# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/bot-mssm-07-k8s-namespace/extra_strategies/AdxSmasS_v7_Long.py kubectl --context=gke_vaulted-gift-406223_europe-west1-b_private-cluster-3 -n bot-mssm-07 exec -it pod/freqtrade-bot-mssm-07-5f8db6bb7-hmcvw -c freqtrade -- cat /extra_strategies/Github_DerSalvador_freqtrade_helm_chart__AdxSmasS_v7_Long__20260115_122204.py from freqtrade.strategy import IStrategy from pandas import DataFrame import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.strategy.informative_decorator import informative # --- Do not remove these libs --- from freqtrade.strategy import IStrategy from pandas import DataFrame from freqtrade.persistence import Trade from datetime import datetime, timedelta from typing import Optional class Github_DerSalvador_freqtrade_helm_chart__AdxSmasS_v7_Long__20260115_122204(IStrategy): INTERFACE_VERSION = 3 minimal_roi = {"0": 0.10} # Targeting a 10% ROI # stoploss = -0.10 # Adjust stoploss for long futures trades timeframe = '5m' informative_timeframe = '15m' # New informative timeframe def informative_pairs(self): # return [("BTC/USDT", self.informative_timeframe)] pairs = [ ("BTC/USDT", self.informative_timeframe), # Bitcoin - Mainstream and highly liquid ("ETH/USDT", self.informative_timeframe), # Ethereum - Popular for both trading and smart contracts ("XRP/USDT", self.informative_timeframe), # Ripple - Known for its fast transactions ("LTC/USDT", self.informative_timeframe), # Litecoin - Often considered the silver to Bitcoin's gold ("BCH/USDT", self.informative_timeframe), # Bitcoin Cash - A Bitcoin fork with different technical decisions # ("ADA/USDT", self.informative_timeframe), # Cardano - Popular for its proof-of-stake mechanism ("DOT/USDT", self.informative_timeframe), # Polkadot - Known for enabling cross-blockchain transfers ("LINK/USDT", self.informative_timeframe), # Chainlink - Widely used for integrating real-world data into blockchains # ("BNB/USDT", self.informative_timeframe), # Binance Coin - Utility token of the Binance exchange ("SOL/USDT", self.informative_timeframe) # Solana - Known for its high throughput and fast transactions ] return pairs def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Main Pair Indicators dataframe['adx'] = ta.ADX(dataframe, timeperiod=14) dataframe['short'] = ta.SMA(dataframe, timeperiod=3) dataframe['long'] = ta.SMA(dataframe, timeperiod=6) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) macd, macdsignal, macdhist = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9) dataframe['macd'] = macd dataframe['macdsignal'] = macdsignal dataframe['macdhist'] = macdhist upperband, middleband, lowerband = ta.BBANDS(dataframe['close'], timeperiod=20, nbdevup=2.0, nbdevdn=2.0, matype=0) dataframe['upperband'] = upperband dataframe['middleband'] = middleband dataframe['lowerband'] = lowerband # Informative Indicators for Multiple Pairs informative_pairs = self.informative_pairs() # Retrieve list of informative pairs for pair, timeframe in informative_pairs: informative = self.dp.get_pair_dataframe(pair=pair, timeframe=timeframe) # Calculate SMA for each informative pair informative[f'{pair}_inf_sma'] = ta.SMA(informative, timeperiod=6) # Ensure correct alignment of timestamps when merging informative.set_index('date', inplace=True) # Merge each informative indicator back to the main dataframe dataframe = dataframe.join(informative[[f'{pair}_inf_sma']], how='left', rsuffix='_inf') return dataframe # def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # # Main Pair Indicators # dataframe['adx'] = ta.ADX(dataframe, timeperiod=14) # dataframe['short'] = ta.SMA(dataframe, timeperiod=3) # dataframe['long'] = ta.SMA(dataframe, timeperiod=6) # # Informative Pair Indicators # informative = self.dp.get_pair_dataframe(pair="BTC/USDT", timeframe=self.informative_timeframe) # informative['inf_long'] = ta.SMA(informative, timeperiod=6) # # Merge informative indicators back to main dataframe # dataframe = dataframe.join(informative[['inf_long']], on='date', rsuffix='_inf') # return dataframe # def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # # Existing indicators # dataframe['adx'] = ta.ADX(dataframe, timeperiod=14) # dataframe['short'] = ta.SMA(dataframe, timeperiod=3) # dataframe['long'] = ta.SMA(dataframe, timeperiod=6) # # Additional Indicators # dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) # macd, macdsignal, macdhist = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9) # dataframe['macd'] = macd # dataframe['macdsignal'] = macdsignal # dataframe['macdhist'] = macdhist # upperband, middleband, lowerband = ta.BBANDS(dataframe['close'], timeperiod=20, nbdevup=2, nbdevdn=2, matype=0) # dataframe['upperband'] = upperband # dataframe['middleband'] = middleband # dataframe['lowerband'] = lowerband # # Informative Pair Indicators # informative = self.dp.get_pair_dataframe(pair="BTC/USDT", timeframe=self.informative_timeframe) # informative['inf_long'] = ta.SMA(informative, timeperiod=6) # # Merge informative indicators back to main dataframe # dataframe = dataframe.join(informative[['inf_long']], on='date', rsuffix='_inf') # return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Long Entry Conditions with cross-market analysis # Entry when the primary pair shows stronger momentum compared to informative pairs for pair, timeframe in self.informative_pairs(): pair_sma_key = f'{pair}_inf_sma' # Fetch the data for the pair informative = self.dp.get_pair_dataframe(pair=pair, timeframe=timeframe) # Calculate indicators, e.g., SMAs informative['long_sma'] = ta.SMA(informative, timeperiod=50) informative['short_sma'] = ta.SMA(informative, timeperiod=20) # Join informative indicators back to the main dataframe # Note: Ensure your join keys ('date') are aligned and data is available at the same timestamps dataframe = dataframe.join(informative[['long_sma', 'short_sma']], on='date', rsuffix=f'_{pair}') # Check if the SMA of the primary pair is greater than the SMA of each informative pair dataframe.loc[ (dataframe['adx'] > 40) & # (dataframe['rsi'] > 50) & # RSI should indicate strength but not overbought (dataframe['macd'] > dataframe['macdsignal']) & # Bullish MACD crossover (dataframe['long'] > dataframe['short']) & (dataframe['long'] > dataframe[pair_sma_key]), 'enter_long'] = 1 dataframe['enter_short'] = 0 # Assuming no short entry handling as per strategy focus return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: return 4 def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Define your informative pairs and timeframes # informative_pairs = [ # ("BTC/USDT", "5m"), # ("ETH/USDT", "5m"), # # Add more pairs as needed # ] # Process each informative pair for pair, timeframe in self.informative_pairs(): # Fetch the data for the pair informative = self.dp.get_pair_dataframe(pair=pair, timeframe=timeframe) # Calculate indicators, e.g., SMAs informative['long_sma'] = ta.SMA(informative, timeperiod=50) informative['short_sma'] = ta.SMA(informative, timeperiod=20) # Join informative indicators back to the main dataframe # Note: Ensure your join keys ('date') are aligned and data is available at the same timestamps dataframe = dataframe.join(informative[['long_sma', 'short_sma']], on='date', rsuffix=f'_{pair}') # Entry condition: Check SMA conditions across all informative pairs entry_condition = (dataframe['long'] > dataframe['short']) # Base condition for the primary pair for pair, _ in informative_pairs: entry_condition &= (dataframe[f'long_sma_{pair}'] > dataframe[f'short_sma_{pair}']) # Apply the entry condition to the dataframe dataframe.loc[entry_condition, 'enter_long'] = 1 dataframe['enter_short'] = 0 # Assuming no short entry handling return dataframe # def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # # Enhanced Long Entry Conditions # dataframe.loc[ # (dataframe['adx'] > 25) & # (dataframe['rsi'] > 50) & # RSI should indicate not oversold # (dataframe['macd'] > dataframe['macdsignal']) & # Bullish MACD crossover # (dataframe['close'] > dataframe['middleband']) & # Price above middle Bollinger Band # (dataframe['long'] > dataframe['short']) & # (dataframe['long'] > dataframe['inf_long']), 'enter_long'] = 1 # dataframe['enter_short'] = 0 # Assuming no short entry handling # return dataframe # def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # # Long Entry Condition: Simple cross above condition with informative pair # dataframe.loc[(dataframe['adx'] > 25) & (dataframe['long'] > dataframe['short']) & (dataframe['long'] > dataframe['inf_long']), 'enter_long'] = 1 # dataframe.loc[(dataframe['adx'] > 25) & (dataframe['long'] > dataframe['short']) & (dataframe['long'] > dataframe['inf_long']), 'enter_short'] = 0 # return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Long Exit Conditions # Exit when RSI indicates overbought conditions, suggesting a potential reversal dataframe.loc[ (dataframe['rsi'] > 70), 'exit_long'] = 1 # Exit when the MACD line crosses below the signal line, indicating decreasing momentum dataframe.loc[ (dataframe['macd'] < dataframe['macdsignal']), 'exit_long'] = 1 # Exit when the price crosses below the lower Bollinger Band, indicating a potential sharp decline dataframe.loc[ (dataframe['close'] < dataframe['lowerband']), 'exit_long'] = 1 # Additional exit condition based on ADX and moving averages, as previously used # Exit if ADX falls below a certain threshold (e.g., 25), indicating a loss of trend strength dataframe.loc[ (dataframe['adx'] < 25) | (dataframe['long'] < dataframe['short']), 'exit_long'] = 1 # Remove any exit short logic if the strategy is only for long positions dataframe['exit_short'] = 0 # This ensures no shorts are exited as this strategy is designed for longs only return dataframe # def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # # Long Exit Condition: Opposite of entry or specific profit target/stoploss hit # dataframe.loc[(dataframe['adx'] < 25) | (dataframe['long'] < dataframe['short']), 'exit_long'] = 1 # dataframe.loc[(dataframe['adx'] < 25) | (dataframe['long'] < dataframe['short']), 'exit_short'] = 0 # dataframe.loc[(), ['exit_short', 'exit_tag']] = (0, 'no_short_exit') # return dataframe