# source: https://raw.githubusercontent.com/padmarajkore/Ai-crypto-trading-system/9ca18f9c9c388458f2d47c01e507d03a2ef8adee/freqtrade/user_data/strategies/HybridScalper.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these imports --- import numpy as np import pandas as pd from datetime import datetime, timedelta, timezone from pandas import DataFrame from typing import Optional, Union from freqtrade.strategy import ( IStrategy, Trade, Order, PairLocks, informative, # @informative decorator # Hyperopt Parameters BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, RealParameter, # timeframe helpers timeframe_to_minutes, timeframe_to_next_date, timeframe_to_prev_date, # Strategy helper functions merge_informative_pair, stoploss_from_absolute, stoploss_from_open, ) # -------------------------------- # Add your lib to import here import talib.abstract as ta from technical import qtpylib # This class is a sample. Feel free to customize it. class Github_padmarajkore_Ai_crypto_trading_system__HybridScalper__20251228_165325(IStrategy): """ Hybrid Scalper Strategy - Combines aggressive scalping with momentum chasing for high-frequency trades. Uses ultra-sensitive RSI thresholds, EMA crossovers, and MACD for confirmation. """ # Strategy interface version - allow new iterations of the strategy interface. INTERFACE_VERSION = 3 can_short: bool = False # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { "0": 0.012, # 1.2% immediately "2": 0.008, # 0.8% after 2 minutes "4": 0.004 # 0.4% after 4 minutes } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.018 # Slightly tighter stoploss for hybrid approach # Trailing stoploss trailing_stop = True trailing_stop_positive = 0.0025 # Enable trailing stop earlier at 0.25% trailing_stop_positive_offset = 0.005 # Lock in profit at 0.5% trailing_only_offset_is_reached = True # Optimal timeframe for the strategy. timeframe = "1m" # Ultra-short timeframe for scalping # 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 # Hyperoptable parameters rsi_buy = IntParameter(25, 45, default=35, space='buy', optimize=True) rsi_sell = IntParameter(55, 75, default=65, space='sell', optimize=True) # Number of candles the strategy requires before producing valid signals 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"} plot_config = { "main_plot": { "ema5": {"color": "orange"}, "ema10": {"color": "blue"}, "ema21": {"color": "purple"}, }, "subplots": { "RSI": { "rsi": {"color": "red"}, }, "MACD": { "macd": {"color": "blue"}, "macdsignal": {"color": "orange"}, } } } 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 """ # RSI for momentum dataframe['rsi'] = ta.RSI(dataframe, timeperiod=9) # EMAs for quick crossovers dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21) # MACD for momentum strength macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # Price change percentage for volatility measurement dataframe['price_change'] = (dataframe['close'] - dataframe['open']) / dataframe['open'] # Volume indicators dataframe['volume_sma'] = dataframe['volume'].rolling(window=10).mean() 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 """ # Aggressive long entry conditions dataframe.loc[ ( # RSI crosses above 35 (oversold to neutral) (qtpylib.crossed_above(dataframe['rsi'], self.rsi_buy.value)) & # EMA crossover - faster EMA crosses above slower EMA (qtpylib.crossed_above(dataframe['ema5'], dataframe['ema10'])) & # MACD histogram positive and increasing (dataframe['macdhist'] > 0) & (dataframe['macdhist'].shift(1) < dataframe['macdhist']) & # Volatility filter - significant price change (abs(dataframe['price_change']) > 0.002) ), "enter_long", ] = 1 # Aggressive short entry conditions dataframe.loc[ ( # RSI crosses below 65 (overbought to neutral) (qtpylib.crossed_below(dataframe['rsi'], self.rsi_sell.value)) & # EMA crossover - faster EMA crosses below slower EMA (qtpylib.crossed_below(dataframe['ema5'], dataframe['ema10'])) & # MACD histogram negative and decreasing (dataframe['macdhist'] < 0) & (dataframe['macdhist'].shift(1) > dataframe['macdhist']) & # Volatility filter - significant price change (abs(dataframe['price_change']) > 0.002) ), "enter_short", ] = 1 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 """ # Quick exit for long positions dataframe.loc[ ( # RSI crosses below 65 (take profit) (qtpylib.crossed_below(dataframe['rsi'], self.rsi_sell.value)) | # EMA crossover in opposite direction (qtpylib.crossed_below(dataframe['ema5'], dataframe['ema10'])) | # MACD histogram turns negative (dataframe['macdhist'] < 0) | # Fixed time exit based on ROI (dataframe['close'] < dataframe['open']) # Simple red candle exit ), "exit_long", ] = 1 # Quick exit for short positions dataframe.loc[ ( # RSI crosses above 35 (take profit) (qtpylib.crossed_above(dataframe['rsi'], self.rsi_buy.value)) | # EMA crossover in opposite direction (qtpylib.crossed_above(dataframe['ema5'], dataframe['ema10'])) | # MACD histogram turns positive (dataframe['macdhist'] > 0) | # Fixed time exit based on ROI (dataframe['close'] > dataframe['open']) # Simple green candle exit ), "exit_short", ] = 1 return dataframe