# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame, Series from freqtrade.strategy import IStrategy from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter from freqtrade.exchange import timeframe_to_prev_date from datetime import datetime from freqtrade.persistence import Trade from freqtrade.strategy import stoploss_from_open # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from technical.indicators import indicators #https://github.com/kanaxe/freqtrade_kanaxe class HmaOrn_github_kanaxe_20211115(IStrategy): """ This is a strategy template to get you started. More information in https://www.freqtrade.io/en/latest/strategy-customization/ You can: :return: a Dataframe with all mandatory indicators for the strategies - Rename the class name (Do not forget to update class_name) - Add any methods you want to build your strategy - Add any lib you need to build your strategy You must keep: - the lib in the section "Do not remove these libs" - the methods: populate_indicators, populate_buy_trend, populate_sell_trend You should keep: - timeframe, minimal_roi, stoploss, trailing_* """ # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 2 # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". # ROI table: # ROI table: minimal_roi = { "0": 0.03148, "62": 0.02599, "121": 0.01904, "135": 0 } # Stoploss: stoploss = -0.08 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.00249 trailing_stop_positive_offset = 0.01195 trailing_only_offset_is_reached = True # Optimal timeframe for the strategy. timeframe = '5m' # Run "populate_indicators()" only for new candle. process_only_new_candles = False # These values can be overridden in the "ask_strategy" section in the config. use_sell_signal = True sell_profit_only = True ignore_roi_if_buy_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: False # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } # Optional order time in force. order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } plot_config = { "main_plot": { }, "subplots": { "RSI": { "rsi": { "color": "red" } }, "hlow": { "h-low": { "color": "#2de7cc", "type": "line" } }, "htsine": { "htsine": { "color": "#688669", "type": "line" } } } } # # Hyperoptable parameters # buy_rsi = IntParameter(low=1, high=30, default=30, space='buy', optimize=True, load=True) # sell_rsi = IntParameter(low=35, high=100, default=40, space='sell', optimize=True, load=True) use_custom_sell = False use_custom_stoploss = False 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"), ] """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['adx'] = ta.ADX(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['mfi'] = ta.MFI(dataframe) stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] stoch_rfast = ta.STOCHRSI(dataframe, timeperiod=7) dataframe['rfastd'] = stoch_rfast['fastd'] dataframe['rfastk'] = stoch_rfast['fastk'] dataframe['plus_di'] = ta.PLUS_DI(dataframe, timeperiod=5) dataframe['minus_di'] = ta.MINUS_DI(dataframe) # Bollinger bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['sar'] = ta.SAR(dataframe) dataframe['ohlc4']=(dataframe['open'] + dataframe['high'] + dataframe['low'] + dataframe['close']) / 4 dataframe['hlc3']=(dataframe['high'] + dataframe['low'] + dataframe['close']) / 3 dataframe['hl2']=(dataframe['high'] + dataframe['low'] ) / 2 dataframe['ho']=dataframe['high'] / dataframe['open'] dataframe['hl']=dataframe['high'] / dataframe['low'] dataframe['ol']=dataframe['open'] / dataframe['low'] dataframe['oc']=dataframe['open'] / dataframe['close'] dataframe['hma19'] = qtpylib.hma(dataframe['close'], 19) dataframe['hma8'] = qtpylib.hma(dataframe['hl2'], 8) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7) dataframe['cci'] = (ta.CCI(dataframe, timeperiod=7)/2) dataframe['sma90'] = ta.SMA(dataframe, timeperiod=90) dataframe['tema80'] = ta.TEMA(dataframe, timeperiod=80) dataframe['s_t9080'] = dataframe['sma90'] / dataframe['tema80'] return dataframe # def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, # current_rate: float, current_profit: float, **kwargs) -> float: # # evaluate highest to lowest, so that highest possible stop is used # if current_profit > 0.021: # return stoploss_from_open(0.02, current_profit) # elif current_profit > 0.011: # return stoploss_from_open(0.01, current_profit) # elif current_profit > 0.003: # return stoploss_from_open(0.002, current_profit) # # return maximum stoploss value, keeping current stoploss price unchanged # return 1 # def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, # current_profit: float, **kwargs): # dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # last_candle = dataframe.iloc[-1].squeeze() # candlem = dataframe.iloc[0].squeeze() # # Above 20% profit, sell when rsi < 80 # # if current_profit > 0.2: # # if last_candle['rsi'] < 60: # # return 'rsi_below_60' # # Between 2% and 10%, sell if EMA-long above EMA-short # # if candlem['close'] < candlem['open']: # if current_profit > 0.031: # return 1 # elif current_profit > 0.021: # return 1 # elif current_profit > 0.015: # return 1 # elif current_profit > 0.011: # return 1 # elif current_profit > 0.002: # return 1 # # if candlem['hma8'] < candlem['hma16']: # # if current_profit < 0.001: # # return 1 def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy column """ dataframe.loc[ ( # (dataframe['ol'] > 1.016)& # (dataframe['mfi'] < 22)& # (dataframe['fastd'] < 35)& # (dataframe['adx'] > 27)& (dataframe['rsi'] < 40)& # (qtpylib.crossed_above( # dataframe['close'], dataframe['sar'] # )) (dataframe['s_t9080'] > 1.02) & (qtpylib.crossed_above( dataframe['tema80'], dataframe['tema80'].shift(1) )) ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe :param dataframe: DataFrame populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy column """ dataframe.loc[ ( (dataframe['fastd'] > 98) ), 'sell'] =1 return dataframe