# source: https://raw.githubusercontent.com/hadif1999/freqtrade_strategies/ff6e476502f195717117adf5c1eb474a50363624/PivotStrategy.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 import pandas as pd from pandas import DataFrame from datetime import datetime import datetime as dt from typing import Optional, Union from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, IStrategy, merge_informative_pair, informative, stoploss_from_absolute, stoploss_from_open) from freqtrade.exchange import timeframe_to_seconds from typing import Literal # -------------------------------- # Add your lib to import here import talib.abstract as ta import pandas_ta as pta from technical import qtpylib from freqtrade.persistence import Trade class Github_hadif1999_freqtrade_strategies__PivotStrategy__20240213_103648(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_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 = 1 # Optimal timeframe for the strategy. timeframe = '15m' # Can this strategy go short? can_short: bool = False # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { "120": 0.02, "60": 0.04, # anytime after 60min reaches 1% profit exits position "30": 0.06, # anytime after 30min reaches 2% profit exits position "0": 0.08 # anytime reaches 4% profit exits position } # STOPLOSS # src: https://www.freqtrade.io/en/stable/stoploss/#trailing-stop-loss-custom-positive-loss # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.05 # Trailing stoploss # trailing_stop = True # trailing_only_offset_is_reached = True # keep inital stoploss untill reaches to offset # trailing_stop_positive = 0.02 # stoploss after reaching to offset # trailing_stop_positive_offset = 0.03 # Disabled / not configured # https://www.freqtrade.io/en/stable/strategy-callbacks use_custom_stoploss = True # Run "populate_indicators()" only for new candle. process_only_new_candles = False # These values can be overridden in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False ignore_buying_expired_candle_after = timeframe_to_seconds(timeframe)//2 # in seconds # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 2 # Strategy parameters # buy_rsi = IntParameter(10, 40, default=30, space="buy") # sell_rsi = IntParameter(60, 90, default=70, space="sell") PivotType:Literal["1w", '1M'] = "1w" pair_data = {} IGNORE_MEAN:bool = False TIMEOUT = dt.timedelta(days=4) MIN_TIME = dt.timedelta(hours=3) MIN_C2_SIZE:float|None = 100 MIN_C1_SIZE:float|None = None IS_C1C2_tolerance:float = 0.00001 # 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 indicators (Moving averages, ...) 'main_plot': { 'tema': {}, 'sar': {'color': 'white'}, }, 'subplots': { # Subplots - each dict defines one additional plot "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 [] @informative(PivotType) def populate_indicators_1w(self, dataframe:pd.DataFrame, metadata:dict): # df as dataframe containing pivot dataframe["mean"] = dataframe[["low", "high"]].mean(axis=1) dataframe["25%"] = dataframe[["low", "mean"]].mean(axis=1) dataframe["75%"] = dataframe[["mean", "high"]].mean(axis=1) return dataframe 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 """ symbol = metadata["pair"] last_close = dataframe["close"].iloc[-1] # macd = ta.MACD(dataframe) # dataframe['macd'] = macd['macd'] # dataframe['macdsignal'] = macd['macdsignal'] # dataframe['macdhist'] = macd['macdhist'] if "enter_long" not in dataframe.columns: dataframe["enter_long"] = 0 if "exit_long" not in dataframe.columns: dataframe["exit_long"] = 0 if symbol not in self.pair_data: self.pair_data[symbol] = {"Pivots":{}, "init_LONG_SL": None, "init_LONG_TP": None, "init_SHORT_SL": None, "init_SHORT_TP": None} pivots = dataframe[[f"low_{self.PivotType}", f"25%_{self.PivotType}", f"mean_{self.PivotType}", f"75%_{self.PivotType}", f"high_{self.PivotType}"]].iloc[-1].to_dict() #print(f"\n{last_close = }| {symbol = }| {pivots = }\n") 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 """ last_close = dataframe["close"].iloc[-1] symbol = metadata["pair"] # getting last pivot pivots = dataframe[[f"low_{self.PivotType}", f"25%_{self.PivotType}", f"mean_{self.PivotType}", f"75%_{self.PivotType}", f"high_{self.PivotType}"]].iloc[-1].to_dict() self.pair_data[symbol]["Pivots"] = pivots # saving current pivots to pair data # finding all C1, C2 candles according to place of given price relative to pivots # sorted by C2 date ([-1] is the newest) C1C2s = FindAllC1C2_Candles_FromPrice(Price=last_close, Pivots_dict = pivots, df = dataframe, ignore_mean=False, timeout=self.TIMEOUT, min_time=self.MIN_TIME, min_C2_Size=self.MIN_C2_SIZE, min_C1_size=self.MIN_C1_SIZE, C1Type="bearish", C2Type="bullish") if C1C2s == []: return dataframe last_C1, last_C2 = C1C2s[-1]["C1"], C1C2s[-1]["C2"] C1_close, C2_close = last_C1["close"], last_C2["close"] if isEqual_with_tolerance_pct(last_close, C2_close, self.IS_C1C2_tolerance): long_log = f"**LONG sig detected, {symbol = }, {C1_close = }|{C2_close = }" dataframe.loc[dataframe["close"] == C2_close, ['enter_long', 'enter_tag'] ] = (1, long_log) #print(long_log) self.pair_data[symbol]["init_LONG_SL"] = last_C2["low"] self.pair_data[symbol]["init_LONG_TP"] = pivots[f"mean_{self.PivotType}"] self.pair_data[symbol]["init_SHORT_SL"] = None self.pair_data[symbol]["init_SHORT_TP"] = None 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 """ last_close = dataframe["close"].iloc[-1] symbol = metadata["pair"] pivots = dataframe[[f"low_{self.PivotType}", f"25%_{self.PivotType}", f"mean_{self.PivotType}", f"75%_{self.PivotType}", f"high_{self.PivotType}"]].iloc[-1].to_dict() self.pair_data[symbol]["Pivots"] = pivots C1C2s = FindAllC1C2_Candles_FromPrice(Price=last_close, Pivots_dict = pivots, df = dataframe, ignore_mean=False, timeout=self.TIMEOUT, min_time=self.MIN_TIME, min_C2_Size=self.MIN_C2_SIZE, min_C1_size=self.MIN_C1_SIZE, C1Type="bullish", C2Type="bearish") if C1C2s == []: return dataframe last_C1, last_C2 = C1C2s[-1]["C1"], C1C2s[-1]["C2"] C1_close, C2_close = last_C1["close"], last_C2["close"] if isEqual_with_tolerance_pct(last_close, C2_close, self.IS_C1C2_tolerance): short_log = f"**SHORT sig detected, {symbol = }, {C1_close = }|{C2_close = }" dataframe.loc[dataframe["close"] == C2_close, ['exit_long', 'exit_tag'] ] = (1, short_log) #print(short_log) self.pair_data[symbol]["init_LONG_SL"] = None self.pair_data[symbol]["init_LONG_TP"] = None self.pair_data[symbol]["init_SHORT_SL"] = None ### last_C2["high"] self.pair_data[symbol]["init_SHORT_TP"] = None # pivots[f"mean_{self.PivotType}"] return dataframe def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, after_fill: bool, **kwargs) -> Optional[float]: init_SL = self.pair_data[pair]["init_LONG_SL"] or abs(self.stoploss) init_SL_pct = stoploss_from_absolute(init_SL, current_rate, trade.is_short, trade.leverage) pivots = self.pair_data[pair]["Pivots"] nearPivots_open = _find_nearest_pivots(trade.open_rate, pivots) nearPivots_current = _find_nearest_pivots(current_rate, pivots) if nearPivots_open.keys() == nearPivots_current.keys(): return min(init_SL_pct, abs(self.stoploss)) else: minPivot_near = min(nearPivots_current.values()) return stoploss_from_absolute(minPivot_near, current_rate, trade.is_short, trade.leverage) def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: self.pair_data[pair]["init_LONG_SL"] = None self.pair_data[pair]["init_LONG_TP"] = None return True @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": 2 }, { "method": "MaxDrawdown", "lookback_period_candles": 50, "trade_limit": 10, "stop_duration_candles": 10, "max_allowed_drawdown": 0.2 }, { "method": "StoplossGuard", "lookback_period_candles": 50, "trade_limit": 