init

user_data/strategies/MACDStrategy.py

@@ -0,0 +1,116 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+# flake8: noqa: F401
+# isort: skip_file
+# --- Do not remove these imports ---
+import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta, timezone
+from pandas import DataFrame
+from typing import Optional, Union
+from freqtrade.strategy import (
+    IStrategy,
+    Trade,
+    Order,
+    PairLocks,
+    informative,  # @informative decorator
+    # Hyperopt Parameters
+    BooleanParameter,
+    CategoricalParameter,
+    DecimalParameter,
+    IntParameter,
+    RealParameter,
+    # timeframe helpers
+    timeframe_to_minutes,
+    timeframe_to_next_date,
+    timeframe_to_prev_date,
+    # Strategy helper functions
+    merge_informative_pair,
+    stoploss_from_absolute,
+    stoploss_from_open,
+)
+import logging  
+  
+logger = logging.getLogger(__name__) 
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta
+from technical import qtpylib
+
+
+class MACDStrategy(IStrategy):
+    # 策略参数
+    INTERFACE_VERSION = 3
+
+    minimal_roi = {"0": 100}
+    stoploss = -1
+    trailing_stop = False
+    timeframe = '15m'
+
+    use_exit_signal = True
+    exit_profit_only = False
+    ignore_roi_if_entry_signal = False
+
+    def TD(self, dataframe:DataFrame):
+        close = dataframe['close'].to_list()
+        td = [0,0,0,0]
+        up = 0
+        down = 0
+        for i in range(4, len(close)):
+            if close[i] > close[i-4]:
+                up += 1
+                down = 0
+                td.append(up)
+            else:
+                down -= 1
+                up = 0
+                td.append(down)
+        return td
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        
+        macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9,)
+        dataframe["macd"] = macd["macd"]
+        dataframe["macdsignal"] = macd["macdsignal"]
+        dataframe["macdhist"] = macd["macdhist"]
+        dataframe['cci'] = ta.CCI(dataframe, 26)
+        dataframe['TD'] = self.TD(dataframe)
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # 入场：1d与4h CCI < -100，且4h CCI上升（当前 > 前一根）
+        dataframe.loc[
+            (
+                # (dataframe['macdhist'] < 0) &
+                # (dataframe['macdhist'] > dataframe['macdhist'].shift(1)) &
+                # (dataframe['macdsignal'] < 0) &
+                # (dataframe['macd'] < dataframe['macdsignal']) &
+                # (dataframe['cci'] < -100) &
+                # (dataframe['cci'].shift(1) < dataframe['cci']) &
+                # (dataframe['macdhist'] < 0) &
+                (dataframe['TD'] == 1) &
+                (dataframe['volume'] > 0)
+            ),
+            'enter_long',
+        ] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # 离场：1d与4h CCI > 100，且4h CCI下降（当前 < 前一根）
+        dataframe.loc[
+            (
+                # (dataframe['macdhist'] > 0) &
+                # (dataframe['macdhist'] < dataframe['macdhist'].shift(1)) &
+                # (dataframe['macdsignal'] > 0) &
+                # (dataframe['macd'] > dataframe['macdsignal']) &
+                # (dataframe['cci'] > 100 )&
+                # (dataframe['cci'].shift(1) > dataframe['cci']) &
+                # (dataframe['macdhist'] > 0) &
+                (dataframe['TD'] == -1) &
+                (dataframe['volume'] > 0)
+            ),
+            'exit_long',
+        ] = 1
+
+        return dataframe
+

user_data/strategies/SimpleRSIStrategy_Fixed.py

@@ -0,0 +1,331 @@
+# strategies/SimpleRSIStrategy_Fixed.py
+
+import talib.abstract as ta
+import pandas as pd
+import numpy as np
+from freqtrade.strategy import IStrategy
+from freqtrade.persistence import Trade
+from typing import Dict, List, Optional
+from functools import reduce
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class SimpleRSIStrategyFixed(IStrategy):
+    """
+    修复版简化 RSI 策略：结合 EMA 交叉 + RSI 超卖 + 上升趋势
+    
+    修复内容：
+    1. 添加缺失的导入语句
+    2. 修复除零错误
+    3. 优化性能
+    4. 改进代码可读性
+    5. 添加数据验证
+    """
+    INTERFACE_VERSION = 3
+
+    # Can this strategy go short?
