - Exponential Moving Average with error handling
try:
informative['ema9'] = ta.EMA(informative, timeperiod=9)
informative['ema20'] = ta.EMA(informative, timeperiod=20)
informative['ema50'] = ta.EMA(informative, timeperiod=50)
informative['ema200'] = ta.EMA(informative, timeperiod=200)
# Fill NaN values for EMAs
ema_columns = ['ema9', 'ema20', 'ema50', 'ema200']
for col in ema_columns:
if col in informative.columns:
informative[col] = informative[col].bfill().fillna(informative['close'])
except:
informative['ema9'] = informative['close']
informative['ema20'] = informative['close']
informative['ema50'] = informative['close']
informative['ema200'] = informative['close']
# Pivot Points with error handling
try:
pivots = pivot_points(informative, self.window.value)
informative['pivot_lows'] = pivots['pivot_lows']
informative['pivot_highs'] = pivots['pivot_highs']
except Exception as e:
logger.warning(f"Error calculating pivot points: {e}")
informative['pivot_lows'] = np.nan
informative['pivot_highs'] = np.nan
# === DIVERGENCE ANALYSIS ===
try:
self.initialize_divergences_lists(informative)
(high_iterator, low_iterator) = self.get_iterators(informative)
# Add divergences for multiple indicators
indicators = ['rsi', 'stoch', 'roc', 'uo', 'ao', 'macd', 'cci', 'cmf', 'obv', 'mfi']
for indicator in indicators:
try:
if indicator in informative.columns:
self.add_divergences(informative, indicator, high_iterator, low_iterator)
except Exception as e:
logger.warning(f"Error adding divergences for {indicator}: {e}")
continue
except Exception as e:
logger.warning(f"Error in divergence analysis: {e}")
# Initialize with empty divergence data
informative["total_bullish_divergences"] = np.nan
informative["total_bullish_divergences_count"] = 0
informative["total_bullish_divergences_names"] = ''
informative["total_bearish_divergences"] = np.nan
informative["total_bearish_divergences_count"] = 0
informative["total_bearish_divergences_names"] = ''
# === SIGNAL STRENGTH CALCULATION ===
try:
informative['signal_strength'] = self.calculate_signal_strength(informative)
except:
informative['signal_strength'] = 0
# === MERGE BACK TO DATAFRAME ===
for col in informative.columns:
if col not in dataframe.columns:
dataframe[col] = informative[col]
else:
dataframe[col] = informative[col]
# Additional market structure analysis with error handling
try:
dataframe['chop'] = choppiness_index(dataframe['high'], dataframe['low'], dataframe['close'], window=14)
dataframe['natr'] = ta.NATR(dataframe['high'], dataframe['low'], dataframe['close'], window=14)
dataframe['natr_diff'] = dataframe['natr'] - dataframe['natr'].shift(1)
dataframe['natr_direction_change'] = (dataframe['natr_diff'] * dataframe['natr_diff'].shift(1) < 0)
except:
dataframe['chop'] = 50
dataframe['natr'] = 0.02
dataframe['natr_diff'] = 0
dataframe['natr_direction_change'] = False
# Support/Resistance levels with error handling
try:
dataframe['swing_high'] = dataframe['high'].rolling(window=50, min_periods=1).max()
dataframe['swing_low'] = dataframe['low'].rolling(window=50, min_periods=1).min()
dataframe['distance_to_resistance'] = (dataframe['swing_high'] - dataframe['close']) / dataframe['close']
dataframe['distance_to_support'] = (dataframe['close'] - dataframe['swing_low']) / dataframe['close']
except:
dataframe['swing_high'] = dataframe['high']
dataframe['swing_low'] = dataframe['low']
dataframe['distance_to_resistance'] = 0.02
dataframe['distance_to_support'] = 0.02
# Initialize confidence score column
dataframe['signal_confidence'] = 75 # Default confidence
