# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-michael-k8s-namespace/lambo_testing.py import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from sqlalchemy.orm.base import RELATED_OBJECT_OK from sqlalchemy.sql.elements import or_ import talib.abstract as ta import pandas_ta as pta import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.exchange import timeframe_to_minutes from technical import indicators import logging from freqtrade.strategy import BooleanParameter, CategoricalParameter, DecimalParameter, RealParameter, IStrategy, IntParameter, merge_informative_pair class Github_DerSalvador_freqtrade_helm_chart__lambo_testing__20260115_122204(IStrategy): # Add some logging logger = logging.getLogger(__name__) # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 3 # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". # ROI table: minimal_roi = {'0': 0.0625, '28': 0.05, '76': 0.04, '125': 0.03, '240': 0.02, '360': 0} # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". # use_custom_stoploss = True stoploss = -0.0625 #-0.10 # Trailing stop: trailing_stop = False trailing_stop_positive = 0.0069 trailing_stop_positive_offset = 0.038 trailing_only_offset_is_reached = False process_only_new_candles = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 20 #30 # Optimal timeframe for the strategy. timeframe = '5m' # These values can be overridden in the "ask_strategy" section in the config. use_exit_signal = True exit_profit_only = True # exit_profit_offset = 0.019 ignore_roi_if_entry_signal = False # hyperopt params entry_williams_low = DecimalParameter(-100, -50, default=-58.201) entry_williams_high = DecimalParameter(-100, -50, default=-92.098) entry_rsi_low = DecimalParameter(0, 30, default=2.342) entry_rsi_high = DecimalParameter(30, 60, default=51.22) entry_volume = DecimalParameter(1000, 100000, default=5000) exit_rsi = DecimalParameter(60, 100, default=60.631) exit_williams = DecimalParameter(-40, -10, default=-19.308) exit_volume = DecimalParameter(1000, 100000, default=1000) # Optional order type mapping. order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False} # Optional order time in force. order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'} @property def plot_config(self): return {'main_plot': {'bb.lower': {'color': '#9c6edc', 'type': 'line'}, 'bb.upper': {'color': '#9c6edc', 'type': 'line'}, 'vwma': {'color': '#4f9f02', 'type': 'line'}}, 'subplots': {'obv': {'OBV': {'color': '#1b61ab', 'type': 'line'}, 'OBVSlope': {'color': '#f18b7a', 'type': 'line'}}, 'vpci': {'vpci': {'color': '#d59a7a', 'type': 'line'}}, 'macd': {'macd': {'color': '#1c3d6a', 'type': 'line'}, 'macdsignal': {'color': '#873480', 'type': 'line'}, 'macdhist': {'color': '#478a87', 'type': 'bar'}}, 'wiliams': {'williamspercent': {'color': '#10f551', 'type': 'line'}}, 'stoch + rsi': {'rsi': {'color': '#d7affd', 'type': 'line'}, 'slowd': {'color': '#d7cc5c', 'type': 'line'}, 'fastk': {'color': '#186f86', 'type': 'line'}}, 'adx': {'adx': {'color': '#c392cd', 'type': 'line'}, 'plus.di': {'color': '#bcd6c5', 'type': 'line'}, 'minus.di': {'color': '#eb044c', 'type': 'line'}}}} def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.timeframe) for pair in pairs] return informative_pairs def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Retrieve best bid and best ask from the orderbook # ------------------------------------ # first check if dataprovider is available if self.dp: if self.dp.runmode.value in ('live', 'dry_run'): ob = self.dp.orderbook(metadata['pair'], 1) dataframe['best_bid'] = ob['bids'][0][0] dataframe['best_ask'] = ob['asks'][0][0] # Bollinger! bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb.lower'] = bollinger['lower'] dataframe['bb.middle'] = bollinger['mid'] dataframe['bb.upper'] = bollinger['upper'] # Added PCB Style OBV dataframe['OBV'] = ta.OBV(dataframe) dataframe['OBVSlope'] = pta.momentum.slope(dataframe['OBV']) # VWMA # vwma_period = 13 # dataframe['vwma'] = ((dataframe["close"] * dataframe["volume"]).rolling(vwma_period).sum() / # dataframe['volume'].rolling(vwma_period).sum()) # VWAP # vwap_period = 20 # dataframe['vwap'] = qtpylib.rolling_vwap(dataframe, window=vwap_period) # VPCI dataframe['vpci'] = indicators.vpci(dataframe, period_long=14) #williamsR dataframe['williamspercent'] = indicators.williams_percent(dataframe) # ADX dataframe['adx'] = ta.ADX(dataframe) dataframe['plus.di'] = ta.PLUS_DI(dataframe) dataframe['minus.di'] = ta.MINUS_DI(dataframe) # RSI dataframe['rsi'] = ta.RSI(dataframe) # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # Stochastic Fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # Stochastic Slow stoch_slow = ta.STOCH(dataframe) dataframe['slowd'] = stoch_slow['slowd'] dataframe['slowk'] = stoch_slow['slowd'] return dataframe def do_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Bollinger! bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb.lower'] = bollinger['lower'] dataframe['bb.middle'] = bollinger['mid'] dataframe['bb.upper'] = bollinger['upper'] # Added PCB Style OBV dataframe['OBV'] = ta.OBV(dataframe) dataframe['OBVSlope'] = pta.momentum.slope(dataframe['OBV']) # VWMA # vwma_period = 13 # dataframe['vwma'] = ((dataframe["close"] * dataframe["volume"]).rolling(vwma_period).sum() / # dataframe['volume'].rolling(vwma_period).sum()) # VPCI dataframe['vpci'] = indicators.vpci(dataframe, period_long=14) #williamsR dataframe['williamspercent'] = indicators.williams_percent(dataframe) # ADX dataframe['adx'] = ta.ADX(dataframe) dataframe['plus.di'] = ta.PLUS_DI(dataframe) dataframe['minus.di'] = ta.MINUS_DI(dataframe) # RSI dataframe['rsi'] = ta.RSI(dataframe) # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # Stochastic Fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # Stochastic Slow stoch_slow = ta.STOCH(dataframe) dataframe['slowd'] = stoch_slow['slowd'] dataframe['slowk'] = stoch_slow['slowd'] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[(dataframe['volume'] > self.entry_volume.value) & (dataframe['OBVSlope'] > 0) & (dataframe['williamspercent'] <= self.entry_williams_low.value) & (dataframe['williamspercent'] > self.entry_williams_high.value) & (dataframe['rsi'] > self.entry_rsi_low.value) & (dataframe['rsi'] <= self.entry_rsi_high.value) & qtpylib.crossed_above(dataframe['fastk'], dataframe['slowd']) & (dataframe['close'] < dataframe['bb.middle']), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[(dataframe['volume'] > self.exit_volume.value) & (dataframe['williamspercent'] > self.exit_williams.value) & (dataframe['rsi'] > self.exit_rsi.value) | qtpylib.crossed_below(dataframe['plus.di'], dataframe['minus.di']) | qtpylib.crossed_below(dataframe['macd'], dataframe['macdsignal']) | qtpylib.crossed_below(dataframe['fastk'], dataframe['slowd']) & (dataframe['close'] > dataframe['bb.upper']) | (dataframe['vpci'] >= dataframe['bb.upper']), 'exit_long'] = 1 return dataframe # "All watched over by machines with loving grace..."