# source: https://raw.githubusercontent.com/Sypers/SeniorProject/c219e2539e43c205542af9f2b59d8e473881d52e/user_data/strategies/MediumTerm.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # --- Do not remove these libs --- from datetime import datetime # noqa from functools import reduce from typing import Optional, Union # noqa import numpy as np # noqa import pandas as pd # noqa import ta.utils from pandas import DataFrame, Series # noqa from ta.momentum import StochRSIIndicator from ta.trend import SMAIndicator # -------------------------------- # Add your lib to import here import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.strategy import (DecimalParameter, IStrategy, IntParameter, BooleanParameter) # BEST Results from strategy github_Sypers_SeniorProject__MediumTerm__20221106_141807 backtesting: # ============================================================= BACKTESTING REPORT ============================================================ # | Pair | Entries | Avg Profit % | Cum Profit % | Tot Profit USDT | Tot Profit % | Avg Duration | Win Draw Loss Win% | # |-----------+-----------+----------------+----------------+-------------------+----------------+------------------+-------------------------| # | FLUX/USDT | 12 | 7.31 | 87.77 | 87.849 | 8.78 | 1 day, 9:40:00 | 7 0 5 58.3 | # | XRP/USDT | 18 | 1.39 | 24.95 | 25.036 | 2.50 | 3 days, 3:20:00 | 11 0 7 61.1 | # | ALGO/USDT | 17 | 1.19 | 20.25 | 20.254 | 2.03 | 3 days, 1:25:00 | 10 0 7 58.8 | # | Backtesting from | 2021-09-04 12:00:00 | # | Backtesting to | 2022-09-27 08:00:00 | class github_Sypers_SeniorProject__MediumTerm__20221106_141807(IStrategy): # 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 # Optimal timeframe for the strategy. timeframe = '4h' # 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". # Return on investment values has been optimized using hyperopt minimal_roi = { "0": 0.607, "1763": 0.272, "4615": 0.065, "8337": 0 } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". # Stoploss and trailing stoploss has been optimized with hyperopt stoploss = -0.125 # Trailing stoploss trailing_stop = True trailing_stop_positive = 0.012 trailing_stop_positive_offset = 0.092 trailing_only_offset_is_reached = False # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 21 # Strategy/Hyperopt parameters # Using the strategy for backtesting or trading will only take the default value. # the value the stochastic indicator has to reach for a buy signal. buy_stoch = DecimalParameter(low=0.01, high=0.3, default=0.155, space="buy") # the value the stochastic indicator has to reach for a sell signal. sell_stoch = DecimalParameter(low=0.65, high=1, default=0.805, space="sell") # value for number of candles for trade locking after buy/sell candle_cooldown = IntParameter(0, 5, default=3, space="protection") # enable or disable RSI buy signal. enable_buy_rsi = BooleanParameter(default=True, space="buy") # enable or disable Stochastic buy signal. enable_buy_stoch = BooleanParameter(default=True, space="buy") # enable or disable MACD buy signal. enable_buy_macd = BooleanParameter(default=True, space="buy") # enable or disable RSI sell signal. enable_sell_rsi = BooleanParameter(default=True, space="sell") # enable or disable Stochastic sell signal. enable_sell_stoch = BooleanParameter(default=True, space="sell") # enable or disable MACD sell signal. enable_sell_macd = BooleanParameter(default=True, space="sell") # 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 protections(self): return [ { # Lock pair trading when a sell signal is issued for a certain number of candles to avoid repeated signals "method": "CooldownPeriod", "stop_duration_candles": self.candle_cooldown.value } ] @property def plot_config(self): return { # Main plot indicators (Moving averages, ...) 