# Plot configuration with error handling
try:
self.plot_config = (
PlotConfig()
.add_total_divergences_in_config(dataframe)
.config)
except:
self.plot_config = None
return dataframe
def _add_dummy_1h_columns(self, dataframe):
"""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:
"""
Your exact original entry logic + Signal Confidence Scoring
No signal logic changes - only intelligent filtering based on historical success
"""
# Initialize
dataframe['enter_long'] = 0
dataframe['enter_short'] = 0
dataframe["enter_tag"] = ""
# === YOUR EXACT ORIGINAL CONDITIONS (UNCHANGED) ===
bullish_divergence = (dataframe['total_bullish_divergences'].shift(1) > 0)
bearish_divergence = (dataframe['total_bearish_divergences'].shift(1) > 0)
# Your existing filters (unchanged)
volatility_ok = (
(dataframe['volatility'].shift(1) >= self.min_volatility.value * 0.5) &
(dataframe['volatility'].shift(1) <= self.max_volatility.value * 2.0)
)
bands_long = (
(dataframe['low'] <= dataframe['kc_lowerband']) |
(dataframe['close'] <= dataframe['kc_lowerband'])
)
bands_short = (
(dataframe['high'] >= dataframe['kc_upperband']) |
(dataframe['close'] >= dataframe['kc_upperband'])
)
rsi_long_ok = (
(dataframe['rsi'].shift(1) < self.rsi_overbought.value + 5) &
(dataframe['rsi'].shift(1) > 25)
)
rsi_short_ok = (
(dataframe['rsi'].shift(1) > self.rsi_oversold.value - 5) &
(dataframe['rsi'].shift(1) < 75)
)
has_volume = dataframe['volume'] > 0
# === ALL YOUR ORIGINAL CONDITIONS (UNCHANGED) ===
long_condition_div = bullish_divergence
short_condition_div = bearish_divergence
long_condition_primary = (
bullish_divergence & volatility_ok & bands_long & rsi_long_ok & has_volume
)
short_condition_primary = (
bearish_divergence & volatility_ok & bands_short & rsi_short_ok & has_volume
)
long_condition_secondary = (
bullish_divergence &
(dataframe['close'] > dataframe['close'].shift(2)) &
(dataframe['rsi'].shift(1) > 35) &
(dataframe['rsi'].shift(1) < 70) &
has_volume
)
short_condition_secondary = (
bearish_divergence &
(dataframe['close'] < dataframe['close'].shift(2)) &
(dataframe['rsi'].shift(1) > 30) &
(dataframe['rsi'].shift(1) < 80) &
has_volume
)
long_condition_tertiary = (
bullish_divergence &
(dataframe['close'] > dataframe['ema20']) &
(dataframe['rsi'].shift(1) > 40) &
(dataframe['rsi'].shift(1) < 65) &
(dataframe['volume'] > dataframe['volume'].rolling(10).mean()) &
has_volume
)
short_condition_tertiary = (
bearish_divergence &
(dataframe['close'] < dataframe['ema20']) &
(dataframe['rsi'].shift(1) > 35) &
(dataframe['rsi'].shift(1) < 75) &
(dataframe['volume'] > dataframe['volume'].rolling(10).mean()) &
has_volume
)
long_condition_quaternary = (
bullish_divergence &
(dataframe['close'].shift(1) < dataframe['ema20'].shift(1)) &
(dataframe['close'] > dataframe['ema20']) &
(dataframe['rsi'] > 45) &
(dataframe['volume'] > dataframe['volume'].rolling(5).mean() * 1.2) &
has_volume
)
short_condition_quaternary = (
bearish_divergence &
(dataframe['close'].shift(1) > dataframe['ema20'].shift(1)) &
(dataframe['close'] < dataframe['ema20']) &
(dataframe['rsi'] < 55) &
(dataframe['volume'] > dataframe['volume'].rolling(5).mean() * 1.2) &
has_volume
)
long_condition_fifth = (
bullish_divergence &
(dataframe['close'] > dataframe['kc_middleband']) &
(dataframe['close'] < dataframe['kc_upperband']) &
(dataframe['close'] > dataframe['close'].shift(1)) &
(dataframe['rsi'].shift(1) > 35) &
(dataframe['rsi'].shift(1) < 70) &
has_volume
)
short_condition_fifth = (
bearish_divergence &
