diff --git a/user_data/strategies/PatternRecognition.py b/user_data/strategies/PatternRecognition.py
new file mode 100644
index 0000000..65d4151
--- /dev/null
+++ b/user_data/strategies/PatternRecognition.py
@@ -0,0 +1,87 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+# flake8: noqa: F401
+
+# --- Do not remove these libs ---
+import numpy as np  # noqa
+import pandas as pd  # noqa
+from pandas import DataFrame
+
+from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
+                                IStrategy, IntParameter)
+
+# --------------------------------
+# Add your lib to import here
+import talib
+import talib.abstract as ta
+import pandas_ta as pta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from technical.util import resample_to_interval, resampled_merge
+
+
+class PatternRecognition(IStrategy):
+    # Pattern Recognition Strategy 
+    # By: @Mablue
+    # freqtrade hyperopt -s PatternRecognition --hyperopt-loss SharpeHyperOptLossDaily -e 1000
+    # 
+
+    # 173/1000:    510 trades. 408/14/88 Wins/Draws/Losses. Avg profit   2.35%. Median profit   5.60%. Total profit 5421.34509618 USDT ( 542.13%). Avg duration 7 days, 11:54:00 min. Objective: -1.60426
+
+
+    # Buy hyperspace params:
+    buy_params = {
+        "buy_pr1": "CDLHIGHWAVE",
+        "buy_vol1": -100,
+    }
+
+    # ROI table:
+    minimal_roi = {
+        "0": 0.936,
+        "5271": 0.332,
+        "18147": 0.086,
+        "48152": 0
+    }
+
+    # Stoploss:
+    stoploss = -0.288
+
+    # Trailing stop:
+    trailing_stop = True
+    trailing_stop_positive = 0.032
+    trailing_stop_positive_offset = 0.084
+    trailing_only_offset_is_reached = True
+
+    # Optimal timeframe for the strategy.
+    timeframe = '1d'
+    prs = talib.get_function_groups()['Pattern Recognition']
+
+    # # Strategy parameters
+    buy_pr1 = CategoricalParameter(prs, default=prs[0], space="buy")
+    buy_vol1 = CategoricalParameter([-100,100], default=0, space="buy")
+
+    
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        for pr in self.prs:
+            dataframe[pr] = getattr(ta, pr)(dataframe)
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+                (dataframe[self.buy_pr1.value]==self.buy_vol1.value)
+                # |(dataframe[self.buy_pr2.value]==self.buy_vol2.value)
+            ),
+            'enter_long'] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+            #     (dataframe[self.sell_pr1.value]==self.sell_vol1.value)|
+            #     (dataframe[self.sell_pr2.value]==self.sell_vol2.value)
+            ),
+            'exit_long'] = 1
+
+        return dataframe