+    can_short: bool = False
+
+    # Minimal ROI designed for the strategy.
+    # 基于x.py策略，使用更保守的ROI设置
+    # minimal_roi = {
+    #     "30": 0.3,  # 20% 目标收益
+    #     # ""
+    # }
+
+    # Optimal stoploss designed for the strategy.
+    # 基于x.py策略的卖出条件：跌破MA60且跌幅超过10%
+    stoploss = -0.05
+
+    # 超参数定义
+    minimal_roi = {
+        "0": 100
+    }
+
+    timeframe = '1d'
+
+    # 策略参数
+    rsi_period_short = 6
+    rsi_period_long = 12
+    ema_period_short = 10
+    ema_period_long = 20
+    rsi_oversold_threshold = 20
+    rsi_oversold_tolerance = 1.05
+    ema_trend_threshold = 0.01
+    ema_breakout_threshold = 0.02
+    ema_separation_threshold = 0.02
+
+    def populate_indicators(self, dataframe: pd.DataFrame, metadata: Dict) -> pd.DataFrame:
+        """
+        添加技术指标到数据框
+        """
+        try:
+            # RSI 指标 (6, 12) 及位移
+            dataframe['rsi_6'] = ta.RSI(dataframe['close'], timeperiod=6)
+            dataframe['rsi_12'] = ta.RSI(dataframe['close'], timeperiod=12)
+            for i in range(1, 6):
+                dataframe[f'rsi_6_shift{i}'] = dataframe['rsi_6'].shift(i)
+                dataframe[f'rsi_12_shift{i}'] = dataframe['rsi_12'].shift(i)
+
+            # EMA 指标 (10, 20, 30) 及位移
+            dataframe['ema_10'] = ta.EMA(dataframe['close'], timeperiod=10)
+            dataframe['ema_20'] = ta.EMA(dataframe['close'], timeperiod=20)
+            dataframe['ema_30'] = ta.EMA(dataframe['close'], timeperiod=30)
+            for i in range(1, 6):
+                dataframe[f'ema_10_shift{i}'] = dataframe['ema_10'].shift(i)
+                dataframe[f'ema_20_shift{i}'] = dataframe['ema_20'].shift(i)
+                dataframe[f'ema_30_shift{i}'] = dataframe['ema_30'].shift(i)
+
+            # 方便条件判断的差值与比率（带除零保护）
+            dataframe['ema_10_20_diff'] = dataframe['ema_10'] - dataframe['ema_20']
+            dataframe['ema_10_20_ratio'] = np.where(
+                dataframe['ema_20'] != 0,
+                dataframe['ema_10_20_diff'] / dataframe['ema_20'],
+                0
+            )
+            for i in range(1, 6):
+                dataframe[f'ema_10_20_diff_shift{i}'] = dataframe['ema_10_20_diff'].shift(i)
+                dataframe[f'ema_10_20_ratio_shift{i}'] = np.where(
+                    dataframe[f'ema_20_shift{i}'] != 0,
+                    dataframe[f'ema_10_20_diff_shift{i}'] / dataframe[f'ema_20_shift{i}'],
+                    0
+                )
+            dataframe.to_csv("rsi_eth2.csv", index=False, encoding='utf-8-sig')
+            logger.debug(f"Indicators populated successfully for {metadata.get('pair', 'unknown')}")
+            return dataframe
+
+        except Exception as e:
+            logger.error(f"Error populating indicators: {e}")
+            return dataframe
+
+    def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: Dict) -> pd.DataFrame:
+        """
+        生成买入信号
+        """
+        try:
+            # 初始化信号列
+            dataframe.loc[:, 'enter_long'] = 0
+
+            # 数据验证
+            required_columns = [
+                'ema_10', 'ema_20', 'ema_30',
+                'rsi_6', 'rsi_12',
+                'ema_10_shift1', 'ema_10_shift2', 'ema_10_shift3', 'ema_10_shift4', 'ema_10_shift5',
