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Dobromir Popov
391
NN/utils/signal_interpreter.py
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391
NN/utils/signal_interpreter.py
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"""
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Signal Interpreter for Neural Network Trading System
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Converts model predictions into actionable trading signals with enhanced profitability filters
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"""
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import numpy as np
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import logging
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from collections import deque
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import time
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logger = logging.getLogger('NN.utils.signal_interpreter')
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class SignalInterpreter:
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"""
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Enhanced signal interpreter for short-term high-leverage trading
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Converts model predictions to trading signals with adaptive filters
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"""
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def __init__(self, config=None):
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"""
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Initialize signal interpreter with configuration parameters
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Args:
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config (dict): Configuration dictionary with parameters
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"""
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self.config = config or {}
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# Signal thresholds - higher thresholds for high-leverage trading
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self.buy_threshold = self.config.get('buy_threshold', 0.65)
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self.sell_threshold = self.config.get('sell_threshold', 0.65)
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self.hold_threshold = self.config.get('hold_threshold', 0.75)
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# Adaptive parameters
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self.confidence_multiplier = self.config.get('confidence_multiplier', 1.0)
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self.signal_history = deque(maxlen=20) # Store recent signals for pattern recognition
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self.price_history = deque(maxlen=20) # Store recent prices for trend analysis
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# Performance tracking
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self.trade_count = 0
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self.profitable_trades = 0
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self.unprofitable_trades = 0
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self.avg_profit_per_trade = 0
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self.last_trade_time = None
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self.last_trade_price = None
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self.current_position = None # None = no position, 'long' = buy, 'short' = sell
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# Filters for better signal quality
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self.trend_filter_enabled = self.config.get('trend_filter_enabled', True)
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self.volume_filter_enabled = self.config.get('volume_filter_enabled', True)
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self.oscillation_filter_enabled = self.config.get('oscillation_filter_enabled', True)
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# Sensitivity parameters
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self.min_price_movement = self.config.get('min_price_movement', 0.0005) # 0.05% minimum expected movement
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self.hold_cooldown = self.config.get('hold_cooldown', 3) # Minimum periods to wait after a HOLD
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self.consecutive_signals_required = self.config.get('consecutive_signals_required', 2)
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# State tracking
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self.consecutive_buy_signals = 0
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self.consecutive_sell_signals = 0
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self.consecutive_hold_signals = 0
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self.periods_since_last_trade = 0
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logger.info("Signal interpreter initialized with enhanced filters for short-term trading")
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def interpret_signal(self, action_probs, price_prediction=None, market_data=None):
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"""
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Interpret model predictions to generate trading signal
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Args:
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action_probs (ndarray): Model action probabilities [SELL, HOLD, BUY]
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price_prediction (float): Predicted price change (optional)
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market_data (dict): Additional market data for filtering (optional)
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Returns:
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dict: Trading signal with action and metadata
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"""
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# Extract probabilities
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sell_prob, hold_prob, buy_prob = action_probs
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# Apply confidence multiplier - amplifies the signal when model is confident
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adjusted_buy_prob = min(buy_prob * self.confidence_multiplier, 1.0)
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adjusted_sell_prob = min(sell_prob * self.confidence_multiplier, 1.0)
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# Incorporate price prediction if available
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if price_prediction is not None:
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# Strengthen buy signal if price is predicted to rise
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if price_prediction > self.min_price_movement:
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adjusted_buy_prob *= (1.0 + price_prediction * 5)
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adjusted_sell_prob *= (1.0 - price_prediction * 2)
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# Strengthen sell signal if price is predicted to fall
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elif price_prediction < -self.min_price_movement:
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adjusted_sell_prob *= (1.0 + abs(price_prediction) * 5)
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adjusted_buy_prob *= (1.0 - abs(price_prediction) * 2)
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# Track consecutive signals to reduce false signals
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raw_signal = self._get_raw_signal(adjusted_buy_prob, adjusted_sell_prob, hold_prob)
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# Update consecutive signal counters
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if raw_signal == 'BUY':
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self.consecutive_buy_signals += 1
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self.consecutive_sell_signals = 0
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self.consecutive_hold_signals = 0
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elif raw_signal == 'SELL':
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self.consecutive_buy_signals = 0
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self.consecutive_sell_signals += 1
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self.consecutive_hold_signals = 0
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else: # HOLD
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self.consecutive_buy_signals = 0
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self.consecutive_sell_signals = 0
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self.consecutive_hold_signals += 1
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# Apply trend filter if enabled and market data available
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if self.trend_filter_enabled and market_data and 'trend' in market_data:
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raw_signal = self._apply_trend_filter(raw_signal, market_data['trend'])
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# Apply volume filter if enabled and market data available
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if self.volume_filter_enabled and market_data and 'volume' in market_data:
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raw_signal = self._apply_volume_filter(raw_signal, market_data['volume'])
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# Apply oscillation filter to prevent excessive trading
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if self.oscillation_filter_enabled:
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raw_signal = self._apply_oscillation_filter(raw_signal)
