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CusTrend_coralTrend_Adx_EMA_Oct_1h

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uploads/CusTrend_coralTrend_Adx_EMA_Oct_1h__2_.py · uploaded by 🐋 Ron · first seen 2026-07-20 · ⬇ 1 download

Basics mode: futures timeframe: 1h interface version: 3 4h
Settings stoploss: -0.347 has minimal roi trailing protections process only new candles startup candle count: 30 hyperopt hyperopt params: 17
Indicators ADX EMA RSI SAR pandas_ta talib technical
Concepts risk_management trailing trend_following
Methods coral_trend determine_trend leverage plot_config protections
3 related strategies (⧉ identical code, ≈ similar name)

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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
from functools import reduce
import numpy as np
import pandas as pd
from pandas import DataFrame
from datetime import datetime
from typing import Optional, Union


from freqtrade.persistence import Trade

from freqtrade.strategy import (BooleanParameter, CategoricalParameter, stoploss_from_open, DecimalParameter,
                                IntParameter, IStrategy, informative, merge_informative_pair)

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
from technical import qtpylib

# custom indicators
# #############################################################################################################################################################################################


#  Coral Trend Indicator
'''
The "Coral Trend Indicator" is a trend-following indicator that uses a combination of exponential moving averages (EMAs) and the Commodity Channel Index (CCI) to identify trends and potential reversal points

This code imports the necessary libraries (pandas and numpy) and defines a function called coral_trend() that takes a DataFrame of cryptocurrency data (with a "close" column) and the EMA period, CCI period, and CCI threshold as inputs. The function first calculates the fast and slow exponential moving averages (EMAs) of the cryptocurrency and stores them in new columns called "ema_fast" and "ema_slow", respectively. It then calculates the Commodity Channel Index (CCI) of the cryptocurrency and stores it in a new column called "cci".

The function then determines the trend based on the EMAs and CCI, and stores it in a new column called "coral_trend".

The coral_trend() function determines the trend by using the np.where() function to set the value of the "coral_trend" column based on the following conditions:

    If the fast EMA is greater than the slow EMA and the CCI is less than the negative CCI threshold, then the trend is set to 1 (indicating an uptrend).
    If the fast EMA is less than the slow EMA and the CCI is greater than the positive CCI threshold, then the trend is set to -1 (indicating a downtrend).
    Otherwise, the trend is set to 0 (indicating no trend or a neutral market).

The coral_trend() function then returns the modified DataFrame with the "coral_trend" column added.
'''
def coral_trend(df, ema_period=10, cci_period=20, cci_threshold=100):
    # Calculate the exponential moving averages (EMAs)
    df['ema_fast'] = df['close'].ewm(span=ema_period, adjust=False).mean()
    df['ema_slow'] = df['close'].ewm(span=ema_period*2, adjust=False).mean()
    
    # Calculate the Commodity Channel Index (CCI)
    df['cci'] = ((df['close'] - df['close'].rolling(cci_period).mean()) / df['close'].rolling(cci_period).std()) * np.sqrt(cci_period)
    
    # Determine the trend based on the EMAs and CCI
    df['coral_trend'] = np.where((df['ema_fast'] > df['ema_slow']) & (df['cci'] < -cci_threshold), 1, 0)
    df['coral_trend'] = np.where((df['ema_fast'] < df['ema_slow']) & (df['cci'] > cci_threshold), -1, df['coral_trend'])
    
    return df


#double up and down closing based trend

def determine_trend(df):
    df['trend'] = 0
    for i in range(2, len(df)):
        close_prev = df['close'].iloc[i-1]
        close = df['close'].iloc[i]
        high_prev = df['high'].iloc[i-1]
        low_prev = df['low'].iloc[i-1]
        open_prev = df['open'].iloc[i-1]
        high = df['high'].iloc[i]
        low = df['low'].iloc[i]
        open = df['open'].iloc[i] 