'main_plot': { }, 'subplots': { # Subplots - each dict defines one additional plot 'MACD':{ 'macd': {'color': 'blue', 'fill_to': 'macdhist'}, 'macdsignal': {'color': 'orange'}, 'macdhist': {'type': 'bar', 'plotly': {'opacity': 0.9}} }, "RSI": { 'rsi': {'color': 'red'} }, 'StochasticRSI': { 'stoch': {'color': 'green'} } } } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: rsi = ta.momentum.RSIIndicator(dataframe['close']) # load RSI indicator stoch = ta.momentum.StochRSIIndicator(dataframe['close']) # load Stochastic indicator macd = ta.trend.MACD(dataframe['close'], window_slow=21, window_fast=8, window_sign=5) # load MACD indicator # attach indicators data to a new column to the pair dataframe dataframe['stoch'] = stoch.stochrsi() dataframe['rsi'] = rsi.rsi() dataframe['macd'] = macd.macd() dataframe['macdsignal'] = macd.macd_signal() dataframe['stoch_buy'] = self.stoch_check(dataframe, True) dataframe['stoch_sell'] = self.stoch_check(dataframe, False) """ 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 """ 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 """ conditions = [] if self.enable_buy_rsi.value: conditions.append(dataframe['rsi'] >= 50) if self.enable_buy_stoch.value: conditions.append( (dataframe['stoch_buy']) | dataframe['stoch_buy'].shift() | dataframe['stoch_buy'].shift(2) | dataframe['stoch_buy'].shift(3)) if self.enable_buy_macd.value: conditions.append(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']) | qtpylib.crossed_above(dataframe['macd'].shift(), dataframe['macdsignal'].shift()) | qtpylib.crossed_above(dataframe['macd'].shift(2), dataframe['macdsignal'].shift(2))) # for hyperopt if there is no conditions then return dataframe with no signals if len(conditions) == 0: return dataframe # locate the row which meets these conditions: dataframe.loc[ ( reduce(lambda x, y: x & y, conditions) ), 'enter_long'] = 1 # dataframe.loc[ # ( # # RSI value is more than 50 means a bullish (up) trend # (dataframe['rsi'] >= 50) & # # check if there is stochasticRSI buy signal in the previous 3 rows/candles # ((dataframe['stoch_buy']) | dataframe['stoch_buy'].shift() | dataframe['stoch_buy'].shift(2) # | dataframe['stoch_buy'].shift(3)) & # # check if the MACD value crossed above the MACD Signal value within 2 rows # (qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) # ), 'enter_long'] = 1 # issue a buy signal if a row is located with all conditions met 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 """ conditions = [] if self.enable_sell_rsi.value: conditions.append(dataframe['rsi'] < 50) if self.enable_sell_stoch.value: conditions.append((dataframe['stoch_sell']) | (dataframe['stoch_sell'].shift()) | ( dataframe['stoch_sell']).shift(2) | dataframe['stoch_sell'].shift(3)) if self.enable_sell_macd.value: conditions.append(qtpylib.crossed_below(dataframe['macd'], dataframe['macdsignal']) | qtpylib.crossed_below(dataframe['macd'].shift(), dataframe['macdsignal'].shift()) | qtpylib.crossed_below(dataframe['macd'].shift(2), dataframe['macdsignal'].shift(2))) # for hyperopt if there is no conditions then return dataframe with no signals if len(conditions) == 0: return dataframe # locate the row which meets these conditions: dataframe.loc[ ( reduce(lambda x, y: x & y, conditions) ), 'exit_long'] = 1 # dataframe.loc[( # # RSI value is less than 50 means a bearish (down) trend # (dataframe['rsi'] < 50) & # # check if there is stochasticRSI sell signal in the previous 3 rows/candles # ((dataframe['stoch_sell']) | (dataframe['stoch_sell'].shift()) | ( # dataframe['stoch_sell']).shift(2)| dataframe['stoch_sell'].shift(3)) & # # check if the MACD value crossed below the MACD Signal value within 2 rows # (qtpylib.crossed_below(dataframe['macd'], dataframe['macdsignal'])) # ), 'exit_long'] = 1 # issue a sell signal if a row is located with all conditions met return dataframe def stoch_check(self, dataframe: DataFrame, buy_signal: bool) -> Series: """ Check the dataframe for Stochastic crossover with stochastic buy and sell values. :param dataframe: Dataframe that contains the pair data including the indicators data. :param buy_signal: True to check for buy signals, False to check for sell signals of the indicator. :return: A pandas Series that includes the rows where Stochastic crossed with Stochastic buy or sell values """ if buy_signal: series = qtpylib.crossed_below(dataframe['stoch'], self.buy_stoch.value) else: series = qtpylib.crossed_above(dataframe['stoch'], self.sell_stoch.value) return series