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# Create final signal with confidence metrics and metadata
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signal = {
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'action': raw_signal,
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'timestamp': time.time(),
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'confidence': self._calculate_confidence(adjusted_buy_prob, adjusted_sell_prob, hold_prob),
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'price_prediction': price_prediction if price_prediction is not None else 0.0,
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'consecutive_signals': max(self.consecutive_buy_signals, self.consecutive_sell_signals),
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'periods_since_last_trade': self.periods_since_last_trade
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}
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# Update signal history
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self.signal_history.append(signal)
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self.periods_since_last_trade += 1
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# Track trade if action taken
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if signal['action'] in ['BUY', 'SELL']:
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self._track_trade(signal, market_data)
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return signal
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def _get_raw_signal(self, buy_prob, sell_prob, hold_prob):
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"""
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Get raw signal based on adjusted probabilities
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Args:
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buy_prob (float): Buy probability
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sell_prob (float): Sell probability
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hold_prob (float): Hold probability
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Returns:
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str: Raw signal ('BUY', 'SELL', or 'HOLD')
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"""
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# Require higher consecutive signals for high-leverage actions
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if buy_prob > self.buy_threshold and self.consecutive_buy_signals >= self.consecutive_signals_required:
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return 'BUY'
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elif sell_prob > self.sell_threshold and self.consecutive_sell_signals >= self.consecutive_signals_required:
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return 'SELL'
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elif hold_prob > self.hold_threshold:
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return 'HOLD'
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elif buy_prob > sell_prob:
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# If close to threshold but not quite there, still prefer action over hold
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if buy_prob > self.buy_threshold * 0.8:
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return 'BUY'
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else:
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return 'HOLD'
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elif sell_prob > buy_prob:
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# If close to threshold but not quite there, still prefer action over hold
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if sell_prob > self.sell_threshold * 0.8:
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return 'SELL'
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else:
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return 'HOLD'
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else:
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return 'HOLD'
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def _apply_trend_filter(self, raw_signal, trend):
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"""
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Apply trend filter to align signals with overall market trend
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Args:
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raw_signal (str): Raw signal
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trend (str or float): Market trend indicator
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Returns:
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str: Filtered signal
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"""
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# Skip if fresh signal doesn't match trend
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if isinstance(trend, str):
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if raw_signal == 'BUY' and trend == 'downtrend':
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return 'HOLD'
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elif raw_signal == 'SELL' and trend == 'uptrend':
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return 'HOLD'
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elif isinstance(trend, (int, float)):
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# Trend as numerical value (positive = uptrend, negative = downtrend)
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if raw_signal == 'BUY' and trend < -0.2:
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return 'HOLD'
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elif raw_signal == 'SELL' and trend > 0.2:
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return 'HOLD'
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return raw_signal
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def _apply_volume_filter(self, raw_signal, volume):
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"""
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Apply volume filter to ensure sufficient liquidity for trade
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Args:
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raw_signal (str): Raw signal
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volume (dict): Volume data
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Returns:
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str: Filtered signal
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"""
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# Skip trading when volume is too low
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if volume.get('is_low', False) and raw_signal in ['BUY', 'SELL']:
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return 'HOLD'
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# Reduce sensitivity during volume spikes to avoid getting caught in volatility
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if volume.get('is_spike', False):
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# For short-term trading, a spike could be an opportunity if it confirms our signal
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if volume.get('direction', 0) > 0 and raw_signal == 'BUY':
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# Volume spike in buy direction - strengthen buy signal
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return raw_signal
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elif volume.get('direction', 0) < 0 and raw_signal == 'SELL':
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# Volume spike in sell direction - strengthen sell signal
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return raw_signal
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else:
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# Volume spike against our signal - be cautious
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return 'HOLD'
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return raw_signal
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def _apply_oscillation_filter(self, raw_signal):
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"""
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Apply oscillation filter to prevent excessive trading
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Returns:
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str: Filtered signal
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"""
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# Implement a cooldown period after HOLD signals
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if self.consecutive_hold_signals < self.hold_cooldown:
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# Check if we're switching positions too quickly
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if len(self.signal_history) >= 2:
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last_action = self.signal_history[-1]['action']
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if last_action in ['BUY', 'SELL'] and raw_signal != last_action and raw_signal != 'HOLD':
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# We're trying to reverse position immediately after taking one
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# For high-leverage trading, this could be allowed if signal is very strong
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if raw_signal == 'BUY' and self.consecutive_buy_signals >= self.consecutive_signals_required * 1.5:
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# Extra strong buy signal - allow reversal
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return raw_signal
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elif raw_signal == 'SELL' and self.consecutive_sell_signals >= self.consecutive_signals_required * 1.5:
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# Extra strong sell signal - allow reversal