        if (
            close_prev < close and
            high_prev < high and
            high_prev < close and
            low_prev < low and  # Close of previous candle is lower than present candle
            close - open > (close_prev - open_prev) and
            # close - open < 3* (close_prev - open_prev) and
            open < close and 
            open_prev < close_prev and
            # (close_prev - open_prev) < (open_prev - low_prev) and
            high_prev - close_prev < close_prev - open_prev and  # High - Close < Close - Open for previous candle
            high - close < close - open  # High - Close < Close - Open for present candle
        ):
            df.at[i, 'trend'] = 1

        elif (
            close_prev > close and
            high_prev > high and
            low_prev > low and   # Close of previous candle is higher than present candle
            low_prev > close and   # Close of previous candle is higher than present candle
            open > close and 
            open_prev > close_prev and
            # (open_prev - close_prev) < (high_prev - open_prev) and
            open - close > (open_prev - close_prev) and
            # open - close < 3* (open_prev - close_prev) and
            close_prev - low_prev < open_prev - close_prev and  # Close - Low < Open - Close for previous candle
            close - low < open - close  # Close - Low < Open - Close for present candle
        ):
            df.at[i, 'trend'] = -1

    return df



# ############################################################################################################################################################################################

class CusTrend_coralTrend_Adx_EMA_Oct_1h(IStrategy):

    
    # 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 = '1h'

    # Can this strategy go short?
    can_short = True



    '''

 




sudo docker-compose run freqtrade backtesting --strategy CusTrend_coralTrend_Adx_EMA_Oct_1h -i 1h --export trades --breakdown month --timerange 20210101-20230815
2023-10-03 15:12:26,838 - freqtrade - INFO - freqtrade 2023.7
2023-10-03 15:12:34,359 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'stake_amount' with value in config file: 200.
2023-10-03 15:12:34,359 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'unfilledtimeout' with value in config file: {'entry': 10, 'exit': 10, 'exit_timeout_count': 0, 'unit': 'minutes'}.
2023-10-03 15:12:34,359 - freqtrade.resolvers.strategy_resolver - INFO - Override strategy 'max_open_trades' with value in config file: 4.
2023-10-03 15:12:34,360 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using minimal_roi: {'0': 0.101, '373': 0.068, '1088': 0.025, '1336': 0}
2023-10-03 15:12:34,360 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using timeframe: 1h
2023-10-03 15:12:34,360 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stoploss: -0.347
2023-10-03 15:12:34,360 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop: True
2023-10-03 15:12:34,360 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive: 0.01
2023-10-03 15:12:34,360 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_stop_positive_offset: 0.012
2023-10-03 15:12:34,360 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using trailing_only_offset_is_reached: True
2023-10-03 15:12:34,360 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using use_custom_stoploss: False
2023-10-03 15:12:34,360 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using process_only_new_candles: True
2023-10-03 15:12:34,360 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_types: {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False}
2023-10-03 15:12:34,361 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using order_time_in_force: {'entry': 'GTC', 'exit': 'GTC'}
2023-10-03 15:12:34,361 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_currency: USDT
2023-10-03 15:12:34,361 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using stake_amount: 200
2023-10-03 15:12:34,361 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using protections: [{'method': 'MaxDrawdown', 'lookback_period_candles': 2, 'trade_limit': 1, 'stop_duration_candles': 4, 'max_allowed_drawdown': 0.1}, {'method': 'StoplossGuard', 'lookback_period_candles': 8, 'trade_limit': 1, 'stop_duration_candles': 4, 'only_per_pair': False}]
2023-10-03 15:12:34,361 - freqtrade.resolvers.strategy_resolver - INFO - Strategy using startup_candle_count: 30


