# source: https://raw.githubusercontent.com/Willemstijn/user_data/96ed82091490659163aa9d85d599678f56cb54ef/strategies/00.Working/GIDLE.py # ============================================================================================== # "3.9 Million" trading strategy for FUTURES/SPOT # # Made by: # ______ _ _ _____ _ ______ _ # | _ \ | | | | / __ \ | | | _ \ | | # | | | | _ _ | |_ ___ | |__ | / \/ _ __ _ _ _ __ | |_ ___ | | | | __ _ __| | # | | | || | | || __|/ __|| '_ \ | | | '__|| | | || '_ \ | __|/ _ \ | | | |/ _` | / _` | # | |/ / | |_| || |_| (__ | | | || \__/\| | | |_| || |_) || |_| (_) || |/ /| (_| || (_| | # |___/ \__,_| \__|\___||_| |_| \____/|_| \__, || .__/ \__|\___/ |___/ \__,_| \__,_| # __/ || | # |___/ |_| # Become my Patron: https://www.patreon.com/dutchalgotrading # # Version : 1.0 # Date : 2023-09 # # Remarks : # As published, explained and tested in my Youtube video on my channel: https://www.youtube.com/@dutchalgotrading # # This strategy is inspired by the following Youtube video's # * https://www.youtube.com/watch?v=E6pgaBOXwCk # * https://www.youtube.com/watch?v=0dnwWvo4WOY # * https://www.youtube.com/watch?v=dwlXUt40Mp8 # * https://www.youtube.com/watch?v=J4lGSNwftsM # # All credits go to these people if this strategy will also make you 3.9 million usd. I'm just coding this stuff for you. # # freqtrade backtesting -c user_data/spot_config.json -s Million --timerange=20190101-20210530 --timeframe=1d # freqtrade plot-dataframe -c user_data/spot_config.json -s Million --timerange=20200101-20210530 --timeframe=1d -p BTC/USDT # # freqtrade backtesting -c user_data/futures_config.json -s Million --timerange=20210101-20210530 --timeframe=1d # freqtrade plot-dataframe -c user_data/futures_config.json -s Million --timerange=20200101-20210530 --timeframe=1d -p MATIC/USDT:USDT # ============================================================================================== # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these libs --- from functools import reduce import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from typing import Optional, Union from freqtrade.strategy import ( BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, IStrategy, merge_informative_pair, ) # -------------------------------- # Add your lib to import here import talib.abstract as ta import pandas_ta as pta from technical import qtpylib class github_Willemstijn_user_data__GIDLE__20231010_184246(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 # Proposed timeframe for the strategy. Can be altered to your own preferred timeframe. timeframe = "1d" # Can this strategy go short? can_short: bool = False # Minimal ROI designed for the strategy. # Set to 100% minimal_roi = {"0": 10.0} # Optimal stoploss designed for the strategy. # Set to 100% stoploss = -1.0 # Trailing stoploss trailing_stop = 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 # Set to the default of 30. startup_candle_count: int = 30 # 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"} def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Gann Hilo dataframe['hilo'] = pta.hilo(high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], high_length=13, low_length=21, mamode=None, offset=None)["HILO_13_21"] # Ichimoku Chikou span # dataframe['chikou'] = pta.ichimoku(high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], tenkan=9, kijun=10, senkou=52, include_chikou=True, offset=0)[0]['ICS_10'] dataframe['imi'] = calculate_imi(dataframe, length=50) dataframe['imi_ema'] = pta.ema(close=dataframe['imi'], length=7) # Assuming you have a DataFrame 'dataframe' with columns: 'date', 'open', 'high', 'low', 'close', 'volume' # You can calculate the IMI like this: dataframe['imi'] = calculate_imi(dataframe, length=7) # Detrended Price Oscillator dataframe['dpo'] = pta.dpo(close=dataframe['close'], length=14, centered=True, lookahead=False) dataframe['lsma'] = calculate_lsma(dataframe, 14) # Ehlers Ficher transform indicator dataframe['fisherT'] = pta.fisher(high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], length=9)['FISHERT_9_1'] dataframe['fishers'] = pta.fisher(high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], length=9)['FISHERTs_9_1'] # Add trade and exit signals to the dataframe. entry_signals(dataframe) exit_signals(dataframe) # 