# source: https://raw.githubusercontent.com/anwaylulu/trading-toolkit/4008c0fb85be968e13d6e96e7f46ab2f30e6a546/examples/freqtrade_strategy_template.py # directory_url: https://github.com/anwaylulu/trading-toolkit/blob/main/examples/ # User: anwaylulu # Repository: trading-toolkit # --------------------""" Freqtrade Strategy Template Complete template for creating trading strategies with Freqtrade """ from freqtrade.strategy import IStrategy, DecimalParameter, IntParameter from pandas import DataFrame import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from functools import reduce class Github_anwaylulu_trading_toolkit__freqtrade_strategy_template__20260407_055015(IStrategy): """ Example Trading Strategy with Multiple Indicators Strategy Logic: - Buy: When RSI is oversold (< 30) and MACD crosses above signal - Sell: When RSI is overbought (> 70) or profit target reached Usage: 1. Copy to freqtrade/user_data/strategies/ 2. Run: freqtrade backtesting --strategy Github_anwaylulu_trading_toolkit__freqtrade_strategy_template__20260407_055015 3. Optimize: freqtrade hyperopt --strategy Github_anwaylulu_trading_toolkit__freqtrade_strategy_template__20260407_055015 """ # Strategy version INTERFACE_VERSION = 3 # Parameters for hyperoptimization buy_rsi = IntParameter(10, 40, default=30, space="buy") sell_rsi = IntParameter(60, 90, default=70, space="sell") # Stoploss and ROI stoploss = -0.10 # 10% stop loss minimal_roi = { "0": 0.15, # 15% profit - sell immediately "60": 0.10, # 10% profit after 60 min "120": 0.05, # 5% profit after 120 min "240": 0.025 # 2.5% profit after 240 min } # Trailing stop trailing_stop = True trailing_stop_positive = 0.02 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True # Timeframe timeframe = '1h' # Startup candle count startup_candle_count = 100 # Order types order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } # Order time in force order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Add technical indicators to the dataframe """ # RSI (Relative Strength Index) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # Bollinger Bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] # Moving Averages dataframe['sma_50'] = ta.SMA(dataframe, timeperiod=50) dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200) dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12) dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26) # Volume dataframe['volume_mean'] = dataframe['volume'].rolling(window=20).mean() return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Define buy signals """ conditions = [] # Condition 1: RSI oversold conditions.append(dataframe['rsi'] < self.buy_rsi.value) # Condition 2: MACD crossing above signal conditions.append(dataframe['macd'] > dataframe['macdsignal']) # Condition 3: Price near lower Bollinger Band conditions.append(dataframe['close'] < dataframe['bb_lowerband'] * 1.02) # Condition 4: Volume above average conditions.append(dataframe['volume'] > dataframe['volume_mean']) # Combine all conditions if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Define sell signals """ conditions = [] # Condition 1: RSI overbought conditions.append(dataframe['rsi'] > self.sell_rsi.value) # Condition 2: MACD crossing below signal conditions.append(dataframe['macd'] < dataframe['macdsignal']) # Condition 3: Price near upper Bollinger Band conditions.append(dataframe['close'] > dataframe['bb_upperband'] * 0.98) # Combine all conditions if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell' ] = 1 return dataframe def leverage(self, pair: str, current_time, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str, side: str, **kwargs) -> float: """ Adjust leverage (for futures trading) """ return 1.0 # No leverage for spot trading class Github_anwaylulu_trading_toolkit__freqtrade_strategy_template__20260407_055015(IStrategy): """ Simple Moving Average Crossover Strategy Buy: Fast MA crosses above Slow MA Sell: Fast MA crosses below Slow MA """ INTERFACE_VERSION = 3 stoploss = -0.10 timeframe = '4h' minimal_roi = { "0": 0.20 } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['fast_ma'] = ta.SMA(dataframe, timeperiod=10) dataframe['slow_ma'] = ta.SMA(dataframe, timeperiod=30) # Crossover signals dataframe['cross_above'] = ( (dataframe['fast_ma'] > dataframe['slow_ma']) & (dataframe['fast_ma'].shift(1) <= dataframe['slow_ma'].shift(1)) ) dataframe['cross_below'] = ( (dataframe['fast_ma'] < dataframe['slow_ma']) & (dataframe['fast_ma'].shift(1) >= dataframe['slow_ma'].shift(1)) ) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[dataframe['cross_above'], 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[dataframe['cross_below'], 'sell'] = 1 return dataframe class Github_anwaylulu_trading_toolkit__freqtrade_strategy_template__20260407_055015(IStrategy): """ Breakout Strategy - Buy when price breaks above resistance """ INTERFACE_VERSION = 3 stoploss = -0.05 timeframe = '1d' minimal_roi = { "0": 0.10, "1440": 0.05 } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Calculate support and resistance dataframe['resistance'] = dataframe['high'].rolling(window=20).max() dataframe['support'] = dataframe['low'].rolling(window=20).min() # Volume confirmation dataframe['volume_ma'] = dataframe['volume'].rolling(window=20).mean() return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Breakout above resistance with volume dataframe.loc[ (dataframe['close'] > dataframe['resistance'].shift(1)) & (dataframe['volume'] > dataframe['volume_ma'] * 1.5), 'buy' ] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Breakdown below support dataframe.loc[ dataframe['close'] < dataframe['support'].shift(1), 'sell' ] = 1 return dataframe