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import numpy as np
class TradingLogic:
def __init__(self):
self.risk_reward_ratio = 2.0 # 1:2 risk/reward
def generate_signal(self, predictions, current_price, df):
"""Generate buy/sell signal based on predictions and indicators"""
try:
if len(predictions) < 5:
return "hold", 0.0
# Get last values
rsi = df['RSI'].iloc[-1] if 'RSI' in df.columns else 50
macd = df['MACD'].iloc[-1] if 'MACD' in df.columns else 0
macd_signal = df['MACD_signal'].iloc[-1] if 'MACD_signal' in df.columns else 0
# Prediction trend
pred_trend = np.polyfit(range(len(predictions)), predictions, 1)[0]
# Price vs predictions
pred_mean = np.mean(predictions)
price_diff_pct = (pred_mean - current_price) / current_price if current_price != 0 else 0
# Initialize signals
signals = []
confidences = []
# RSI signal
if rsi < 30:
signals.append("buy")
confidences.append(0.6)
elif rsi > 70:
signals.append("sell")
confidences.append(0.6)
# MACD signal
if macd > macd_signal and macd < 0:
signals.append("buy")
confidences.append(0.7)
elif macd < macd_signal and macd > 0:
signals.append("sell")
confidences.append(0.7)
# Prediction signal
if pred_trend > 0 and price_diff_pct > 0.01:
signals.append("buy")
confidences.append(min(abs(price_diff_pct) * 10, 0.8))
elif pred_trend < 0 and price_diff_pct < -0.01:
signals.append("sell")
confidences.append(min(abs(price_diff_pct) * 10, 0.8))
# Bollinger Bands
if 'BB_lower' in df.columns and 'BB_upper' in df.columns:
bb_position = (current_price - df['BB_lower'].iloc[-1]) / (df['BB_upper'].iloc[-1] - df['BB_lower'].iloc[-1])
if bb_position < 0.2:
signals.append("buy")
confidences.append(0.5)
elif bb_position > 0.8:
signals.append("sell")
confidences.append(0.5)
# Aggregate signals
if not signals:
return "hold", 0.0
buy_count = signals.count("buy")
sell_count = signals.count("sell")
total_signals = len(signals)
if buy_count > sell_count and buy_count >= total_signals * 0.5:
signal = "buy"
confidence = np.mean([c for s, c in zip(signals, confidences) if s == "buy"])
elif sell_count > buy_count and sell_count >= total_signals * 0.5:
signal = "sell"
confidence = np.mean([c for s, c in zip(signals, confidences) if s == "sell"])
else:
signal = "hold"
confidence = 0.0
return signal, confidence
except Exception as e:
print(f"Signal generation error: {e}")
return "hold", 0.0
def calculate_tp_sl(self, current_price, atr, signal):
"""Calculate Take Profit and Stop Loss based on ATR"""
try:
if signal == "hold":
return None, None
# Use 2x ATR for stop loss, 4x ATR for take profit (1:2 ratio)
sl_distance = atr * 2 if atr else current_price * 0.02
tp_distance = atr * 4 if atr else current_price * 0.04
if signal == "buy":
tp = current_price + tp_distance
sl = current_price - sl_distance
else: # sell
tp = current_price - tp_distance
sl = current_price + sl_distance
return round(tp, 2), round(sl, 2)
except Exception as e:
print(f"TP/SL calculation error: {e}")
return None, None |