feat: Add comprehensive debug logging to trading strategy and optimization
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@ -71,12 +71,20 @@ class DynamicStrategy(Strategy):
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def next(self):
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price = self.data.Close[-1]
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# Debug current price and position
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if len(self.data.Close) % 20 == 0: # Log every 20th candle to avoid spam
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print(f"Current price: {price}, Has position: {bool(self.position)}")
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# Example trading logic using indicators
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for ind_name, ind_config in self.indicator_configs.items():
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if ind_config['type'] == 'SMA':
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if price > self.indicators[ind_name][-1] and not self.position:
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current_sma = self.indicators[ind_name][-1]
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print(f"SMA - Price: {price:.2f}, SMA: {current_sma:.2f}")
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if price > current_sma and not self.position:
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print(f"BUY signal - Price above SMA")
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self.buy()
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elif price < self.indicators[ind_name][-1] and self.position:
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elif price < current_sma and self.position:
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print(f"SELL signal - Price below SMA")
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self.position.close()
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elif ind_config['type'] == 'MACD':
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@ -85,36 +93,49 @@ class DynamicStrategy(Strategy):
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prev_macd = self.indicators[f"{ind_name}_macd"][-2]
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prev_signal = self.indicators[f"{ind_name}_signal"][-2]
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print(f"MACD - MACD: {macd:.2f}, Signal: {signal:.2f}")
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if macd > signal and prev_macd <= prev_signal and not self.position:
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print(f"BUY signal - MACD crossover")
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self.buy()
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elif macd < signal and prev_macd >= prev_signal and self.position:
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print(f"SELL signal - MACD crossunder")
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self.position.close()
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elif ind_config['type'] == 'BB':
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upper = self.indicators[f"{ind_name}_upper"][-1]
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lower = self.indicators[f"{ind_name}_lower"][-1]
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print(f"BB - Price: {price:.2f}, Upper: {upper:.2f}, Lower: {lower:.2f}")
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# Buy when price touches lower band
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if price <= lower and not self.position:
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print(f"BUY signal - Price at lower band")
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self.buy()
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# Sell when price touches upper band
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elif price >= upper and self.position:
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print(f"SELL signal - Price at upper band")
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self.position.close()
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elif ind_config['type'] == 'RSI':
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rsi = self.indicators[ind_name][-1]
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print(f"RSI - Value: {rsi:.2f}")
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# Buy when RSI is below 30 (oversold)
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if rsi < 30 and not self.position:
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print(f"BUY signal - RSI oversold")
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self.buy()
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# Sell when RSI is above 70 (overbought)
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elif rsi > 70 and self.position:
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print(f"SELL signal - RSI overbought")
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self.position.close()
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elif ind_config['type'] == 'EMA':
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if price > self.indicators[ind_name][-1] and not self.position:
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current_ema = self.indicators[ind_name][-1]
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print(f"EMA - Price: {price:.2f}, EMA: {current_ema:.2f}")
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if price > current_ema and not self.position:
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print(f"BUY signal - Price above EMA")
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self.buy()
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elif price < self.indicators[ind_name][-1] and self.position:
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elif price < current_ema and self.position:
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print(f"SELL signal - Price below EMA")
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self.position.close()
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def get_available_indicators() -> Dict:
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@ -149,22 +170,31 @@ def get_available_indicators() -> Dict:
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def prepare_data_for_backtest(df: pd.DataFrame) -> pd.DataFrame:
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"""Prepare the dataframe for backtesting"""
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print("\nPreparing data for backtest...")
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print(f"Initial dataframe shape: {df.shape}")
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print(f"Initial columns: {df.columns.tolist()}")
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# Ensure the dataframe has the required columns
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required_columns = ['Open', 'High', 'Low', 'Close', 'Volume']
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# Rename columns if needed
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df = df.copy()
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df.columns = [c.capitalize() for c in df.columns]
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print(f"Columns after capitalization: {df.columns.tolist()}")
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# Set the date as index if it's not already
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if 'Date' in df.columns:
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df.set_index('Date', inplace=True)
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print("Set Date as index")
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# Verify all required columns exist
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missing_cols = [col for col in required_columns if col not in df.columns]
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if missing_cols:
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print(f"WARNING: Missing columns: {missing_cols}")
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raise ValueError(f"Missing required columns: {missing_cols}")
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print(f"Final dataframe shape: {df.shape}")
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return df
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def backtesting_page():
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@ -259,9 +289,13 @@ def backtesting_page():
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def run_optimization(df: pd.DataFrame, indicator_settings: Dict) -> List:
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"""Run optimization with different parameter combinations"""
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param_combinations = generate_param_combinations(indicator_settings)
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print(f"Generated {len(param_combinations)} parameter combinations")
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results = []
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for params in param_combinations:
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for i, params in enumerate(param_combinations):
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print(f"\nTesting combination {i+1}/{len(param_combinations)}")
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print(f"Parameters: {params}")
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# Configure strategy with current parameters
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DynamicStrategy.indicator_configs = params
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@ -269,6 +303,10 @@ def run_optimization(df: pd.DataFrame, indicator_settings: Dict) -> List:
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bt = Backtest(df, DynamicStrategy, cash=100000, commission=.002)
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stats = bt.run()
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print(f"Results - Return: {stats['Return [%]']:.2f}%, "
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f"Sharpe: {stats['Sharpe Ratio']:.2f}, "
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f"Drawdown: {stats['Max. Drawdown [%]']:.2f}%")
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results.append({
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'parameters': params,
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'Return [%]': stats['Return [%]'],
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