(dataframe['close'] < dataframe['kc_middleband']) &
(dataframe['close'] > dataframe['kc_lowerband']) &
(dataframe['close'] < dataframe['close'].shift(1)) &
(dataframe['rsi'].shift(1) > 30) &
(dataframe['rsi'].shift(1) < 65) &
has_volume
)
long_condition_sixth = (
(dataframe['total_bullish_divergences_count'] >= 2) &
bullish_divergence &
(dataframe['rsi'].shift(1) > 30) &
(dataframe['rsi'].shift(1) < 75) &
has_volume
)
short_condition_sixth = (
(dataframe['total_bearish_divergences_count'] >= 2) &
bearish_divergence &
(dataframe['rsi'].shift(1) > 25) &
(dataframe['rsi'].shift(1) < 70) &
has_volume
)
long_condition_seventh = (
(dataframe['ema20'] > dataframe['ema50']) &
(dataframe['close'].shift(1) < dataframe['ema20'].shift(1)) &
(dataframe['close'] > dataframe['ema20']) &
(dataframe['volume'] > dataframe['volume'].rolling(20).mean() * 1.2) &
(dataframe['rsi'].shift(1) > 30) &
(dataframe['rsi'].shift(1) < 70) &
(dataframe['close'] > dataframe['close'].shift(2)) &
(dataframe['atr'] > dataframe['atr'].rolling(20).mean() * 0.5) &
(dataframe['close'] > dataframe['kc_middleband']) &
(dataframe['adx'] > 15) &
has_volume
)
short_condition_seventh = (
(dataframe['ema20'] < dataframe['ema50']) &
(dataframe['close'].shift(1) > dataframe['ema20'].shift(1)) &
(dataframe['close'] < dataframe['ema20']) &
(dataframe['volume'] > dataframe['volume'].rolling(20).mean() * 1.2) &
(dataframe['rsi'].shift(1) > 30) &
(dataframe['rsi'].shift(1) < 70) &
(dataframe['close'] < dataframe['close'].shift(2)) &
(dataframe['atr'] > dataframe['atr'].rolling(20).mean() * 0.5) &
(dataframe['close'] < dataframe['kc_middleband']) &
(dataframe['adx'] > 15) &
has_volume
)
long_condition_trend = (
(dataframe['close'] > dataframe['ema20']) &
(dataframe['ema20'] > dataframe['ema50']) &
(dataframe['ema50'] > dataframe['ema200']) &
(dataframe['close'].shift(1) < dataframe['ema20'].shift(1)) &
(dataframe['close'] > dataframe['ema20']) &
(dataframe['volume'] > dataframe['volume'].rolling(20).mean() * 1.5) &
(dataframe['rsi'].shift(1) > 40) &
(dataframe['rsi'].shift(1) < 60) &
(dataframe['close'] > dataframe['close'].shift(2)) &
(dataframe['atr'] > dataframe['atr'].rolling(20).mean() * 1.0) &
(dataframe['close'] > dataframe['kc_middleband']) &
(dataframe['adx'] > 25) &
has_volume
)
short_condition_trend = (
(dataframe['close'] < dataframe['ema20']) &
(dataframe['ema20'] < dataframe['ema50']) &
(dataframe['ema50'] < dataframe['ema200']) &
(dataframe['close'].shift(1) > dataframe['ema20'].shift(1)) &
(dataframe['close'] < dataframe['ema20']) &
(dataframe['volume'] > dataframe['volume'].rolling(20).mean() * 1.5) &
(dataframe['rsi'].shift(1) > 40) &
(dataframe['rsi'].shift(1) < 60) &
(dataframe['close'] < dataframe['close'].shift(2)) &
(dataframe['atr'] > dataframe['atr'].rolling(20).mean() * 1.0) &
(dataframe['close'] < dataframe['kc_middleband']) &
(dataframe['adx'] > 25) &
has_volume
)
long_condition_momentum = (
(dataframe['close'] > dataframe['ema50']) &
(dataframe['ema20'] > dataframe['ema50']) &
(dataframe['ema50'] > dataframe['ema200']) &
(dataframe['rsi'].shift(3) < 50) &
(dataframe['rsi'].shift(1) > 55) &
(dataframe['rsi'] > dataframe['rsi'].shift(1)) &
(dataframe['low'].shift(1) > dataframe['low'].shift(2)) &
(dataframe['close'] > dataframe['high'].shift(2)) &
(dataframe['volume'] > dataframe['volume'].rolling(10).mean() * 1.2) &
(dataframe['close'] < dataframe['kc_upperband'] * 0.995) &
(dataframe['adx'] > 25) &
has_volume
)
short_condition_momentum = (
(dataframe['close'] < dataframe['ema50']) &