+                'ema_20_shift1', 'ema_20_shift2', 'ema_20_shift3', 'ema_20_shift4', 'ema_20_shift5',
+                'rsi_6_shift1', 'rsi_6_shift2', 'rsi_6_shift3',
+                'rsi_12_shift1', 'rsi_12_shift2', 'rsi_12_shift3',
+                'ema_10_20_ratio', 'ema_10_20_ratio_shift5',
+                'ema_10_20_diff', 'ema_10_20_diff_shift1', 'ema_10_20_diff_shift2', 'ema_10_20_diff_shift3', 'ema_10_20_diff_shift4', 'ema_10_20_diff_shift5',
+                'close'
+            ]
+            if not all(col in dataframe.columns for col in required_columns):
+                logger.warning(f"Missing required columns: {required_columns}")
+                return dataframe
+
+            # 按给定逻辑实现买入条件
+            ema_10 = dataframe['ema_10']
+            ema_20 = dataframe['ema_20']
+            ema_10_s1 = dataframe['ema_10_shift1']
+            ema_10_s2 = dataframe['ema_10_shift2']
+            ema_10_s3 = dataframe['ema_10_shift3']
+            ema_10_s4 = dataframe['ema_10_shift4']
+            ema_10_s5 = dataframe['ema_10_shift5']
+            ema_20_s1 = dataframe['ema_20_shift1']
+            ema_20_s2 = dataframe['ema_20_shift2']
+            ema_20_s3 = dataframe['ema_20_shift3']
+            ema_20_s4 = dataframe['ema_20_shift4']
+            ema_20_s5 = dataframe['ema_20_shift5']
+            rsi_6 = dataframe['rsi_6']
+            rsi_12 = dataframe['rsi_12']
+            rsi_6_s1 = dataframe['rsi_6_shift1']
+            rsi_6_s2 = dataframe['rsi_6_shift2']
+            rsi_6_s3 = dataframe['rsi_6_shift3']
+            rsi_12_s1 = dataframe['rsi_12_shift1']
+            rsi_12_s2 = dataframe['rsi_12_shift2']
+            rsi_12_s3 = dataframe['rsi_12_shift3']
+            close = dataframe['close']
+
+            # 比率与安全除法
+            ratio_now = np.where(ema_20 != 0, (ema_10 - ema_20) / ema_20, 0)
+            ratio_s5 = np.where(ema_20_s5 != 0, (ema_10_s5 - ema_20_s5) / ema_20_s5, 0)
+            ratio_pos_and_small = (ratio_s5 != 0) & ((ratio_now / ratio_s5) > 0) & ((ratio_now / ratio_s5) < 0.2)
+
+            cond_1 = (
+                (
+                    (ema_10 > ema_20)
+                    & (ema_10_s1 > ema_20_s1)
+                    & (ema_10_s1 > ema_10_s2)
+                    & (ema_10 > ema_10_s1)
+                    & (ratio_now > 0.01)
+                )
+                |
+                (
+                    (ema_10 > 1.02 * ema_10_s1)
+                    & (ema_10_s1 > ema_10_s2)
+                    & (ema_10 > ema_20)
+                )
+                |
+                (
+                    ratio_pos_and_small
+                    & ((ema_20 - ema_10) < (ema_20_s1 - ema_10_s1))
+                    & ((ema_20 - ema_10) < (ema_20_s2 - ema_10_s2))
+                    & ((ema_20 - ema_10) < (ema_20_s3 - ema_10_s3))
+                    & ((ema_20 - ema_10) < (ema_20_s4 - ema_10_s4))
+                    & ((ema_20 - ema_10) < (ema_20_s5 - ema_10_s5))
+                    & (ema_20_s5 > ema_10_s5)
+                    & (ema_10 < close)
+                )
+            )
+
+            cond_rsi = (
+                (rsi_6 > 1.5 * rsi_6_s1)
+                & (rsi_6 > 0.95 * rsi_12)
+                & (rsi_6_s1 < 20 * 1.05)
+                & (rsi_6_s2 < 20 * 1.05)
+                & (rsi_6_s3 < 20 * 1.05)
