# source: https://raw.githubusercontent.com/imadev26/crypto-trading-bot/4335d4bd136c42854e81def5ee01afa882707de2/user_data/strategies/FastScalperPro.py # directory_url: https://github.com/imadev26/crypto-trading-bot/blob/main/user_data/strategies/ # User: imadev26 # Repository: crypto-trading-bot # --------------------# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file import numpy as np import pandas as pd from datetime import datetime, timedelta, timezone from pandas import DataFrame from typing import Optional, Union, Dict from freqtrade.strategy import ( IStrategy, Trade, Order, PairLocks, informative, BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, RealParameter, ) import talib.abstract as ta import pandas_ta as pta from technical import qtpylib class Github_imadev26_crypto_trading_bot__FastScalperPro__20260831_150440(IStrategy): """ ================================================================================ Github_imadev26_crypto_trading_bot__FastScalperPro__20260831_150440 - High-Frequency Sniper Scalper (1m Timeframe) ================================================================================ Built for: - High trade frequency (20-50 quick trades per day) - Quick in & out (5 to 20 minutes average trade duration) - High Win Rate target (70% - 80%+) - Fast compounding for small capital (50$) Logic: 1. Trend Confirmation (15m): 15m Close > 15m EMA 50 (Trade only with momentum) 2. 1m Trigger: - Price bounces off Lower Bollinger Band (Oversold Dip) - Stochastic RSI K crosses above D in Oversold zone (< 25) - RSI recovers above 35 - Volume surge (Volume > 1.3x Volume SMA 15) 3. Quick Sniper Exits: - Immediate Take-Profit at +1.2% to +1.8% - Trailing Stop locks profit after +0.8% - Tight Hard Stoploss at -1.6% """ INTERFACE_VERSION = 3 # Fast 1-minute execution timeframe timeframe = "1m" informative_timeframe = "15m" can_short: bool = False startup_candle_count: int = 150 process_only_new_candles = True # ------------------------------------------------------------------------- # Sniper Risk Management & Quick Take-Profit # ------------------------------------------------------------------------- # Tight Stoploss (-1.6%) stoploss = -0.016 # Trailing Stop: Activates quickly at +0.8% profit, trails tightly by 0.35% trailing_stop = True trailing_stop_positive = 0.0035 # 0.35% trailing gap trailing_stop_positive_offset = 0.008 # Triggers once +0.8% is hit trailing_only_offset_is_reached = True # Quick Sniper ROI ladder minimal_roi = { "0": 0.018, # 1.8% immediate sniper profit "10": 0.012, # 1.2% profit after 10 mins "20": 0.008, # 0.8% profit after 20 mins "40": 0.005 # 0.5% profit after 40 mins } use_exit_signal = True exit_profit_only = True ignore_roi_if_entry_signal = False order_types = { "entry": "limit", "exit": "limit", "emergency_exit": "market", "force_entry": "market", "force_exit": "market", "stoploss": "market", "stoploss_on_exchange": False } order_time_in_force = { "entry": "GTC", "exit": "GTC" } # Protections protections = [ { "method": "CooldownPeriod", "stop_duration_candles": 3 }, { "method": "StoplossGuard", "lookback_period_candles": 30, "trade_limit": 2, "stop_duration_candles": 30, "only_per_pair": True } ] # ------------------------------------------------------------------------- # Informative Higher Timeframe (15 Min) Trend Filter # ------------------------------------------------------------------------- @informative("15m") def populate_indicators_15m(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200) dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) return dataframe # ------------------------------------------------------------------------- # Base 1-Minute Scalping Indicators # ------------------------------------------------------------------------- def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # EMAs dataframe["ema_9"] = ta.EMA(dataframe, timeperiod=9) dataframe["ema_21"] = ta.EMA(dataframe, timeperiod=21) dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) # RSI & Stochastic RSI (Fast momentum) dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) stoch_rsi = ta.STOCHRSI(dataframe, timeperiod=14, fastk_period=3, fastd_period=3) dataframe["stoch_k"] = stoch_rsi["fastk"] dataframe["stoch_d"] = stoch_rsi["fastd"] # Bollinger Bands for Dip-Buying bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2.0) dataframe["bb_lowerband"] = bollinger["lower"] dataframe["bb_middleband"] = bollinger["mid"] dataframe["bb_upperband"] = bollinger["upper"] dataframe["bb_percent"] = ( (dataframe["close"] - dataframe["bb_lowerband"]) / (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) ) # Volume Moving Average dataframe["volume_mean_15"] = dataframe["volume"].rolling(window=15).mean() return dataframe # ------------------------------------------------------------------------- # Entry Signals (Quick Buy Trigger) # ------------------------------------------------------------------------- def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # 1. Higher Timeframe 15m Trend Confirmation (Micro Bullish bias) conditions.append(dataframe["close_15m"] > dataframe["ema_50_15m"]) conditions.append(dataframe["rsi_15m"] > 45) # 2. Oversold Bounce on 1m (Dip Buying) conditions.append(dataframe["close"] <= dataframe["bb_lowerband"] * 1.002) # Touched / near lower band conditions.append(dataframe["rsi"] < 45) conditions.append(dataframe["stoch_k"] < 30) # Stoch RSI oversold conditions.append(dataframe["stoch_k"] > dataframe["stoch_d"]) # Stoch Bullish Crossover # 3. Volume Surge (Buyer presence) conditions.append(dataframe["volume"] > (dataframe["volume_mean_15"] * 1.15)) conditions.append(dataframe["volume"] > 0) if conditions: dataframe.loc[ np.logical_and.reduce(conditions), "enter_long" ] = 1 return dataframe # ------------------------------------------------------------------------- # Exit Signals (Fast Take-Profit & Reversal) # ------------------------------------------------------------------------- def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # Exit when price hits upper Bollinger Band with overbought RSI hit_upper_bb = dataframe["close"] >= dataframe["bb_upperband"] rsi_overbought = dataframe["rsi"] > 70 stoch_overbought = (dataframe["stoch_k"] > 80) & (dataframe["stoch_k"] < dataframe["stoch_d"]) conditions.append(hit_upper_bb | (rsi_overbought & stoch_overbought)) conditions.append(dataframe["volume"] > 0) if conditions: dataframe.loc[ np.logical_and.reduce(conditions), "exit_long" ] = 1 return dataframe