(dataframe['ema20'] < dataframe['ema50']) &
(dataframe['ema50'] < dataframe['ema200']) &
(dataframe['rsi'].shift(3) > 50) &
(dataframe['rsi'].shift(1) < 45) &
(dataframe['rsi'] < dataframe['rsi'].shift(1)) &
(dataframe['high'].shift(1) < dataframe['high'].shift(2)) &
(dataframe['close'] < dataframe['low'].shift(2)) &
(dataframe['volume'] > dataframe['volume'].rolling(10).mean() * 1.2) &
(dataframe['close'] > dataframe['kc_lowerband'] * 1.005) &
(dataframe['adx'] > 25) &
has_volume
)
# === YOUR ORIGINAL COMBINED CONDITIONS ===
original_long_condition = (
long_condition_div |
long_condition_primary |
long_condition_secondary |
long_condition_tertiary |
long_condition_quaternary |
long_condition_fifth |
long_condition_sixth |
long_condition_seventh |
long_condition_trend |
long_condition_momentum
)
original_short_condition = (
short_condition_div |
short_condition_primary |
short_condition_secondary |
short_condition_tertiary |
short_condition_quaternary |
short_condition_fifth |
short_condition_sixth |
short_condition_seventh |
short_condition_trend |
short_condition_momentum
)
# === APPLY ORIGINAL SIGNALS FIRST ===
dataframe.loc[original_long_condition, 'enter_long'] = 1
dataframe.loc[original_short_condition, 'enter_short'] = 1
# === SIGNAL CONFIDENCE SCORING & FILTERING ===
pair = metadata['pair']
if SKLEARN_AVAILABLE and confidence_scorer and self.enable_confidence_training.value:
# Train confidence model if needed (weekly retraining)
if (len(dataframe) >= 500 and
(pair not in confidence_scorer.is_trained or not confidence_scorer.is_trained.get(pair, False))):
logger.info(f"Training confidence model for {pair}")
result = confidence_scorer.train_confidence_model(dataframe, pair)
if result['status'] == 'success':
logger.info(f"Confidence model trained: {result['training_samples']} samples, "
f"R2={result['confidence_r2']:.3f}")
else:
logger.warning(f"Confidence training failed for {pair}: {result['status']}")
# Update confidence scorer thresholds from strategy parameters
confidence_scorer.min_confidence_threshold = self.confidence_threshold.value
confidence_scorer.high_confidence_threshold = self.high_confidence_threshold.value
# Score current signals
confidence_scores = confidence_scorer.score_current_signals(dataframe, pair)
dataframe['signal_confidence'] = confidence_scores
# Apply confidence filter - only keep high-confidence signals
low_confidence_mask = confidence_scores < self.confidence_threshold.value
# Count signals before filtering
original_signals = dataframe['enter_long'].sum() + dataframe['enter_short'].sum()
# Filter out low-confidence signals
dataframe.loc[low_confidence_mask, 'enter_long'] = 0
dataframe.loc[low_confidence_mask, 'enter_short'] = 0
# Count signals after filtering
filtered_signals = dataframe['enter_long'].sum() + dataframe['enter_short'].sum()
if original_signals > 0:
logger.info(f"Confidence filter for {pair}: {original_signals} -> {filtered_signals} signals "
f"(threshold: {self.confidence_threshold.value}%)")
else:
# No confidence filtering - use all original signals
dataframe['signal_confidence'] = 75 # Default confidence
# === ENHANCED TAGGING WITH CONFIDENCE INFO ===
for idx in dataframe.index:
if dataframe.loc[idx, 'enter_long'] == 1:
confidence = dataframe.loc[idx, 'signal_confidence']
# Determine confidence level for tag
if confidence >= self.high_confidence_threshold.value:
conf_tag = f"_High{confidence:.0f}"
elif confidence >= self.confidence_threshold.value:
conf_tag = f"_Med{confidence:.0f}"
else:
conf_tag = f"_Low{confidence:.0f}"