+                & (rsi_12_s1 < 25 * 1.05)
+                & (rsi_12_s2 < 25 * 1.05)
+                & (rsi_12_s3 < 25 * 1.05)
+            )
+
+            buy_condition = cond_1 | cond_rsi
+
+            dataframe.loc[buy_condition, 'enter_long'] = 1
+            
+            # 记录信号统计
+            signal_count = buy_condition.sum()
+            if signal_count > 0:
+                logger.info(f"Generated {signal_count} buy signals for {metadata.get('pair', 'unknown')}")
+
+            return dataframe
+
+        except Exception as e:
+            logger.error(f"Error in populate_entry_trend: {e}")
+            return dataframe
+
+    def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: Dict) -> pd.DataFrame:
+        """
+        生成卖出信号
+        """
+        try:
+            dataframe.loc[:, 'exit_long'] = 0
+
+            # 数据验证
+            required_columns = [
+                'ema_10', 'ema_20',
+                'ema_10_shift1', 'ema_10_shift2', 'ema_10_shift3', 'ema_10_shift4', 'ema_10_shift5',
+                'ema_20_shift1', 'ema_20_shift2', 'ema_20_shift3', 'ema_20_shift4', 'ema_20_shift5',
+                'ema_10_20_ratio', 'ema_10_20_ratio_shift4',
+            ]
+            if not all(col in dataframe.columns for col in required_columns):
+                logger.warning(f"Missing required columns: {required_columns}")
+                return dataframe
+
+            ema_10 = dataframe['ema_10']
+            ema_20 = dataframe['ema_20']
+            ema_10_s1 = dataframe['ema_10_shift1']
+            ema_10_s2 = dataframe['ema_10_shift2']
+            ema_10_s3 = dataframe['ema_10_shift3']
+            ema_10_s4 = dataframe['ema_10_shift4']
+            ema_10_s5 = dataframe['ema_10_shift5']
+            ema_20_s1 = dataframe['ema_20_shift1']
+            ema_20_s2 = dataframe['ema_20_shift2']
+            ema_20_s3 = dataframe['ema_20_shift3']
+            ema_20_s4 = dataframe['ema_20_shift4']
+            ema_20_s5 = dataframe['ema_20_shift5']
+
+            ratio_now = np.where(ema_20 != 0, (ema_10 - ema_20) / ema_20, 0)
+            ratio_s4 = np.where(ema_20_s4 != 0, (ema_10_s4 - ema_20_s4) / ema_20_s4, 0)
+            ratio_pos_and_lt03 = (ratio_s4 != 0) & ((ratio_now / ratio_s4) > 0) & ((ratio_now / ratio_s4) < 0.3)
+
+            cond_block_1 = (
+                (
+                    (ema_10 < ema_20)
+                    & (ema_10_s1 < ema_20_s1)
+                    & (ema_10_s2 < ema_20_s2)
+                    & (ema_10_s5 > ema_20_s5)
+                )
+                |
+                (
+                    (ema_10 < 0.995 * ema_10_s1)
+                    & (ema_10_s1 < ema_10_s2)
+                    & (ema_10 < ema_20)
+                )
+            )
+
+            cond_block_2 = (
+                (ratio_now < 0.02)
+                & ratio_pos_and_lt03
+                & (ema_10_s4 > ema_20_s4)
+                & (ema_10 < ema_10_s1)
+                & (ema_10 < ema_10_s2)
+                & (ema_10 < ema_10_s3)
+                & (ema_10 < ema_10_s4)
+            )
+
+            widening_now_vs_history = (
+                ((ema_20 - ema_10) > (ema_20_s1 - ema_10_s1))
+                & ((ema_20 - ema_10) > (ema_20_s2 - ema_10_s2))
+                & ((ema_20 - ema_10) > (ema_20_s3 - ema_10_s3))
