# source: https://raw.githubusercontent.com/adevrent/freqtrade_custom_strats/af066de00c26843ab714531cdac47c073b1eaaa2/strats/pandaspivots.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter) # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib # This class is a sample. Feel free to customize it. class github_adevrent_freqtrade_custom_strats__pandaspivots__20221214_114053(IStrategy): """ This is a sample strategy to inspire you. 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_entry_trend, populate_exit_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 = 3 # Can this strategy go short? can_short: bool = True # ROI table: minimal_roi = { "15": 0.02, "30": 0.01, "60": 0.00 } # Stoploss: stoploss = -0.8 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.0025 trailing_stop_positive_offset = 0.01 trailing_only_offset_is_reached = True # Optimal timeframe for the strategy. timeframe = '1m' # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the config. use_exit_signal = False exit_profit_only = False ignore_roi_if_entry_signal = False # Hyperoptable parameters #buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True) #sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True) #short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True) #exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True) # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 120 # 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' } plot_config = { 'main_plot': { 'tema': {}, 'sar': {'color': 'white'}, }, 'subplots': { "MACD": { 'macd': {'color': 'blue'}, 'macdsignal': {'color': 'orange'}, }, "RSI": { 'rsi': {'color': 'red'}, } } } 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: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. :param dataframe: Dataframe with data from the exchange :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ pd.options.mode.chained_assignment = None # default='warn' ################################## Maxima / Minima Points of High / Low ##################################### pivot_range = int(10) # Minima code conditions1_minima = np.array([(dataframe["low"].shift(periods = pivot_range) < dataframe["low"].shift(periods = pivot_range + lb)) for lb in range(1, pivot_range + 1)]) conditions2_minima = np.array([(dataframe["low"].shift(periods = pivot_range) < dataframe["low"].shift(periods = pivot_range - lb)) for lb in range(1, pivot_range + 1)]) conditions_minima = conditions1_minima & conditions2_minima # 1st element is if condition is true compared to first candle before, # 2nd element is if condition is true compared to second candle before and so on ... conditions_minima_T = np.transpose(conditions_minima) # First element checks if 10 forward and 10 backward minimum conditions are true for 1st row, # Second element checks if 10 forward and 10 backward minimum conditions are true for 2nd row and so on ... check_minima = np.all(conditions_minima_T, axis = 1) # Test whether all array elements along a given axis evaluate to True. dataframe["check_minima"] = check_minima dataframe["check_minima"][dataframe["check_minima"] == False] = None dataframe["minima"] = dataframe["low"].shift(periods = pivot_range)[check_minima == True] # Maxima code conditions1_maxima = np.array([(dataframe["high"].shift(periods = pivot_range) > dataframe["high"].shift(periods = pivot_range + lb)) for lb in range(1, pivot_range + 1)]) conditions2_maxima = np.array([(dataframe["high"].shift(periods = pivot_range) > dataframe["high"].shift(periods = pivot_range - lb)) for lb in range(1, pivot_range + 1)]) conditions_maxima = conditions1_maxima & conditions2_maxima # 1st element is if condition is true compared to first candle before, # 2nd element is if condition is true compared to second candle before and so on ... conditions_maxima_T = np.transpose(conditions_maxima) # First element checks if 10 forward and 10 backward maximum conditions are true for 1st row, # Second element checks if 10 forward and 10 backward maximum conditions are true for 2nd row and so on ... check_maxima = np.all(conditions_maxima_T, axis = 1) # Test whether all array elements along a given axis evaluate to True. dataframe["check_maxima"] = check_maxima dataframe["check_maxima"][dataframe["check_maxima"] == False] = None dataframe["maxima"] = dataframe["high"].shift(periods = pivot_range)[check_maxima == True] dataframe["maxima"][0] = dataframe["high"][0] * 1.5 # an arbitrarily large value assigned to first row (to make .fillna() function work.) dataframe["minima"][0] = dataframe["low"][0] * 0.5 # an arbitrarily small value assigned to first row (to make .fillna() function work.) dataframe["maxima"] = dataframe["maxima"].fillna(method = "ffill") # Fill NaN with last value. dataframe["minima"] = dataframe["minima"].fillna(method = "ffill") # Fill NaN with last value. # Rolling max and min to support the pivot points: dataframe["rolling_max"] = dataframe["high"].rolling(pivot_range * 2).max().shift(periods = 1) dataframe["rolling_min"] = dataframe["low"].rolling(pivot_range * 2).min().shift(periods = 1) ########################################################################################################################## # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod = (pivot_range * 3)) dataframe["rsi_maxima"] = dataframe["rsi"].shift(periods = pivot_range)[check_maxima == True] # RSI of last maxima point. dataframe["rsi_minima"] = dataframe["rsi"].shift(periods = pivot_range)[check_minima == True] # RSI of last minima point. dataframe["rsi_maxima"][0] = 80 # an arbitrarily large value assigned to first row (to make .fillna() function work.) dataframe["rsi_minima"][0] = 20 # an arbitrarily small value assigned to first row (to make .fillna() function work.) dataframe["rsi_maxima"] = dataframe["rsi_maxima"].fillna(method = "ffill") # Fill NaN with last value. dataframe["rsi_minima"] = dataframe["rsi_minima"].fillna(method = "ffill") # Fill NaN with last value. # # SMA - Simple Moving Average dataframe['sma_fast'] = ta.SMA(dataframe, timeperiod=20) dataframe['sma_slow'] = ta.SMA(dataframe, timeperiod=60) # 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] """ return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the entry signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with entry columns populated """ dataframe.loc[ ( # Signal: (dataframe["sma_fast"] > dataframe["sma_slow"]) & # Uptrend. ((dataframe["low"] < dataframe["minima"])) & # Candle low lower than minima. #(dataframe["low"] < dataframe['rolling_min']) & # Candle low is lower than rolling_min. (dataframe["rsi"] > (dataframe["rsi_minima"])) & # RSI divergence (dataframe["rsi_minima"] < 30) & # RSI of minima point is lower than 30. (dataframe["close"] > dataframe["minima"]) & # But close is greater than minima. (wick down) (dataframe['volume'] > 0) # Make sure Volume is not 0 ), ['enter_long', 'enter_tag']] = (1, 'rsi_bull_div') dataframe.loc[ ( # Signal: (dataframe["sma_fast"] < dataframe["sma_slow"]) & # Downtrend. ((dataframe["high"] > dataframe["maxima"])) & # Candle high higher than maxima. #(dataframe["high"] > dataframe['rolling_max']) & # High is higher than rolling_max. (dataframe["rsi"] < (dataframe["rsi_maxima"])) & # RSI divergence (dataframe["rsi_maxima"] > 70) & # RSI of maxima point is greater than 70. (dataframe["close"] < dataframe["maxima"]) & # But close is less than maxima. (wick up)) (dataframe['volume'] > 0) # Make sure Volume is not 0 ), ['enter_short', 'enter_tag']] = (1, 'rsi_bear_div') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the exit signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with exit columns populated """ dataframe.loc[ ( # Signal: RSI crosses below 50 OR RSI crosses below rsi of minima point (invalidation). (dataframe["sma_fast"] < dataframe["sma_slow"]) & # Trend change. Exit. (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'exit_long'] = 1 dataframe.loc[ ( # Signal: RSI crosses below 50 OR RSI crosses above rsi of maxima point (invalidation). (dataframe["sma_fast"] > dataframe["sma_slow"]) & # Trend change. Exit. (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'exit_short'] = 1 return dataframe