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return raw_signal
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else:
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# Not strong enough to justify immediate reversal
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return 'HOLD'
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# Check for oscillation patterns over time
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if len(self.signal_history) >= 4:
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# Look for alternating BUY/SELL pattern which indicates indecision
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actions = [s['action'] for s in list(self.signal_history)[-4:]]
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if actions.count('BUY') >= 2 and actions.count('SELL') >= 2:
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# Oscillating pattern detected, force a HOLD
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return 'HOLD'
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return raw_signal
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def _calculate_confidence(self, buy_prob, sell_prob, hold_prob):
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"""
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Calculate confidence score for the signal
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Args:
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buy_prob (float): Buy probability
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sell_prob (float): Sell probability
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hold_prob (float): Hold probability
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Returns:
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float: Confidence score (0.0-1.0)
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"""
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# Maximum probability indicates confidence level
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max_prob = max(buy_prob, sell_prob, hold_prob)
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# Calculate the gap between highest and second highest probability
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sorted_probs = sorted([buy_prob, sell_prob, hold_prob], reverse=True)
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prob_gap = sorted_probs[0] - sorted_probs[1]
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# Combine both factors - higher max and larger gap mean more confidence
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confidence = (max_prob * 0.7) + (prob_gap * 0.3)
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# Scale to ensure output is between 0 and 1
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return min(max(confidence, 0.0), 1.0)
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def _track_trade(self, signal, market_data):
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"""
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Track trade for performance monitoring
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Args:
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signal (dict): Trading signal
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market_data (dict): Market data including price
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"""
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self.trade_count += 1
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self.periods_since_last_trade = 0
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# Update position state
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if signal['action'] == 'BUY':
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self.current_position = 'long'
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elif signal['action'] == 'SELL':
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self.current_position = 'short'
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# Store trade time and price if available
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current_time = time.time()
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current_price = market_data.get('price', None) if market_data else None
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# Record profitability if we have both current and previous trade data
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if self.last_trade_time and self.last_trade_price and current_price:
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# Calculate holding period
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holding_period = current_time - self.last_trade_time
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# Calculate profit/loss based on position
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if self.current_position == 'long' and signal['action'] == 'SELL':
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# Closing a long position
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profit_pct = (current_price - self.last_trade_price) / self.last_trade_price
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# Update trade statistics
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if profit_pct > 0:
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self.profitable_trades += 1
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else:
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self.unprofitable_trades += 1
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# Update average profit
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total_trades = self.profitable_trades + self.unprofitable_trades
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self.avg_profit_per_trade = ((self.avg_profit_per_trade * (total_trades - 1)) + profit_pct) / total_trades
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logger.info(f"Closed LONG position with {profit_pct:.4%} profit after {holding_period:.1f}s")
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elif self.current_position == 'short' and signal['action'] == 'BUY':
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# Closing a short position
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profit_pct = (self.last_trade_price - current_price) / self.last_trade_price
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# Update trade statistics
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if profit_pct > 0:
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self.profitable_trades += 1
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else:
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self.unprofitable_trades += 1
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# Update average profit
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total_trades = self.profitable_trades + self.unprofitable_trades
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self.avg_profit_per_trade = ((self.avg_profit_per_trade * (total_trades - 1)) + profit_pct) / total_trades
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logger.info(f"Closed SHORT position with {profit_pct:.4%} profit after {holding_period:.1f}s")
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# Update last trade info
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self.last_trade_time = current_time
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self.last_trade_price = current_price
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def get_performance_stats(self):
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"""
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Get trading performance statistics
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Returns:
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dict: Performance statistics
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"""
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total_trades = self.profitable_trades + self.unprofitable_trades
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win_rate = self.profitable_trades / total_trades if total_trades > 0 else 0
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return {
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'total_trades': self.trade_count,
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'profitable_trades': self.profitable_trades,
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'unprofitable_trades': self.unprofitable_trades,
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'win_rate': win_rate,
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'avg_profit_per_trade': self.avg_profit_per_trade
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}
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def reset(self):
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"""Reset all trading statistics and state"""
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self.signal_history.clear()
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self.price_history.clear()
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self.trade_count = 0
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self.profitable_trades = 0
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self.unprofitable_trades = 0
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self.avg_profit_per_trade = 0
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self.last_trade_time = None
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self.last_trade_price = None
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self.current_position = None
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self.consecutive_buy_signals = 0
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self.consecutive_sell_signals = 0
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self.consecutive_hold_signals = 0
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self.periods_since_last_trade = 0
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logger.info("Signal interpreter reset")
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