+                & ((ema_20 - ema_10) > (ema_20_s4 - ema_10_s4))
+                & ((ema_20 - ema_10) > (ema_20_s5 - ema_10_s5))
+            )
+
+            sell_condition = (
+                (
+                    cond_block_1
+                    & (ema_10 < ema_10_s1)
+                    & (ema_10 < ema_10_s2)
+                    & (ema_10 < ema_10_s3)
+                    & (ema_10 < ema_10_s4)
+                    & (ema_10 < ema_10_s5)
+                    & widening_now_vs_history
+                )
+                |
+                (
+                    cond_block_2
+                    & ((ema_20 - ema_10) > (ema_20_s1 - ema_10_s1))
+                    & ((ema_20 - ema_10) > (ema_20_s2 - ema_10_s2))
+                    & ((ema_20 - ema_10) > (ema_20_s3 - ema_10_s3))
+                    & ((ema_20 - ema_10) > (ema_20_s4 - ema_10_s4))
+                )
+            )
+
+            dataframe.loc[sell_condition, 'exit_long'] = 1
+
+            # 记录信号统计
+            signal_count = sell_condition.sum()
+            if signal_count > 0:
+                logger.info(f"Generated {signal_count} sell signals for {metadata.get('pair', 'unknown')}")
+
+            return dataframe
+
+        except Exception as e:
+            logger.error(f"Error in populate_exit_trend: {e}")
+            return dataframe
+
+    # def custom_stoploss(self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs) -> float:
+    #     """
+    #     自定义止损逻辑
+    #     """
+    #     try:
+    #         # 动态止损：根据RSI调整止损点
+    #         dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+    #         if len(dataframe) > 0:
+    #             last_candle = dataframe.iloc[-1]
+    #             rsi_short = last_candle.get('rsi_6', 50)
+                
+    #             # RSI越高，止损越宽松
+    #             if rsi_short > 70:
+    #                 return -0.15  # 更宽松的止损
+    #             elif rsi_short < 30:
+    #                 return -0.05  # 更严格的止损
+    #             else:
+    #                 return self.stoploss  # 默认止损
+    #         else:
+    #             return self.stoploss
+    #     except Exception as e:
+    #         logger.error(f"Error in custom_stoploss: {e}")
+    #         return self.stoploss

user_data/strategies/TD.py

@@ -0,0 +1,426 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+# flake8: noqa: F401
+# isort: skip_file
+# --- Do not remove these imports ---
+import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta, timezone
+from pandas import DataFrame, date_range
+from typing import Optional, Union
+
+from freqtrade.strategy import (
+    IStrategy,
+    Trade,
+    Order,
+    PairLocks,
+    informative,  # @informative decorator
+    # Hyperopt Parameters
+    BooleanParameter,
+    CategoricalParameter,
+    DecimalParameter,
+    IntParameter,
+    RealParameter,
+    # timeframe helpers
+    timeframe_to_minutes,
+    timeframe_to_next_date,
+    timeframe_to_prev_date,
+    # Strategy helper functions
+    merge_informative_pair,
+    stoploss_from_absolute,
+    stoploss_from_open,
+)
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta
+from technical import qtpylib
+
+
+# This class is a sample. Feel free to customize it.
+class TD(IStrategy):
+    """
+    This is a sample strategy to inspire you.