# Your original tag priority logic + confidence info
if long_condition_div.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bull_Div{conf_tag}'
elif long_condition_primary.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bull_E1{conf_tag}'
elif long_condition_secondary.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bull_E2{conf_tag}'
elif long_condition_tertiary.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bull_E3{conf_tag}'
elif long_condition_quaternary.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bull_E4{conf_tag}'
elif long_condition_fifth.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bull_E5{conf_tag}'
elif long_condition_sixth.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bull_E6{conf_tag}'
elif long_condition_seventh.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bull_E7{conf_tag}'
elif long_condition_trend.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bull_Trend{conf_tag}'
elif long_condition_momentum.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bull_Momentum{conf_tag}'
elif dataframe.loc[idx, 'enter_short'] == 1:
confidence = dataframe.loc[idx, 'signal_confidence']
if confidence >= self.high_confidence_threshold.value:
conf_tag = f"_High{confidence:.0f}"
elif confidence >= self.confidence_threshold.value:
conf_tag = f"_Med{confidence:.0f}"
else:
conf_tag = f"_Low{confidence:.0f}"
# Your original short tag logic + confidence
if short_condition_div.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bear_Div{conf_tag}'
elif short_condition_primary.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bear_E1{conf_tag}'
elif short_condition_secondary.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bear_E2{conf_tag}'
elif short_condition_tertiary.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bear_E3{conf_tag}'
elif short_condition_quaternary.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bear_E4{conf_tag}'
elif short_condition_fifth.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bear_E5{conf_tag}'
elif short_condition_sixth.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bear_E6{conf_tag}'
elif short_condition_seventh.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bear_E7{conf_tag}'
elif short_condition_trend.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bear_Trend{conf_tag}'
elif short_condition_momentum.loc[idx]:
dataframe.loc[idx, 'enter_tag'] = f'Bear_Momentum{conf_tag}'
# === CONFIDENCE FILTER DEBUG INFO ===
if len(dataframe) > 0:
last_idx = dataframe.index[-1]
if dataframe.loc[last_idx, 'enter_long'] == 1 or dataframe.loc[last_idx, 'enter_short'] == 1:
confidence = dataframe.loc[last_idx, 'signal_confidence']
logger.warning(f"🔍 CONFIDENCE DEBUG for {metadata['pair']}:")
logger.warning(f" Div_long: {long_condition_div.loc[last_idx]}")
logger.warning(f" Primary_long: {long_condition_primary.loc[last_idx]}")
logger.warning(f" Secondary_long: {long_condition_secondary.loc[last_idx]}")
logger.warning(f" Confidence Score: {confidence:.1f}%")
logger.warning(f" Above threshold: {confidence >= self.confidence_threshold.value}")
logger.warning(f" Div_short: {short_condition_div.loc[last_idx]}")
logger.warning(f" Primary_short: {short_condition_primary.loc[last_idx]}")
# === ENHANCED DEBUG OUTPUT ===
if True: # Show for all pairs
recent_entries = dataframe['enter_long'].tail(10).sum() + dataframe['enter_short'].tail(10).sum()
if recent_entries > 0:
latest = dataframe.iloc[-1]
logger.info(f"🚀 CONFIDENCE-FILTERED {metadata['pair']} ENTRY DETECTED!")
