diff --git a/user_data/strategies/PowerTower.py b/user_data/strategies/PowerTower.py
new file mode 100644
index 0000000..c99cdfe
--- /dev/null
+++ b/user_data/strategies/PowerTower.py
@@ -0,0 +1,104 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+# flake8: noqa: F401
+# isort: skip_file
+# --- Do not remove these libs ---
+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 PowerTower(IStrategy):
+    # By: Masoud Azizi (@mablue)
+    # Power Tower is a complitly New Strategy(or Candlistic Pattern or Indicator) to finding strongly rising coins.
+    # much effective than "Three black Crows" but based on Idea of this candlestick pattern, but with different rules!
+
+    # 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 = '5m'
+
+    # Can this strategy go short?
+    can_short: bool = False
+
+    # $ freqtrade hyperopt -s PowerTower --hyperopt-loss SharpeHyperOptLossDaily
+
+    # "max_open_trades": 1,
+    # "stake_currency": "USDT",
+    # "stake_amount": 990,
+    # "dry_run_wallet": 1000,
+    # "trading_mode": "spot",
+    # "XMR/USDT","ATOM/USDT","FTM/USDT","CHR/USDT","BNB/USDT","ALGO/USDT","XEM/USDT","XTZ/USDT","ZEC/USDT","ADA/USDT",
+    # "CHZ/USDT","BTT/USDT","LUNA/USDT","VRA/USDT","KSM/USDT","DASH/USDT","COMP/USDT","CRO/USDT","WAVES/USDT","MKR/USDT",
+    # "DIA/USDT","LINK/USDT","DOT/USDT","YFI/USDT","UNI/USDT","FIL/USDT","AAVE/USDT","KCS/USDT","LTC/USDT","BSV/USDT",
+    # "XLM/USDT","ETC/USDT","ETH/USDT","BTC/USDT","XRP/USDT","TRX/USDT","VET/USDT","NEO/USDT","EOS/USDT","BCH/USDT",
+    # "CRV/USDT","SUSHI/USDT","KLV/USDT","DOGE/USDT","CAKE/USDT","AVAX/USDT","MANA/USDT","SAND/USDT","SHIB/USDT",
+    # "KDA/USDT","ICP/USDT","MATIC/USDT","ELON/USDT","NFT/USDT","ARRR/USDT","NEAR/USDT","CLV/USDT","SOL/USDT","SLP/USDT",
+    # "XPR/USDT","DYDX/USDT","FTT/USDT","KAVA/USDT","XEC/USDT"
+    # "method": "StaticPairList"
+
+    # 38/100:     67 trades. 32/34/1 Wins/Draws/Losses.
+    # Avg profit   1.23%. Median profit   0.00%.
+    # Total profit 815.05358020 USDT (  81.51%).
+    # Avg duration 10:58:00 min. Objective: -9.86920
+    
+    # ROI table:
+    minimal_roi = {
+        "0": 0.213,
+        "39": 0.048,
+        "56": 0.029,
+        "159": 0
+    }
+
+    # Stoploss:
+    stoploss = -0.288
+
+    # Trailing stop:
+    trailing_stop = False  # value loaded from strategy
+    trailing_stop_positive = None  # value loaded from strategy
+    trailing_stop_positive_offset = 0.0  # value loaded from strategy
+    trailing_only_offset_is_reached = False  # value loaded from strategy
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 30
+
+    # Strategy parameters
+    buy_pow = DecimalParameter(0, 4, decimals=3, default=3.849, space="buy")
+    sell_pow = DecimalParameter(0, 4, decimals=3, default=3.798, space="sell")
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+                    (dataframe['close'].shift(0) > dataframe['close'].shift(2) ** self.buy_pow.value) &
+                    (dataframe['close'].shift(1) > dataframe['close'].shift(3) ** self.buy_pow.value) &
+                    (dataframe['close'].shift(2) > dataframe['close'].shift(4) ** self.buy_pow.value)
+
+            ),
+            'enter_long'] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[(
+                (dataframe['close'].shift(0) < dataframe['close'].shift(2) ** self.sell_pow.value) |
+                (dataframe['close'].shift(1) < dataframe['close'].shift(3) ** self.sell_pow.value) |
+                (dataframe['close'].shift(2) < dataframe['close'].shift(4) ** self.sell_pow.value)
+        ),
+        'exit_long'] = 1
+
+        return dataframe