+    More information in https://www.freqtrade.io/en/latest/strategy-customization/
+
+    You can:
+        :return: a Dataframe with all mandatory indicators for the strategies
+    - Rename the class name (Do not forget to update class_name)
+    - Add any methods you want to build your strategy
+    - Add any lib you need to build your strategy
+
+    You must keep:
+    - the lib in the section "Do not remove these libs"
+    - the methods: populate_indicators, populate_entry_trend, populate_exit_trend
+    You should keep:
+    - timeframe, minimal_roi, stoploss, trailing_*
+    """
+
+    # 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
+
+    # Can this strategy go short?
+    can_short: bool = False
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi".
+    # minimal_roi = {
+    #     # "120": 0.0,  # exit after 120 minutes at break even
+    #     "60": 0.1,
+    #     "30": 0.2,
+    #     "0": 0.4,
+    # }
+
+    # Optimal stoploss designed for the strategy.
+    # This attribute will be overridden if the config file contains "stoploss".
+    stoploss = -1
+
+    # Trailing stoploss
+    trailing_stop = False
+    # trailing_only_offset_is_reached = False
+    # trailing_stop_positive = 0.01
+    # trailing_stop_positive_offset = 0.0  # Disabled / not configured
+
+    # Optimal timeframe for the strategy.
+    timeframe = "5m"
+
+    # 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
+
+    # Hyperoptable parameters
+    buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
+    sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True)
+    short_rsi = IntParameter(low=51, high=100, default=70, space="sell", optimize=True, load=True)
+    exit_short_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 200
+
+    # 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"}
+
+    plot_config = {
+        "main_plot": {
+            "tema": {},
+            "sar": {"color": "white"}
+        },
+        "subplots": {
+            "MACD": {
+                "macd": {"color": "blue"},
+                "macdsignal": {"color": "orange"},
+            },
+            "RSI": {
+                "rsi": {"color": "red"},
+            }
+        },
+    }
+
+    def TD(self, dataframe:DataFrame):
+        close = dataframe['close'].to_list()
+        td = [0,0,0,0]
+        up = 0
+        down = 0
+        for i in range(4, len(close)):
+            if close[i] > close[i-4]:
+                up += 1
+                down = 0
+                td.append(up)
+            else:
+                down -= 1
+                up = 0
+                td.append(down)
+        return td
+
+    def informative_pairs(self):
+        """
+        Define additional, informative pair/interval combinations to be cached from the exchange.
+        These pair/interval combinations are non-tradeable, unless they are part
+        of the whitelist as well.
+        For more information, please consult the documentation
+        :return: List of tuples in the format (pair, interval)
+            Sample: return [("ETH/USDT", "5m"),
+                            ("BTC/USDT", "15m"),
+                            ]
+        """
+        return []
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Adds several different TA indicators to the given DataFrame
+
+        Performance Note: For the best performance be frugal on the number of indicators
+        you are using. Let uncomment only the indicator you are using in your strategies
+        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
+        :param dataframe: Dataframe with data from the exchange
+        :param metadata: Additional information, like the currently traded pair
+        :return: a Dataframe with all mandatory indicators for the strategies
+        """
+
+        # Momentum Indicators
+        # ------------------------------------
+
+        # ADX
+        dataframe["adx"] = ta.ADX(dataframe)
+
+        # # Plus Directional Indicator / Movement
+        # dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
+        # dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+
+        # # Minus Directional Indicator / Movement
+        # dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
+        # dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+        # # Aroon, Aroon Oscillator
+        # aroon = ta.AROON(dataframe)
+        # dataframe['aroonup'] = aroon['aroonup']
+        # dataframe['aroondown'] = aroon['aroondown']
+        # dataframe['aroonosc'] = ta.AROONOSC(dataframe)
+
+        # # Awesome Oscillator
+        # dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
+
+        # # Keltner Channel
+        # keltner = qtpylib.keltner_channel(dataframe)
+        # dataframe["kc_upperband"] = keltner["upper"]
+        # dataframe["kc_lowerband"] = keltner["lower"]
+        # dataframe["kc_middleband"] = keltner["mid"]
+        # dataframe["kc_percent"] = (
+        #     (dataframe["close"] - dataframe["kc_lowerband"]) /
+        #     (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
+        # )
+        # dataframe["kc_width"] = (
+        #     (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
+        # )
+
+        # # Ultimate Oscillator
+        # dataframe['uo'] = ta.ULTOSC(dataframe)
+
+        # # Commodity Channel Index: values [Oversold:-100, Overbought:100]
+        # dataframe['cci'] = ta.CCI(dataframe)
+
+        # RSI
+        dataframe["rsi"] = ta.RSI(dataframe)
+
+        # # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
+        # rsi = 0.1 * (dataframe['rsi'] - 50)
+        # dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
+
+        # # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
+        # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
+
+        # # Stochastic Slow
+        # stoch = ta.STOCH(dataframe)
+        # dataframe['slowd'] = stoch['slowd']
+        # dataframe['slowk'] = stoch['slowk']
+
+        # Stochastic Fast
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe["fastd"] = stoch_fast["fastd"]
+        dataframe["fastk"] = stoch_fast["fastk"]
+
+        # # Stochastic RSI
+        # Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
+        # STOCHRSI is NOT aligned with tradingview, which may result in non-expected results.