logger.info(f" 🏷️ Tag: {latest['enter_tag']}")
logger.info(f" 📈 Confidence Score: {latest['signal_confidence']:.1f}%")
logger.info(f" 📊 RSI: {latest['rsi']:.1f}")
logger.info(f" 💧 Volume Ratio: {latest['volume_ratio']:.2f}")
logger.info(f" 🎯 Bull Div Count: {latest.get('total_bullish_divergences_count', 0)}")
logger.info(f" 🎯 Bear Div Count: {latest.get('total_bearish_divergences_count', 0)}")
# General debug output for all pairs
if len(dataframe) > 0:
last_row = dataframe.iloc[-1]
total_entries = last_row.get('enter_long', 0) + last_row.get('enter_short', 0)
if total_entries > 0:
logger.info(f"{metadata['pair']} Confidence-Filtered Entry: {last_row.get('enter_tag', '')}, "
f"Confidence:{last_row.get('signal_confidence', 75):.1f}%")
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Exit when opposite entry signals appear
"""
# Initialize
dataframe['exit_long'] = 0
dataframe['exit_short'] = 0
dataframe['exit_tag'] = ''
# === USE YOUR EXISTING ENTRY CONDITIONS AS EXIT TRIGGERS ===
# Exit longs when ANY short entry condition triggers
exit_long_condition = (
(dataframe['enter_short'] == 1) # Any short signal exits long
)
# Exit shorts when ANY long entry condition triggers
exit_short_condition = (
(dataframe['enter_long'] == 1) # Any long signal exits short
)
# Set the exits
dataframe.loc[exit_long_condition, 'exit_long'] = 1
dataframe.loc[exit_short_condition, 'exit_short'] = 1
# Set tags
dataframe.loc[exit_long_condition, 'exit_tag'] = 'Exit_Long_Short_Signal'
dataframe.loc[exit_short_condition, 'exit_tag'] = 'Exit_Short_Long_Signal'
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 and confidence
"""
try:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
if len(dataframe) > 0:
current_signal_strength = dataframe['signal_strength'].iloc[-1]
current_confidence = dataframe.get('signal_confidence', pd.Series([75])).iloc[-1]
# Base leverage adjustment on signal strength
if current_signal_strength >= 8:
strength_multiplier = 1.0 # Full leverage for strong signals
elif current_signal_strength >= 6:
strength_multiplier = 0.8 # 80% leverage
elif current_signal_strength >= 4:
strength_multiplier = 0.6 # 60% leverage
else:
strength_multiplier = 0.4 # 40% leverage for weak signals
# Additional adjustment based on confidence
if current_confidence >= self.high_confidence_threshold.value:
confidence_multiplier = 1.0 # No reduction for high confidence
elif current_confidence >= self.confidence_threshold.value:
confidence_multiplier = 0.9 # Small reduction for medium confidence
else:
confidence_multiplier = 0.7 # Larger reduction for low confidence
final_multiplier = strength_multiplier * confidence_multiplier
return self.leverage_value * final_multiplier
except:
pass
return self.leverage_value * 0.5 # Conservative fallback
def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, after_fill: bool, **kwargs) -> float:
"""Modified to not interfere with profit taking"""
# Only apply stoploss for losses or very small profits
if current_profit > 0.04: # Let custom_exit handle profits > 4%
return None
# Your existing stoploss logic here for losses only
try:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
if not dataframe.empty:
current_candle = dataframe.iloc[-1]
atr_value = current_candle.get('atr', 0.02)
atr_multiplier = 3.0 # More conservative
if trade.is_short:
stoploss_price = trade.open_rate + (atr_value * atr_multiplier)
else:
stoploss_price = trade.open_rate - (atr_value * atr_multiplier)
return stoploss_from_absolute(stoploss_price, current_rate,
is_short=trade.is_short, leverage=trade.leverage)
except:
pass
return None # Keep current 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) -> Tuple[pd.Series, pd.Series]:
"""Enhanced divergence finder with improved logic"""
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)
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 validation for bearish divergence
price_diff = abs(bearish_current_pivot - bearish_prev_pivot)
indicator_diff = abs(bearish_ind_current_pivot - bearish_ind_prev_pivot)
time_diff = current_pivot - prev_pivot
# Only accept divergences with sufficient magnitude and time separation
if (price_diff > dataframe['atr'][current_pivot] * 0.5 and
indicator_diff > 5 and
time_diff >= 5):