2023-10-03 15:14:52,156 - freqtrade.optimize.backtesting - INFO - Running backtesting for Strategy CusTrend_coralTrend_Adx_EMA_Oct_1h
2023-10-03 15:14:52,156 - freqtrade.strategy.hyper - INFO - Strategy Parameter: adx_long_max = 50
2023-10-03 15:14:52,157 - freqtrade.strategy.hyper - INFO - Strategy Parameter: adx_long_min = 21
2023-10-03 15:14:52,157 - freqtrade.strategy.hyper - INFO - Strategy Parameter: adx_short_max = 51
2023-10-03 15:14:52,157 - freqtrade.strategy.hyper - INFO - Strategy Parameter: adx_short_min = 13
2023-10-03 15:14:52,157 - freqtrade.strategy.hyper - INFO - Strategy Parameter: ema_period = 142
2023-10-03 15:14:52,157 - freqtrade.strategy.hyper - INFO - Strategy Parameter: leverage_num = 4
2023-10-03 15:14:52,158 - freqtrade.strategy.hyper - INFO - Strategy Parameter: volume_check = 22
2023-10-03 15:14:52,158 - freqtrade.strategy.hyper - INFO - Strategy Parameter: sell_shift = 5
2023-10-03 15:14:52,158 - freqtrade.strategy.hyper - INFO - Strategy Parameter: sell_shift_short = 5
2023-10-03 15:14:52,158 - freqtrade.strategy.hyper - INFO - Strategy Parameter: volume_check_exit = 19
2023-10-03 15:14:52,159 - freqtrade.strategy.hyper - INFO - Strategy Parameter: max_allowed_drawdown = 0.1
2023-10-03 15:14:52,159 - freqtrade.strategy.hyper - INFO - Strategy Parameter: max_drawdown_lookback = 2
2023-10-03 15:14:52,159 - freqtrade.strategy.hyper - INFO - Strategy Parameter: max_drawdown_stop_duration = 4
2023-10-03 15:14:52,159 - freqtrade.strategy.hyper - INFO - Strategy Parameter: max_drawdown_trade_limit = 1
2023-10-03 15:14:52,159 - freqtrade.strategy.hyper - INFO - Strategy Parameter: stoploss_guard_lookback = 8
2023-10-03 15:14:52,159 - freqtrade.strategy.hyper - INFO - Strategy Parameter: stoploss_guard_stop_duration = 4
2023-10-03 15:14:52,160 - freqtrade.strategy.hyper - INFO - Strategy Parameter: stoploss_guard_trade_limit = 1