+        # stoch_rsi = ta.STOCHRSI(dataframe)
+        # dataframe['fastd_rsi'] = stoch_rsi['fastd']
+        # dataframe['fastk_rsi'] = stoch_rsi['fastk']
+
+        # MACD
+        macd = ta.MACD(dataframe)
+        dataframe["macd"] = macd["macd"]
+        dataframe["macdsignal"] = macd["macdsignal"]
+        dataframe["macdhist"] = macd["macdhist"]
+
+        # MFI
+        dataframe["mfi"] = ta.MFI(dataframe)
+
+        # # ROC
+        # dataframe['roc'] = ta.ROC(dataframe)
+
+        # Overlap Studies
+        # ------------------------------------
+
+        # Bollinger Bands
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        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"]
+        )
+        dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe[
+            "bb_middleband"
+        ]
+
+        # Bollinger Bands - Weighted (EMA based instead of SMA)
+        # weighted_bollinger = qtpylib.weighted_bollinger_bands(
+        #     qtpylib.typical_price(dataframe), window=20, stds=2
+        # )
+        # dataframe["wbb_upperband"] = weighted_bollinger["upper"]
+        # dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
+        # dataframe["wbb_middleband"] = weighted_bollinger["mid"]
+        # dataframe["wbb_percent"] = (
+        #     (dataframe["close"] - dataframe["wbb_lowerband"]) /
+        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
+        # )
+        # dataframe["wbb_width"] = (
+        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) /
+        #     dataframe["wbb_middleband"]
+        # )
+
+        # # EMA - Exponential Moving Average
+        # dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
+        # dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
+        # dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+        # dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
+        # dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
+        # dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+
+        # # SMA - Simple Moving Average
+        dataframe['sma12'] = ta.SMA(dataframe, timeperiod=12)
+        dataframe['sma26'] = ta.SMA(dataframe, timeperiod=26)
+        # dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
+        # dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
+        # dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
+        # dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
+
+        # CCI
+        dataframe['cci'] = ta.CCI(dataframe, timeperiod=12)
+
+        # Parabolic SAR
+        dataframe["sar"] = ta.SAR(dataframe)
+
+        # TEMA - Triple Exponential Moving Average
+        dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
+
+        # Cycle Indicator
+        # ------------------------------------
+        # Hilbert Transform Indicator - SineWave
+        hilbert = ta.HT_SINE(dataframe)
+        dataframe["htsine"] = hilbert["sine"]
+        dataframe["htleadsine"] = hilbert["leadsine"]
+
+        # Pattern Recognition - Bullish candlestick patterns
+        # ------------------------------------
+        # # Hammer: values [0, 100]
+        # dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
+        # # Inverted Hammer: values [0, 100]
+        # dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
+        # # Dragonfly Doji: values [0, 100]
+        # dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
+        # # Piercing Line: values [0, 100]
+        # dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
+        # # Morningstar: values [0, 100]
+        # dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
+        # # Three White Soldiers: values [0, 100]
+        # dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
+
+        # Pattern Recognition - Bearish candlestick patterns
+        # ------------------------------------
+        # # Hanging Man: values [0, 100]
+        # dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
+        # # Shooting Star: values [0, 100]
+        # dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
+        # # Gravestone Doji: values [0, 100]