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)
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 validation for bullish divergence
price_diff = abs(bullish_current_pivot - bullish_prev_pivot)
indicator_diff = abs(bullish_ind_current_pivot - bullish_ind_prev_pivot)
time_diff = current_pivot - prev_pivot
# Only accept divergences with sufficient magnitude and time separation
if (price_diff > dataframe['atr'][current_pivot] * 0.5 and
indicator_diff > 5 and
time_diff >= 5):
bullish_divergences[index] = row.close
dataframe.loc[index, "total_bullish_divergences"] = row.close
# CORRECT - increment BULLISH counters for bullish divergence:
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 bearish_divergence_finder(self, dataframe, indicator, high_iterator, index):
"""Enhanced bearish divergence detection"""
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 - self.window.value - 1, -1):
if i < 0 or i >= len(occurences):
continue
prev_pivot = occurences[i]
if np.isnan(prev_pivot):
continue
# Enhanced divergence validation
price_higher = dataframe['pivot_highs'][current_pivot] > dataframe['pivot_highs'][prev_pivot]
indicator_lower = indicator[current_pivot] < indicator[prev_pivot]
price_lower = dataframe['pivot_highs'][current_pivot] < dataframe['pivot_highs'][prev_pivot]
indicator_higher = indicator[current_pivot] > indicator[prev_pivot]
# Check for classic or hidden divergence
if (price_higher and indicator_lower) or (price_lower and indicator_higher):
# Additional validation: check trend consistency
if self.validate_divergence_trend(dataframe, prev_pivot, current_pivot, 'bearish'):
return (prev_pivot, current_pivot)
except:
pass
return None
def bullish_divergence_finder(self, dataframe, indicator, low_iterator, index):
"""Enhanced bullish divergence detection"""
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 - self.window.value - 1, -1):
if i < 0 or i >= len(occurences):
continue
prev_pivot = occurences[i]
if np.isnan(prev_pivot):
continue
# Enhanced divergence validation
price_lower = dataframe['pivot_lows'][current_pivot] < dataframe['pivot_lows'][prev_pivot]
indicator_higher = indicator[current_pivot] > indicator[prev_pivot]
price_higher = dataframe['pivot_lows'][current_pivot] > dataframe['pivot_lows'][prev_pivot]
indicator_lower = indicator[current_pivot] < indicator[prev_pivot]
# Check for classic or hidden divergence
if (price_lower and indicator_higher) or (price_higher and indicator_lower):
# Additional validation: check trend consistency
if self.validate_divergence_trend(dataframe, prev_pivot, current_pivot, 'bullish'):
return (prev_pivot, current_pivot)
except:
pass
return None
def validate_divergence_trend(self, dataframe, prev_pivot, current_pivot, divergence_type):
"""Validate divergence by checking intermediate trend"""
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 custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
current_profit: float, **kwargs):
"""
Hybrid exit: Emergency + Swing profit-taking for 15m timeframe
"""
trade_duration_minutes = (current_time - trade.open_date_utc).total_seconds() / 60
# === EMERGENCY HIGH PROFIT PROTECTION ===
if current_profit >= 0.40: return "emergency_40pct"
if current_profit >= 0.30: return "emergency_30pct"
if current_profit >= 0.25: return "emergency_25pct"
# === QUICK SCALP PROTECTION (15M SWINGS) ===
if trade_duration_minutes <= 20: # Within 1-2 candles
if current_profit >= 0.08: return "quick_scalp_8pct"
elif current_profit >= 0.06: return "quick_scalp_6pct"
elif current_profit >= 0.04: return "quick_scalp_4pct"
# === 15M SWING PROTECTION ===
# 45 minutes (3 candles) - catch swing moves
if trade_duration_minutes >= 45:
if current_profit >= 0.12: return "swing_45min_12pct"
elif current_profit >= 0.08: return "swing_45min_8pct"
# 90 minutes (6 candles) - reasonable swing duration
if trade_duration_minutes >= 90:
if current_profit >= 0.10: return "swing_90min_10pct"
elif current_profit >= 0.06: return "swing_90min_6pct"
elif current_profit >= 0.04: return "swing_90min_4pct"
# 2 hours (8 candles) - target swing completion
if trade_duration_minutes >= 120:
if current_profit >= 0.08: return "swing_2hr_8pct"
elif current_profit >= 0.05: return "swing_2hr_5pct"