=========================================================== ENTER TAG STATS ===========================================================
|   TAG |   Entries |   Avg Profit % |   Cum Profit % |   Tot Profit USDT |   Tot Profit % |   Avg Duration |   Win  Draw  Loss  Win% |
|-------+-----------+----------------+----------------+-------------------+----------------+----------------+-------------------------|
| TOTAL |     19512 |           1.53 |       29904.97 |         59666.204 |        5966.62 |        1:27:00 | 15205     0  4307  77.9 |
======================================================= EXIT REASON STATS ========================================================
|        Exit Reason |   Exits |   Win  Draws  Loss  Win% |   Avg Profit % |   Cum Profit % |   Tot Profit USDT |   Tot Profit % |
|--------------------+---------+--------------------------+----------------+----------------+-------------------+----------------|
| trailing_stop_loss |   13767 |  11305     0  2462  82.1 |           1.5  |       20712.3  |         41334.9   |        5178.08 |
|                roi |    3912 |   3899     0    13  99.7 |           9.93 |       38849.4  |         77530.3   |        9712.34 |
|          exit_long |     846 |      1     0   845   0.1 |         -14.05 |      -11885.5  |        -23720.7   |       -2971.38 |
|         exit_short |     723 |      0     0   723     0 |         -11.59 |       -8378.26 |        -16724.4   |       -2094.57 |
|          stop_loss |     259 |      0     0   259     0 |         -34.87 |       -9030.74 |        -18029.8   |       -2257.69 |
|        liquidation |       4 |      0     0     4     0 |         -89.06 |        -356.25 |          -712.153 |         -89.06 |
|         force_exit |       1 |      0     0     1     0 |          -5.95 |          -5.95 |           -11.907 |          -1.49 |
======================= MONTH BREAKDOWN ========================
|      Month |   Tot Profit USDT |   Wins |   Draws |   Losses |
|------------+-------------------+--------+---------+----------|
| 31/01/2021 |          2053.09  |    377 |       0 |       93 |
| 28/02/2021 |          2911.42  |    532 |       0 |       93 |
| 31/03/2021 |          3454.87  |    619 |       0 |      153 |
| 30/04/2021 |          3404.27  |    597 |       0 |      135 |
| 31/05/2021 |          2013.48  |    531 |       0 |      120 |
| 30/06/2021 |          2163.77  |    479 |       0 |      114 |
| 31/07/2021 |          1982.28  |    543 |       0 |      135 |
| 31/08/2021 |          1980.32  |    557 |       0 |      155 |
| 30/09/2021 |          3063.71  |    483 |       0 |      107 |
| 31/10/2021 |          2045.31  |    540 |       0 |      162 |
| 30/11/2021 |          3190.49  |    629 |       0 |      152 |
| 31/12/2021 |          2820.53  |    540 |       0 |      123 |
| 31/01/2022 |          2365.55  |    504 |       0 |      116 |
| 28/02/2022 |          1496.26  |    449 |       0 |      145 |
| 31/03/2022 |          1702.11  |    521 |       0 |      168 |
| 30/04/2022 |          2633.78  |    522 |       0 |      147 |
| 31/05/2022 |          2176.6   |    514 |       0 |      120 |
| 30/06/2022 |          2342.53  |    495 |       0 |      138 |
| 31/07/2022 |          2140.83  |    483 |       0 |      152 |
| 31/08/2022 |          1366.66  |    429 |       0 |      146 |
| 30/09/2022 |           956.942 |    417 |       0 |      148 |
| 31/10/2022 |          1152.5   |    405 |       0 |      146 |
| 30/11/2022 |          1286.25  |    424 |       0 |      126 |
| 31/12/2022 |           883.354 |    418 |       0 |      155 |
| 31/01/2023 |          1521.18  |    462 |       0 |      138 |
| 28/02/2023 |          2311.52  |    491 |       0 |      113 |
| 31/03/2023 |           664.388 |    428 |       0 |      154 |
| 30/04/2023 |          1015.03  |    413 |       0 |      129 |
| 31/05/2023 |           677.527 |    389 |       0 |      134 |
| 30/06/2023 |          1286.08  |    453 |       0 |      133 |
| 31/07/2023 |           478.821 |    368 |       0 |      177 |
| 31/08/2023 |           124.745 |    193 |       0 |       80 |
=================== SUMMARY METRICS ====================
| Metric                      | Value                  |
|-----------------------------+------------------------|
| Backtesting from            | 2021-01-02 06:00:00    |
| Backtesting to              | 2023-08-15 00:00:00    |
| Max open trades             | 4                      |
|                             |                        |
| Total/Daily Avg Trades      | 19512 / 20.45          |
| Starting balance            | 1000 USDT              |
| Final balance               | 60666.204 USDT         |
| Absolute profit             | 59666.204 USDT         |
| Total profit %              | 5966.62%               |
| CAGR %                      | 381.01%                |
| Sortino                     | 63.30                  |
| Sharpe                      | 74.94                  |
| Calmar                      | 8256.74                |
| Profit factor               | 1.72                   |
| Expectancy (Ratio)          | 3.06 (0.16)            |
| Trades per day              | 20.45                  |
| Avg. daily profit %         | 6.25%                  |
| Avg. stake amount           | 199.529 USDT           |
| Total trade volume          | 3893209.107 USDT       |
|                             |                        |
| Long / Short                | 11155 / 8357           |
| Total profit Long %         | 3511.16%               |
| Total profit Short %        | 2455.46%               |
| Absolute profit Long        | 35111.567 USDT         |
| Absolute profit Short       | 24554.637 USDT         |
|                             |                        |
| Best Pair                   | CRV/USDT:USDT 1223.38% |
| Worst Pair                  | CHZ/USDT:USDT -64.24%  |
| Best trade                  | BEL/USDT:USDT 13.43%   |
| Worst trade                 | FTM/USDT:USDT -92.36%  |
| Best day                    | 416.069 USDT           |
| Worst day                   | -295.425 USDT          |
| Days win/draw/lose          | 718 / 0 / 231          |
| Avg. Duration Winners       | 0:53:00                |
| Avg. Duration Loser         | 3:28:00                |
| Max Consecutive Wins / Loss | 41 / 9                 |
| Rejected Entry signals      | 34147                  |
| Entry/Exit Timeouts         | 0 / 5491               |
|                             |                        |
| Min balance                 | 1020.199 USDT          |
| Max balance                 | 60788.167 USDT         |
| Max % of account underwater | 22.01%                 |
| Absolute Drawdown (Account) | 1.45%                  |
| Absolute Drawdown           | 597.565 USDT           |
| Drawdown high               | 40292.12 USDT          |
| Drawdown low                | 39694.556 USDT         |
| Drawdown Start              | 2022-05-11 12:00:00    |
| Drawdown End                | 2022-05-11 13:00:00    |
| Market change               | 23.42%                 |
========================================================

