# source: https://raw.githubusercontent.com/rmallarapu-bc/brahma/9287745fc036c2f4c00c586e598290885c2883b9/archive/Vashishta.py # --- Do not remove these libs --- from typing import Optional from pandas import DataFrame import talib.abstract as tal import freqtrade.vendor.qtpylib.indicators as qtpylib from datetime import datetime from freqtrade.persistence import Trade from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair from functools import reduce import logging import warnings import pandas as pd log = logging.getLogger(__name__) log.setLevel(logging.INFO) warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning) import pandas_ta as ta import pandas as pd import numpy as np def guppy(df): """ Uses a dataframe of at least 70 rows and a columned name 'close' as input Returns trend prediction based on Super Guppy """ if type(df) != pd.DataFrame: if type(df) == list: df = pd.DataFrame(df, columns=["close"]) elif type(df) == np.ndarray: df = pd.DataFrame(data=df, columns=["close"]) else: #print(f"Your input type is not supported: {type(df)}") # Make the fast and slow EMAs guppies = [{"kind": "ema", "length": f} for f in list(range(3, 25, 2))] guppies += [{"kind": "ema", "length": s} for s in list(range(25, 73, 3))] # Create your own Custom Strategy super_guppy = ta.Strategy( name="Super Guppy", description="7 Fast EMAs, 16 slow EMAs", ta=guppies ) # Run the strategy over close prices df.ta.strategy(super_guppy, close="close") # Remove all NaN rows, which are the first 70 rows df = df.dropna() # To ignore the warning df = df.copy() df['colfastL'] = np.where((df["EMA_3"] > df["EMA_5"]) & (df["EMA_5"] > df["EMA_7"]) & (df["EMA_7"] > df["EMA_9"]) & (df["EMA_9"] > df["EMA_11"]) & (df["EMA_11"] > df["EMA_13"]) & (df["EMA_13"] > df["EMA_15"]) & (df["EMA_15"] > df["EMA_17"]) & (df["EMA_17"] > df["EMA_19"]) & (df["EMA_19"] > df["EMA_21"]) & (df["EMA_21"] > df["EMA_23"]), True, False) df['colfastS'] = np.where((df["EMA_3"] < df["EMA_5"]) & (df["EMA_5"] < df["EMA_7"]) & (df["EMA_7"] < df["EMA_9"]) & (df["EMA_9"] < df["EMA_11"]) & (df["EMA_11"] < df["EMA_13"]) & (df["EMA_13"] < df["EMA_15"]) & (df["EMA_15"] < df["EMA_17"]) & (df["EMA_17"] < df["EMA_19"]) & (df["EMA_19"] < df["EMA_21"]) & (df["EMA_21"] < df["EMA_23"]), True, False) df["colslowL"] = np.where((df["EMA_25"] > df["EMA_28"]) & (df["EMA_28"] > df["EMA_31"]) & (df["EMA_31"] > df["EMA_34"]) & (df["EMA_34"] > df["EMA_37"]) & (df["EMA_37"] > df["EMA_40"]) & (df["EMA_40"] > df["EMA_43"]) & (df["EMA_43"] > df["EMA_46"]) & (df["EMA_46"] > df["EMA_49"]) & (df["EMA_49"] > df["EMA_52"]) & (df["EMA_52"] > df["EMA_55"]) & (df["EMA_55"] > df["EMA_58"]) & (df["EMA_58"] > df["EMA_61"]) & (df["EMA_61"] > df["EMA_64"]) & (df["EMA_64"] > df["EMA_67"]) & (df["EMA_67"] > df["EMA_70"]), True, False) df["colslowS"] = np.where((df["EMA_25"] < df["EMA_28"]) & (df["EMA_28"] < df["EMA_31"]) & (df["EMA_31"] < df["EMA_34"]) & (df["EMA_34"] < df["EMA_37"]) & (df["EMA_37"] < df["EMA_40"]) & (df["EMA_40"] < df["EMA_43"]) & (df["EMA_43"] < df["EMA_46"]) & (df["EMA_46"] < df["EMA_49"]) & (df["EMA_49"] < df["EMA_52"]) & (df["EMA_52"] < df["EMA_55"]) & (df["EMA_55"] < df["EMA_58"]) & (df["EMA_58"] < df["EMA_61"]) & (df["EMA_61"] < df["EMA_64"]) & (df["EMA_64"] < df["EMA_67"]) & (df["EMA_67"] < df["EMA_70"]), True, False) # Fast EMA color rules df["colFinal"] = np.where((df["colfastL"] == True) & (df["EMA_25"] > df["EMA_70"]), "aqua", "gray") # Aqua = Buy df["colFinal"] = np.where((df["colslowL"] == True) & (df["EMA_25"] < df["EMA_70"]), "blue", df["colFinal"]) # Blue = Sell