elif current_profit >= 0.03: return "swing_2hr_3pct"
# === ATR-BASED SWING TARGETS ===
try:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
for i in range(1, min(len(dataframe), 20)):
if dataframe.iloc[-i]['date'].to_pydatetime().replace(tzinfo=timezone.utc) <= trade.open_date_utc:
entry_candle = dataframe.iloc[-i-1] if i < len(dataframe) - 1 else dataframe.iloc[-i]
atr_value = entry_candle.get('atr', 0.02)
if trade.is_short:
atr_target_05 = entry_candle['close'] - (atr_value * 0.5)
atr_target_10 = entry_candle['close'] - (atr_value * 1.0)
if current_rate <= atr_target_10 and current_profit > 0.05:
return "atr_1x_short"
elif current_rate <= atr_target_05 and current_profit > 0.03:
return "atr_0.5x_short"
else:
atr_target_05 = entry_candle['close'] + (atr_value * 0.5)
atr_target_10 = entry_candle['close'] + (atr_value * 1.0)
if current_rate >= atr_target_10 and current_profit > 0.05:
return "atr_1x_long"
elif current_rate >= atr_target_05 and current_profit > 0.03:
return "atr_0.5x_long"
break
except:
pass
# === EXTENDED TIME MANAGEMENT ===
if trade_duration_minutes >= 240: # 4+ hours - beyond normal swing
if current_profit >= 0.05: return "extended_5pct"
elif current_profit >= 0.03: return "extended_3pct"
elif current_profit >= 0.02: return "extended_2pct"
# === STAGNANT TRADE CLEANUP ===
if trade_duration_minutes >= 300 and abs(current_profit) < 0.01:
return "stagnant_timeout"
return None
def choppiness_index(high, low, close, window=14):
"""Calculate Choppiness Index"""
natr = pd.Series(ta.NATR(high, low, close, window=window))
high_max = high.rolling(window=window).max()
low_min = low.rolling(window=window).min()
choppiness = 100 * np.log10((natr.rolling(window=window).sum()) / (high_max - low_min)) / np.log10(window)
return choppiness
def resample(indicator):
"""Resample function for compatibility"""
return indicator
def two_bands_check_long(dataframe):
"""Allow long when price is near/at lower band (oversold area)"""
return (
(dataframe['low'] <= dataframe['kc_lowerband']) |
(dataframe['close'] <= dataframe['kc_lowerband'])
)
def two_bands_check_short(dataframe):
"""Allow short when price is near/at upper band (overbought area)"""
return (
(dataframe['high'] >= dataframe['kc_upperband']) |
(dataframe['close'] >= dataframe['kc_upperband'])
)
def green_candle(dataframe):
"""Check for green candle"""
return dataframe[resample('open')] < dataframe[resample('close')]
def red_candle(dataframe):
"""Check for red candle"""
return dataframe[resample('open')] > dataframe[resample('close')]
def pivot_points(dataframe: DataFrame, window: int = 5, pivot_source=None) -> DataFrame:
"""Enhanced pivot point detection"""
from enum import Enum
class PivotSource(Enum):
HighLow = 0
Close = 1
if pivot_source is None:
pivot_source = PivotSource.Close
high_source = 'close' if pivot_source == PivotSource.Close else 'high'
low_source = 'close' if pivot_source == PivotSource.Close else '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 with enhanced validation
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
# Additional validation: ensure pivot is significant
if is_greater:
current_high = getattr(current_value, high_source)
# Check if high is significant enough (above ATR threshold)
if hasattr(current_value, 'atr') and current_high > 0:
pivot_points_highs[index - window] = current_high
if is_less:
current_low = getattr(current_value, low_source)
# Check if low is significant enough
if hasattr(current_value, 'atr') and current_low > 0:
pivot_points_lows[index - window] = current_low
last_values.popleft()
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]:
"""Helper function for pivot point validation"""
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):
"""Calculate EMA-based Keltner Channels"""
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:
"""Calculate Chaikin Money Flow indicator"""
df = dataframe.copy()
mfv = ((df['close'] - df['low']) - (df['high'] - df['close'])) / (df['high'] - df['low'])
mfv = mfv.fillna(0.0)
mfv *= df['volume']
cmf = (mfv.rolling(n, min_periods=0).sum() / df['volume'].rolling(n, min_periods=0).sum())
if fillna:
cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0)
return Series(cmf, name='cmf')# --- Do not remove these libs ---