4, "stop_duration_candles": 5, "only_per_pair": True }, { "method": "LowProfitPairs", "lookback_period_candles": 100, "trade_limit": 8, "stop_duration_candles": 2, "required_profit": 0.01 } ] ########################################### dataTypes ################### CandleType = Literal["bullish", "bearish"] SideType = Literal["LONG", "SHORT"] C1C2Type = Literal["C1", "C2"] ######################################### utils functions ##################### from typing import Literal # check if last pivots are long position pivots or not def _findSide_fromPivots(close_price:float, pivots_dict: dict[str,float]) -> Literal['SHORT', 'LONG']: """find position side from input price according to it's nearest pivots Raises: ValueError: _description_ Returns: "LONG" or "SHORT" """ return "SHORT" if close_price >= pivots_dict[f"mean_{Github_hadif1999_freqtrade_strategies__PivotStrategy__20240213_103648.PivotType}"] else "LONG" def getBulish_CrossedPrice(df: pd.DataFrame, Price:float) -> pd.DataFrame: cond = (df["close"] > Price) & (df["open"] < Price) return df.loc[cond, :] def getBearish_CrossedPrice(df: pd.DataFrame, Price: float) -> pd.DataFrame: cond = (df["close"] < Price) & (df["open"] > Price) return df.loc[cond, :] def _find_nearest_pivots(price:float, pivots_dict: dict[str, float]): """find one/two nearest pivots to a price Args: price (float): price Returns: tuple[float, float]| None, float | float, None: pivots """ lower_pivots = {} upper_pivots = {} for name, val in pivots_dict.items(): if price >= val: lower_pivots[name] = val elif price < val: upper_pivots[name] = val upper_pivot = [min(upper_pivots.items(), key = lambda x:x[1])] if upper_pivots!={} else {} lower_pivot = [max(lower_pivots.items(), key = lambda x:x[1])] if lower_pivots!={} else {} return {**dict(lower_pivot), **dict(upper_pivot)} def _GetCrossedPivot_Candles(Price: float, *, pivots_dict:dict[str, float], df:pd.DataFrame, candle_type:CandleType = "bullish"): """finds last 'bullish'/'bearish' candle that crossed a pivot. Args: dataframe (pd.DataFrame, optional): finds candle on this df. Defaults to None. candle_type (str, optional): 'bullish' or 'bearish'. Defaults to "bullish". Raises: ValueError: _description_ Returns: candle_indice, crossed pivot """ df_ = df.copy() pivots_dict = _find_nearest_pivots(price = Price, pivots_dict = pivots_dict) match candle_type.lower(): case "bullish": return {pivot_name: getBulish_CrossedPrice(df_, pivot) for pivot_name, pivot in pivots_dict.items()} case "bearish": return {pivot_name: getBearish_CrossedPrice(df_, pivot) for pivot_name, pivot in pivots_dict.items()} case _ : raise ValueError("candle_type can be 'bullish' or 'bearish'") def _FindAllC1C2_Candles_FromPrice( Price: float, *, Pivots_dict:dict[str ,float], df: pd.DataFrame = pd.DataFrame(), C1_Type:CandleType = "bearish", C2_Type:CandleType = "bullish", ignore_mean: bool = False): assert C1_Type != C2_Type, "C1 and C2 candle type must be diffrent" C1_dict = _GetCrossedPivot_Candles(Price=Price, pivots_dict=Pivots_dict, df = df, candle_type = C1_Type) C2_dict = _GetCrossedPivot_Candles(Price=Price, pivots_dict=Pivots_dict, df = df, candle_type = C2_Type) assert C1_dict.keys() == C2_dict.keys(), "pivotLevels found must be same!" pivot_levelNames = C1_dict.keys() cols = ['open', 'high', 'low', 'close', 'volume', "date"] c1, c2 = "_C1", "_C2" C1_colNames, C2_colNames = [col+c1 for col in cols], [col+c2 for col in cols] # getting C1,C2 pair C1C2_candles = {} for levelName in pivot_levelNames: if ignore_mean and "mean" in levelName.lower(): continue C1_df, C2_df = C1_dict[levelName], C2_dict[levelName] # adding suffix to C1,C2 col names to identify them easily C1_df = add_to_ColumnNames(C1_df.copy(), suffix = c1) C2_df = add_to_ColumnNames(C2_df.copy(), suffix = c2) ## first concat to add indices C1C2_df = pd.concat([C1_df, C2_df], axis = 1) # shifting C1 part to align indexes and removing empty rows shifted_C1_df = C1C2_df[C1_colNames].shift(1) C1C2_df = pd.concat([shifted_C1_df, C2_df], axis = 1).dropna().copy() ## reverting column names to their initial names _C1_df, _C2_df = C1C2_df[C1_colNames], C1C2_df[C2_colNames] _C1_df = remove_from_ColumnNames(_C1_df, suffix = c1) _C2_df = remove_from_ColumnNames(_C2_df, suffix = c2) # making a list of dicts with C1 and C2 keys C1C2_candles[levelName] = [{"C1":C1[1].to_dict(), "C2":C2[1].to_dict()} for C1, C2 in zip(_C1_df.iterrows(), _C2_df.iterrows())] # putting all levels C1, C2s in a single list all_C1C2_candles = [] for C1C2s in C1C2_candles.values(): all_C1C2_candles += C1C2s all_C1C2_candles = sorted(all_C1C2_candles, key = lambda x: x["C2"]["date"]) return all_C1C2_candles def FindAllC1C2_Candles_FromPrice( Price: float, *, Pivots_dict:dict[str ,float], df: pd.DataFrame = pd.DataFrame(), ignore_mean: bool = False, C1Type:CandleType = "bearish", C2Type:CandleType = "bullish", timeout:dt.timedelta = dt.timedelta(days=3), min_time :dt.timedelta = dt.timedelta(hours=3), min_C2_Size:int|None = 100, min_C1_size:int|None = None): C1C2s_raw = _FindAllC1C2_Candles_FromPrice(Price=Price, Pivots_dict=Pivots_dict, df=df, C1_Type=C1Type, C2_Type=C2Type, ignore_mean=ignore_mean) ##### adding filter to found C1,C2s all_C1C2_candles = C1C2_time_filter(C1C2s_raw, timeout = timeout, # adding time filter min_time=min_time) if min_C2_Size: all_C1C2_candles = C1C2_size_filter(all_C1C2_candles, # C2 size CandleType="C2", minSize=min_C2_Size) if min_C1_size: all_C1C2_candles = C1C2_size_filter(all_C1C2_candles, # C1 size CandleType="C1", minSize=min_C1_size) # removing repeated C1C2s for C1C2 in all_C1C2_candles: while all_C1C2_candles.count(C1C2) > 1: all_C1C2_candles.remove(C1C2) # sorting C1,C2 s by C2 date all_C1C2_candles = sorted(all_C1C2_candles, key=lambda x: x["C2"]["date"] ) return all_C1C2_candles def C1C2_time_filter(C1C2s:list[dict[str,dict]], timeout:dt.timedelta = dt.timedelta(days = 1), min_time:dt.timedelta = dt.timedelta(hours = 3)): assert timeout > min_time, "timout must be longer than min_time" all_C1C2s = [C1C2 for C1C2 in C1C2s if C1C2["C2"]["date"] - C1C2["C1"]["date"] <= timeout] return [C1C2 for C1C2 in all_C1C2s if C1C2["C2"]["date"] - C1C2["C1"]["date"] > min_time] def C1C2_size_filter(C1C2s: list[dict[str,dict]], CandleType: C1C2Type = "C2", minSize:float = 100): _C1C2s = C1C2s.copy() return [C1C2 for C1C2 in _C1C2s if abs(C1C2[CandleType]["Close"] - C1C2[CandleType]["Open"]) >= minSize] def add_to_ColumnNames(dataframe: pd.DataFrame, *, prefix:str = '', suffix:str = ''): return dataframe.rename(columns={col: prefix+col+suffix for col in dataframe.columns}).copy() def remove_from_ColumnNames(dataframe: pd.DataFrame, *, prefix:str = '', suffix:str = ''): return dataframe.rename(columns={col: col.removeprefix(prefix).removesuffix(suffix) for col in dataframe.columns}).copy() def isEqual_with_tolerance_pct(val1: float, val2: float, tol: float = 0.001): val2_upper = val2 + tol * val2 val2_lower = val2 - tol * val2 return val1 > val2_lower and val1 < val2_upper def _GetLatestCrossedPivot_candle_from_LastClose(*, dataframe: pd.DataFrame = pd.DataFrame(), pivots_dict: dict[str,float], candle_type: CandleType = "bullish"): """get the latest pivot crossed pivot candle relative to last close Raises: ValueError: _description_ Returns: _type_: _description_ """ crossed_candles_dict = _GetCrossedPivot_Candles(df = dataframe, candle_type = candle_type, pivots_dict = pivots_dict) lastCrossed_candle = max( [df.iloc[-1] for levelName, df in crossed_candles_dict.items() if "mean" not in levelName and not df.empty], key = lambda x: x.date, default = pd.Series() ) return lastCrossed_candle ###########################################################