Backtested 2021-01-02 06:00:00 -> 2023-08-15 00:00:00 | Max open trades : 4
==================================================================================== STRATEGY SUMMARY ====================================================================================
|                           Strategy |   Entries |   Avg Profit % |   Cum Profit % |   Tot Profit USDT |   Tot Profit % |   Avg Duration |   Win  Draw  Loss  Win% |            Drawdown |
|------------------------------------+-----------+----------------+----------------+-------------------+----------------+----------------+-------------------------+---------------------|
| CusTrend_coralTrend_Adx_EMA_Oct_1h |     19512 |           1.53 |       29904.97 |         59666.204 |        5966.62 |        1:27:00 | 15205     0  4307  77.9 | 597.565 USDT  1.45% |
==========================================================================================================================================================================================


    '''

    # Minimal ROI designed for the strategy.
    minimal_roi = {'0': 0.101, '373': 0.068, '1088': 0.025, '1336': 0}

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.347

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.012
    trailing_only_offset_is_reached = True

    # 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

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30

    #leverage here
    leverage_optimize = True
    leverage_num = IntParameter(low=1, high=5, default=5, space='buy', optimize=leverage_optimize)

    # Strategy parameters
    parameters_yes = True
    parameters_no = False

    '''
    adx_long_min = IntParameter(4, 21, default=9, space="buy", optimize = parameters_yes)
    adx_long_max = IntParameter(21, 56, default=38, space="buy", optimize = parameters_yes)
    adx_short_min = IntParameter(4, 21, default=21, space="buy", optimize = parameters_yes)
    adx_short_max = IntParameter(20, 56, default=29, space="buy", optimize = parameters_yes)

    ema_period = IntParameter(22, 200, default=175, space="buy", optimize= parameters_yes)

    volume_check = IntParameter(15, 45, default=36, space="buy", optimize= parameters_yes)
    volume_check_exit = IntParameter(15, 45, default=35, space="sell", optimize= parameters_yes)

    sell_shift = IntParameter(1, 6, default=6, space="sell", optimize= parameters_yes)
    sell_shift_short = IntParameter(1, 6, default=6, space="sell", optimize= parameters_yes) 
    
    '''
    adx_long_min = IntParameter(4, 21, default=21, space="buy", optimize = parameters_yes)
    adx_long_max = IntParameter(21, 56, default=50, space="buy", optimize = parameters_yes)
    adx_short_min = IntParameter(4, 21, default=13, space="buy", optimize = parameters_yes)
    adx_short_max = IntParameter(20, 56, default=51, space="buy", optimize = parameters_yes)

    ema_period = IntParameter(22, 200, default=142, space="buy", optimize= parameters_yes)

    volume_check = IntParameter(15, 45, default=22, space="buy", optimize= parameters_yes)
    volume_check_exit = IntParameter(15, 45, default=19, space="sell", optimize= parameters_yes)

    sell_shift = IntParameter(1, 6, default=5, space="sell", optimize= parameters_yes)
    sell_shift_short = IntParameter(1, 6, default=5, space="sell", optimize= parameters_yes)  # **added this at random-state 11165