df["colFinal2"] = np.where((df["colfastL"] == True), "lime", "gray") # Lime = Buy df["colFinal2"] = np.where((df["colslowS"] == True), "red", "gray") # Red = Sell # Custom buy signals, 1 = Buy, 0 = Hold, -1 = Sell df["guppy"] = np.where((df["colFinal"] == "aqua") | (df["colFinal2"] == "lime"), 1, 0) # Aqua = Buy df["guppy"] = np.where((df["colFinal"] == "blue") | (df["colFinal2"] == "red"), -1, df["guppy"]) df["guppy"] = np.where((df["colFinal"] == "gray") & (df["colFinal2"] == "gray"), 0, df["guppy"]) return df class Github_rmallarapu_bc_brahma__Vashishta__20240229_213751(IStrategy): INTERFACE_VERSION: int = 3 minimal_roi = {"0": 0.5} can_short = True # Exit criteria use_exit_signal = False exit_profit_only = False ignore_roi_if_entry_signal = True # Trailing stoploss trailing_stop = True trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.01 trailing_only_offset_is_reached = True stoploss = -0.1 timeframe = '1d' informative_timeframe = '1w' position_adjustment_enable = True max_entry_position_adjustment = 10 # Strategy parameters rsi_length = IntParameter(10, 40, default=14, space="buy") bb_window = IntParameter(10, 40, default=17, space="buy") bb_std = IntParameter(1, 6, default=1, space="buy") buy_rsi = IntParameter(10, 40, default=37, space="buy") sell_rsi = IntParameter(60, 90, default=76, space="sell") # timeframe = '5m' buy_params = {"buy_limit": 0.943} buy_limit = DecimalParameter(0.90, 0.98, default=0.98, space='buy', decimals=3, optimize=True, load=True) process_only_new_candles = False startup_candle_count = 10 use_custom_stoploss = False def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] return informative_pairs def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: pairs = self.dp.current_whitelist()[0] # Take the first pair informative = self.dp.get_pair_dataframe(pair=pairs, timeframe=self.informative_timeframe) # calculate SMA20 on informative pair informative['sma20'] = informative['close'].rolling(20).mean() # Combine the 2 dataframe # This will result in a column named 'closeETH' or 'closeBTC' - depending on stake_currency. dataframe['rsi'] = tal.RSI(dataframe, timeperiod=self.rsi_length.value) # TEMA - Triple Exponential Moving Average dataframe["tema"] = tal.TEMA(dataframe, timeperiod=9) # Bollinger bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=self.bb_window.value, stds=self.bb_std.value) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['ema20'] = tal.EMA(dataframe, timeperiod=20) dataframe['ema50'] = tal.EMA(dataframe, timeperiod=50) dataframe['ema100'] = tal.EMA(dataframe, timeperiod=100) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True) dataframe = guppy(dataframe) return dataframe # def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # dataframe = guppy(dataframe) # return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( # (dataframe['ema20'] > dataframe['ema50']) & # # (dataframe['close_15m'] > dataframe['sma20_15m']) & # (dataframe['close_1h'] > dataframe['sma20_1h']) & # (dataframe['rsi'] < self.buy_rsi.value) & # (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe["guppy"] == 1) ), 'enter_long'] = 1 dataframe.loc[ ( # (dataframe['ema20'] > dataframe['ema50']) & # # (dataframe['close_15m'] > dataframe['sma20_15m']) & # (dataframe['close_1h'] > dataframe['sma20_1h']) & # (dataframe['rsi'] < self.buy_rsi.value) & # (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe["guppy"] == -1) ), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe["guppy"] == -1) ), 'exit_long'] = 1 dataframe.loc[ ( (dataframe["guppy"] == 1) ), 'exit_short'] = 1 return dataframe