diff --git a/user_data/strategies/multi_tf.py b/user_data/strategies/multi_tf.py
new file mode 100644
index 0000000..de4ad50
--- /dev/null
+++ b/user_data/strategies/multi_tf.py
@@ -0,0 +1,145 @@
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+import numpy as np
+import talib.abstract as ta
+from freqtrade.strategy import IStrategy, informative
+                                DecimalParameter, IntParameter, BooleanParameter, CategoricalParameter, stoploss_from_open)
+from pandas import DataFrame, Series
+from typing import Dict, List, Optional, Tuple
+from functools import reduce
+from freqtrade.persistence import Trade
+from datetime import datetime, timedelta, timezone
+from freqtrade.exchange import timeframe_to_prev_date
+from freqtrade_strategies.custom_indicators import zema, tv_hma, pmax
+import talib.abstract as ta
+import math
+import pandas_ta as pta
+# from finta import TA as fta
+import logging
+from logging import FATAL
+import time
+
+logger = logging.getLogger(__name__)
+
+# NOT TO BE USED FOR LIVE!!!!!!
+
+class multi_tf (IStrategy):
+
+    def version(self) -> str:
+        return "v1"
+
+    INTERFACE_VERSION = 3
+
+    # ROI table:
+    minimal_roi = {
+        "0": 0.2
+    }
+
+    # Stoploss:
+    stoploss = -0.1
+
+    # Trailing stop:
+    trailing_stop = False
+    trailing_stop_positive = 0.001
+    trailing_stop_positive_offset = 0.01
+    trailing_only_offset_is_reached = True
+
+    # Sell signal
+    use_exit_signal = True
+    exit_profit_only = False
+    exit_profit_offset = 0.01
+    ignore_roi_if_entry_signal = False
+
+    timeframe = '5m'
+
+    process_only_new_candles = True
+    startup_candle_count = 100
+
+    # This method is not required. 
+    # def informative_pairs(self): ...
+
+    # Define informative upper timeframe for each pair. Decorators can be stacked on same 
+    # method. Available in populate_indicators as 'rsi_30m' and 'rsi_1h'.
+    @informative('30m')
+    @informative('1h')
+    def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+        return dataframe
+
+    # Define BTC/STAKE informative pair. Available in populate_indicators and other methods as
+    # 'btc_rsi_1h'. Current stake currency should be specified as {stake} format variable 
+    # instead of hard-coding actual stake currency. Available in populate_indicators and other 
+    # methods as 'btc_usdt_rsi_1h' (when stake currency is USDT).
+    @informative('1h', 'BTC/{stake}')
+    def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+        return dataframe
+
+    # Define BTC/ETH informative pair. You must specify quote currency if it is different from
+    # stake currency. Available in populate_indicators and other methods as 'eth_btc_rsi_1h'.
+    @informative('1h', 'ETH/BTC')
+    def populate_indicators_eth_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+        return dataframe
+
+    # Define BTC/STAKE informative pair. A custom formatter may be specified for formatting
+    # column names. A callable `fmt(**kwargs) -> str` may be specified, to implement custom
+    # formatting. Available in populate_indicators and other methods as 'rsi_fast_upper'.
+    @informative('1h', 'BTC/{stake}', '{column}')
+    def populate_indicators_btc_1h_2(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe['rsi_fast_upper'] = ta.RSI(dataframe, timeperiod=4)
+        return dataframe
+
+    # Define BTC/STAKE informative pair. A custom formatter may be specified for formatting
+    # column names. A callable `fmt(**kwargs) -> str` may be specified, to implement custom
+    # formatting. Available in populate_indicators and other methods as 'btc_rsi_super_fast_1h'.
+    @informative('1h', 'BTC/{stake}', '{base}_{column}_{timeframe}')
+    def populate_indicators_btc_1h_3(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe['rsi_super_fast'] = ta.RSI(dataframe, timeperiod=2)
+        return dataframe
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # Strategy timeframe indicators for current pair.
+        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+        # Informative pairs are available in this method.
+        dataframe['rsi_less'] = dataframe['rsi'] < dataframe['rsi_1h']
+        return dataframe
+    
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        stake = self.config['stake_currency']
+        dataframe.loc[
+            (
+                (dataframe[f'btc_{stake}_rsi_1h'] < 35)
+                &
+                (dataframe['eth_btc_rsi_1h'] < 50)
+                &
+                (dataframe['rsi_fast_upper'] < 40)
+                &
+                (dataframe['btc_rsi_super_fast_1h'] < 30)
+                &
+                (dataframe['rsi_30m'] < 40)
+                &
+                (dataframe['rsi_1h'] < 40)
+                &
+                (dataframe['rsi'] < 30)
+                &
+                (dataframe['rsi_less'] == True)
+                &
+                (dataframe['volume'] > 0)
+            ),
+            ['enter_long', 'enter_tag']] = (1, 'buy_signal_rsi')
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        dataframe.loc[
+            (
+                (dataframe['rsi'] > 70)
+                &
+                (dataframe['rsi_less'] == False)
+                &
+                (dataframe['volume'] > 0)
+            ),
+            ['exit_long', 'exit_tag']] = (1, 'exit_signal_rsi')
+
+        return dataframe