    protect_optimize = True
    # cooldown_lookback = IntParameter(1, 40, default=4, space="protection", optimize=protect_optimize)
    max_drawdown_lookback = IntParameter(1, 50, default=2, space="protection", optimize=protect_optimize)
    max_drawdown_trade_limit = IntParameter(1, 3, default=1, space="protection", optimize=protect_optimize)
    max_drawdown_stop_duration = IntParameter(1, 50, default=4, space="protection", optimize=protect_optimize)
    max_allowed_drawdown = DecimalParameter(0.05, 0.30, default=0.10, decimals=2, space="protection",
                                            optimize=protect_optimize)
    stoploss_guard_lookback = IntParameter(1, 50, default=8, space="protection", optimize=protect_optimize)
    stoploss_guard_trade_limit = IntParameter(1, 3, default=1, space="protection", optimize=protect_optimize)
    stoploss_guard_stop_duration = IntParameter(1, 50, default=4, space="protection", optimize=protect_optimize)

    @property
    def protections(self):
        return [
            # {
            #     "method": "CooldownPeriod",
            #     "stop_duration_candles": self.cooldown_lookback.value
            # },
            {
                "method": "MaxDrawdown",
                "lookback_period_candles": self.max_drawdown_lookback.value,
                "trade_limit": self.max_drawdown_trade_limit.value,
                "stop_duration_candles": self.max_drawdown_stop_duration.value,
                "max_allowed_drawdown": self.max_allowed_drawdown.value
            },
            {
                "method": "StoplossGuard",
                "lookback_period_candles": self.stoploss_guard_lookback.value,
                "trade_limit": self.stoploss_guard_trade_limit.value,
                "stop_duration_candles": self.stoploss_guard_stop_duration.value,
                "only_per_pair": False
            }
        ]

 


    # ema_long = IntParameter(50, 250, default=100, space="buy")
    # ema_short = IntParameter(50, 250, default=100, space="buy")

    # sell_rsi = IntParameter(60, 90, default=70, space="sell")

    # 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'
    }
    
    @property
    def plot_config(self):
        return {
            # Main plot indicators (Moving averages, ...)
            'main_plot': {
                'tema': {},
                'sar': {'color': 'white'},
            },
            'subplots': {
                # Subplots - each dict defines one additional plot
                "MACD": {
                    'macd': {'color': 'blue'},
                    'macdsignal': {'color': 'orange'},
                },
                "RSI": {
                    'rsi': {'color': 'red'},
                }
            }
        }

    def informative_pairs(self):
        
        # get access to all pairs available in whitelist.
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, '4h') for pair in pairs]
        
        return informative_pairs
    

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if not self.dp:
            # Don't do anything if DataProvider is not available.
            return dataframe
        
        inf_tf = '4h'
        # Get the informative pair
        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
        # Get the 14 day rsi
        informative['ema'] = ta.EMA(informative['close'], timeperiod=50)

        # Use the helper function merge_informative_pair to safely merge the pair
        # Automatically renames the columns and merges a shorter timeframe dataframe and a longer timeframe informative pair
        # use ffill to have the 1d value available in every row throughout the day.
        # Without this, comparisons between columns of the original and the informative pair would only work once per day.
        # Full documentation of this method, see below
        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)

        L_determine_trend_strategy = determine_trend(df = dataframe)
        dataframe['trend'] = L_determine_trend_strategy['trend']


        # long_coral_trend = coral_trend(df=dataframe, ema_period = self.ct_ema_period.value, cci_period=self.ct_cci_period.value, cci_threshold = self.ct_cci_threshold.value)

        # dataframe['ct_ema_fast']= long_coral_trend['ema_fast']
        # dataframe['ct_ema_slow'] = long_coral_trend['ema_slow']
        # dataframe['ct_cci'] = long_coral_trend['cci']
        # dataframe['ct_coral_trend'] = long_coral_trend['coral_trend'] 


        # dataframe['psar'] = calculate_psar(df=dataframe, af_start=self.af_start_range.value, af_max=self.af_max_range.value)
        
        # Parabolic SAR
        dataframe['psar'] = ta.SAR(dataframe)

        