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] #print(self) #print(metadata) #print(dataframe.tail(15)) # #print(dataframe[['date', 'close', 'regression_line', 'trend', 'ut_direction', 'ut_bot_signal', 'trade_signal', 'exit_signal']][( # dataframe['trade_signal'] == 'Go_long') | (dataframe['trade_signal'] == 'Go_short')].tail(20)) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Long trades here: conditions = [] conditions.append( (dataframe['entry_direction'] == 'Go_long' & dataframe['entry_signal'] == True) # (dataframe['trend'] == 'Rising' and dataframe['ut_direction'] == 1 and dataframe['ut_bot_signal']) & (dataframe["volume"] > 0) # Guard ) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1 # For short trades, use the section below conditions.append( (dataframe['entry_direction'] == 'Go_short' & dataframe['entry_signal'] == True) # (dataframe['trend'] == 'Falling' and dataframe['ut_direction'] == 0 and dataframe['ut_bot_signal']) & (dataframe["volume"] < 0) # Guard ) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Exit long here: conditions = [] conditions.append( (dataframe['exit_direction'] == 'Exit_long' & dataframe['exit_signal'] == True) # (dataframe['ut_direction'] == 0 and dataframe['ut_bot_signal']) & (dataframe["volume"] > 0) # Guard ) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1 # For short trades, use the section below conditions.append( (dataframe['exit_direction'] == 'Exit_short' & dataframe['exit_signal'] == True) # (dataframe['ut_direction'] == 1 and dataframe['ut_bot_signal']) & (dataframe["volume"] > 0) # Guard ) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'exit_short'] = 1 return dataframe # === Functions section === def entry_signals(df): # Create boolean masks for each condition long_condition = (df['dpo'] > 0) & (df['lsma'] > df['hilo']) & (df['imi'] > df['imi_ema']) short_condition = (df['dpo'] < 0) & (df['lsma'] < df['hilo']) & (df['imi'] < df['imi_ema']) # Create 'trade_direction' column based on conditions df['entry_direction'] = np.where(long_condition, 'Go_long', np.where( short_condition, 'Go_short', 'Stay_neutral')) # Calculate trade signals based on changes in 'trade_direction' and trend condition df['entry_changed'] = df['entry_direction'].shift(1) != df['entry_direction'] df['entry_signal'] = np.where(df['entry_changed'] & ( (df['entry_direction'] == 'Go_long') | (df['entry_direction'] == 'Go_short')), True, False) # Clean up by dropping the 'trend_changed' column df.drop(columns=['entry_changed'], inplace=True) return df def exit_signals(df): # Create boolean masks for exit conditions exit_long_condition = (df['fisherT'] < df['fishers']) exit_short_condition = (df['fisherT'] > df['fishers']) # Create 'exit_signal' column based on exit conditions df['exit_direction'] = np.where(exit_long_condition, 'Exit_long', np.where( exit_short_condition, 'Exit_short', 'Do_nothing')) # Calculate exit trade signals based on changes in 'exit_signal' df['exit_signal'] = df['exit_direction'].shift(1) != df['exit_direction'] return df def calculate_imi(dataframe, length=14): # Calculate gains and losses dataframe['gain'] = 0.0 dataframe['loss'] = 0.0 for i in range(1, len(dataframe)): if dataframe['close'].iloc[i] > dataframe['open'].iloc[i]: dataframe.at[dataframe.index[i], 'gain'] = dataframe['close'].iloc[i] - \ dataframe['open'].iloc[i] else: dataframe.at[dataframe.index[i], 'loss'] = dataframe['open'].iloc[i] - \ dataframe['close'].iloc[i] # Calculate sums of gains and losses over 'length' periods dataframe['up_sum'] = dataframe['gain'].rolling(window=length).sum() dataframe['down_sum'] = dataframe['loss'].rolling(window=length).sum() # Calculate IMI dataframe['imi'] = 100 * dataframe['up_sum'] / (dataframe['up_sum'] + dataframe['down_sum']) # Clean up and drop intermediate columns dataframe.drop(['gain', 'loss', 'up_sum', 'down_sum'], axis=1, inplace=True) return dataframe['imi'] def calculate_lsma(data, period=21): lsma_values = [] for i in range(period, len(data)): # Extract the most recent N data points subset = data.iloc[i - period:i] # Perform linear regression to fit a line x = np.arange(len(subset)) y = subset['close'].values slope, intercept = np.polyfit(x, y, 1) # Calculate the LSMA value using the linear equation lsma = intercept + slope * (period - 1) lsma_values.append(lsma) lsma_series = pd.Series(lsma_values, index=data.index[period:]) return lsma_series