# source: https://raw.githubusercontent.com/kevenbazile/booki/a5c626a13ce29dfc1663a8d74326d84c406803a3/SimplePortfolio.py from freqtrade.strategy import IStrategy from pandas import DataFrame import talib.abstract as ta import logging import numpy as np logger = logging.getLogger(__name__) class Github_kevenbazile_booki__SimplePortfolio__20250614_094946(IStrategy): INTERFACE_VERSION = 3 # FAST profit targets - DCA bots must take profits quickly minimal_roi = { "0": 0.08, # 8% immediate profit target "60": 0.06, # 6% after 1 hour "180": 0.04, # 4% after 3 hours "360": 0.03, # 3% after 6 hours "720": 0.02 # 2% after 12 hours - TAKE ANY PROFIT } stoploss = -0.15 # Wider stop for DCA (should never hit this) timeframe = '5m' process_only_new_candles = True startup_candle_count = 30 can_short = False # AGGRESSIVE DCA but fewer total entries position_adjustment_enable = True max_entry_position_adjustment = 4 # Keep same as original def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ SIMPLE indicators for DCA signals """ # RSI - simple and effective dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) # EMAs for trend dataframe['ema_9'] = ta.EMA(dataframe, timeperiod=9) dataframe['ema_21'] = ta.EMA(dataframe, timeperiod=21) # Volume for confirmation dataframe['volume_avg'] = dataframe['volume'].rolling(window=20).mean() # Price momentum dataframe['price_change'] = dataframe['close'].pct_change(periods=5) * 100 # Support levels (buying opportunities) dataframe['low_5'] = dataframe['low'].rolling(window=5).min() dataframe['low_20'] = dataframe['low'].rolling(window=20).min() return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ MUCH MORE SELECTIVE buying - only on big dips """ dataframe.loc[ ( # ONLY BUY MAJOR DIPS - be very selective ( (dataframe['rsi'] < 30) & # Very oversold (was 45) (dataframe['close'] <= dataframe['low_5'] * 1.005) & # Very near recent low (dataframe['volume'] > dataframe['volume_avg'] * 1.2) & # Volume surge (dataframe['price_change'] < -3) # Big price drop ) ), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ TAKE PROFITS FAST - this is critical for DCA success """ dataframe.loc[ ( # EXIT ON SMALL PROFITS - don't be greedy (dataframe['rsi'] > 55) & # Not even overbought (was 75) (dataframe['close'] > dataframe['ema_9']) # Above short-term trend ) | ( # EXIT ON ANY DECENT BOUNCE (dataframe['price_change'] > 2) & # Small bounce up (dataframe['rsi'] > 50) # Neutral RSI ), 'exit_long'] = 1 return dataframe def adjust_trade_position(self, trade, current_time, current_rate, current_profit, min_stake, max_stake, **kwargs): """ AGGRESSIVE DCA - this is where DCA bots make their money """ if current_profit >= -0.01: # Only add when down 3%+ (was 1%) return None # BIGGER DCA amounts - go heavy when losing if current_profit < -0.15: # Down 15%+ additional_stake = min_stake * 3.0 # Massive add (was 2.0) logger.info(f"🔥 MASSIVE DCA: Adding ${additional_stake:.2f} to {trade.pair} (down {current_profit:.1%})") elif current_profit < -0.10: # Down 10-15% additional_stake = min_stake * 2.5 # Big add (was 1.5) logger.info(f"💪 BIG DCA: Adding ${additional_stake:.2f} to {trade.pair} (down {current_profit:.1%})") elif current_profit < -0.06: # Down 6-10% additional_stake = min_stake * 2.0 # Medium add (was 1.0) logger.info(f"📈 MEDIUM DCA: Adding ${additional_stake:.2f} to {trade.pair} (down {current_profit:.1%})") elif current_profit < -0.03: # Down 3-6% additional_stake = min_stake * 1.0 # Small add (was 0.8) logger.info(f"💰 SMALL DCA: Adding ${additional_stake:.2f} to {trade.pair} (down {current_profit:.1%})") else: return None # Check if we can still add more positions if trade.nr_of_successful_entries >= (self.max_entry_position_adjustment + 1): logger.info(f"⚠️ Max DCA entries reached for {trade.pair}") return None return min(additional_stake, max_stake) def custom_stake_amount(self, pair: str, current_time, current_rate: float, proposed_stake: float, min_stake: float, max_stake: float, entry_tag: str, **kwargs) -> float: """ MUCH SMALLER initial stakes - save money for DCA """ # Use only 25% for initial entry (was 60%) - save 75% for DCA initial_stake = proposed_stake * 0.25 logger.info(f"💰 Small initial stake for {pair}: ${initial_stake:.2f} (25% - saving for DCA)") return max(initial_stake, min_stake) def custom_exit_price(self, pair: str, trade, current_time, proposed_rate: float, current_profit: float, **kwargs) -> float: """ SELL IMMEDIATELY when profitable - don't wait for better prices """ if current_profit > 0.02: # If up even 2%, sell at market (was 8%) logger.info(f"💰 TAKING PROFIT: {pair} up {current_profit:.1%} - selling at market") return proposed_rate # Take the market price immediately return proposed_rate def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time, entry_tag, **kwargs) -> bool: """ Log all entries for tracking """ trade_value = amount * rate logger.info(f"🎯 DCA BUY: {pair} | {amount:.4f} @ ${rate:.4f} = ${trade_value:.2f}") return True def confirm_trade_exit(self, pair: str, trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time, **kwargs) -> bool: """ Log all exits with profit tracking """ profit_pct = trade.calc_profit_ratio(rate) * 100 profit_usd = trade.calc_profit(rate) if profit_pct > 0: logger.info(f"💰 DCA PROFIT: {pair} | SOLD {amount:.4f} @ ${rate:.4f} | +{profit_pct:.2f}% (+${profit_usd:.2f})") else: logger.info(f"🛑 DCA LOSS: {pair} | SOLD {amount:.4f} @ ${rate:.4f} | {profit_pct:.2f}% (${profit_usd:.2f})") logger.info(f" Duration: {current_time - trade.open_date} | Reason: {exit_reason}") return True def leverage(self, pair: str, current_time, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str, side: str, **kwargs) -> float: """ Spot trading only """ return 1.0