        # ADX
        dataframe['adx'] = ta.ADX(dataframe)
        
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe)

        # EMA
        dataframe['ema'] = ta.EMA(dataframe['close'], timeperiod=self.ema_period.value)

        # Volume Weighted
        dataframe['volume_mean'] = dataframe['volume'].rolling(self.volume_check.value).mean().shift(1)
        dataframe['volume_mean_exit'] = dataframe['volume'].rolling(self.volume_check_exit.value).mean().shift(1)


        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe.loc[
            (
                        (dataframe['adx'] > self.adx_long_min.value) & # trend strength confirmation
                        (dataframe['adx'] < self.adx_long_max.value) & # trend strength confirmation
                        # (dataframe['adx'] > dataframe['adx'].shift(1)) & 
                        (dataframe['psar'] < dataframe['close']) & 
                        (dataframe['ema'] < dataframe['close']) & 
                        (dataframe['ema_4h'] < dataframe['close']) & 
                        # (dataframe['eth_ema_15m'] < dataframe['close']) & 
                        # (dataframe['btc_ema_30m'] < dataframe['close']) &  
                        # (dataframe['eth_ema_30m'] < dataframe['close']) & 
                        (dataframe['trend'] == 1) &
                        # (dataframe['ct_coral_trend'] > 0) & 
                        (dataframe['rsi'] > 50) &
                        # (dataframe['volume'] > dataframe['volume'].shift(1)) &
                        (dataframe['volume'] > dataframe['volume_mean'])

            ),
            'enter_long'] = 1

        dataframe.loc[
            (
                        (dataframe['adx'] > self.adx_short_min.value) & # trend strength confirmation
                        (dataframe['adx'] < self.adx_short_max.value) & # trend strength confirmation
                        # (dataframe['adx'] > dataframe['adx'].shift(1)) & 
                        (dataframe['psar'] > dataframe['close']) & # trend reversal confirmation
                        (dataframe['ema'] > dataframe['close']) & # trend confirmation
                        (dataframe['ema_4h'] > dataframe['close']) & 
                        # (dataframe['btc_ema_15m'] > dataframe['close']) & 
                        # (dataframe['eth_ema_15m'] > dataframe['close']) & 
                        # (dataframe['btc_ema_30m'] > dataframe['close']) & 
                        # (dataframe['eth_ema_30m'] > dataframe['close']) & 
                        (dataframe['trend'] == -1) & 
                        # (dataframe['ct_coral_trend'] < 0) & 
                        (dataframe['rsi'] < 50) & # momentum indicator
                        # (dataframe['volume'] > dataframe['volume'].shift(1)) &
                        (dataframe['volume'] > dataframe['volume_mean']) # volume weighted indicator
            ),
            'enter_short'] = 1
        
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        conditions_long = []
        conditions_short = []
        dataframe.loc[:, 'exit_tag'] = ''

        exit_long = (
                (dataframe['close'] < dataframe['low'].shift(self.sell_shift.value)) &
                (dataframe['volume'] > dataframe['volume_mean_exit'])
        )

        exit_short = (
                (dataframe['close'] > dataframe['high'].shift(self.sell_shift_short.value)) &
                (dataframe['volume'] > dataframe['volume_mean_exit'])
        )


        conditions_short.append(exit_short)
        dataframe.loc[exit_short, 'exit_tag'] += 'exit_short'


        conditions_long.append(exit_long)
        dataframe.loc[exit_long, 'exit_tag'] += 'exit_long'


        if conditions_long:
            dataframe.loc[
                reduce(lambda x, y: x | y, conditions_long),
                'exit_long'] = 1

        if conditions_short:
            dataframe.loc[
                reduce(lambda x, y: x | y, conditions_short),
                'exit_short'] = 1
            
        return dataframe

    def leverage(self, pair: str, current_time: datetime, current_rate: float,
                 proposed_leverage: float, max_leverage: float, side: str,
                 **kwargs) -> float:

        return self.leverage_num.value