+        # dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
+        # # Dark Cloud Cover: values [0, 100]
+        # dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
+        # # Evening Doji Star: values [0, 100]
+        # dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
+        # # Evening Star: values [0, 100]
+        # dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
+
+        # Pattern Recognition - Bullish/Bearish candlestick patterns
+        # ------------------------------------
+        # # Three Line Strike: values [0, -100, 100]
+        # dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
+        # # Spinning Top: values [0, -100, 100]
+        # dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
+        # # Engulfing: values [0, -100, 100]
+        # dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
+        # # Harami: values [0, -100, 100]
+        # dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
+        # # Three Outside Up/Down: values [0, -100, 100]
+        # dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
+        # # Three Inside Up/Down: values [0, -100, 100]
+        # dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
+
+        # # Chart type
+        # # ------------------------------------
+        # # Heikin Ashi Strategy
+        # heikinashi = qtpylib.heikinashi(dataframe)
+        # dataframe['ha_open'] = heikinashi['open']
+        # dataframe['ha_close'] = heikinashi['close']
+        # dataframe['ha_high'] = heikinashi['high']
+        # dataframe['ha_low'] = heikinashi['low']
+
+        # Retrieve best bid and best ask from the orderbook
+        # ------------------------------------
+        """
+        # first check if dataprovider is available
+        if self.dp:
+            if self.dp.runmode.value in ('live', 'dry_run'):
+                ob = self.dp.orderbook(metadata['pair'], 1)
+                dataframe['best_bid'] = ob['bids'][0][0]
+                dataframe['best_ask'] = ob['asks'][0][0]
+        """
+        dataframe['TD'] = self.TD(dataframe)
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the entry signal for the given dataframe
+        :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with entry columns populated
+        """
+        dataframe.loc[
+            (
+                # (qtpylib.crossed_above(dataframe['sma10'], dataframe['sma30'])) &
+                # (dataframe['sma5'] > dataframe['sma10']) &
+                # (dataframe['low'] > dataframe['sma5']) 
+                (dataframe['TD'] == 1) 
+                # &
+                # (dataframe['macd'] > dataframe['macdsignal']) &
+                # (dataframe['sma12'] > dataframe['sma26']) &
+                # (dataframe['cci'] < -100)
+                # (dataframe['low'] <= dataframe['low'].shift(4)) 
+                # (dataframe['sma7'] < dataframe['sma30'])&
+                # (dataframe['sma30'] < dataframe['sma60'])
+            ),
+            'enter_long'] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the exit signal for the given dataframe
+        :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with exit columns populated
+        """
+        dataframe.loc[
+             (
+                # (qtpylib.crossed_below(dataframe['sma10'], dataframe['sma30']))
+                (dataframe['TD'] == -1) 
+                # &
+                # (dataframe['high'] >= dataframe['high'].shift(4))
+            ),
+            'exit_long'] = 1
+
+        return dataframe
+

user_data/strategies/cci_multi_tf_strategy.py

@@ -0,0 +1,134 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+# flake8: noqa: F401
+# isort: skip_file
+# --- Do not remove these imports ---
+import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta, timezone
+from pandas import DataFrame
+from typing import Optional, Union
+from freqtrade.strategy import (
+    IStrategy,
+    Trade,
+    Order,
+    PairLocks,
+    informative,  # @informative decorator
+    # Hyperopt Parameters
+    BooleanParameter,
+    CategoricalParameter,
+    DecimalParameter,
+    IntParameter,
+    RealParameter,
+    # timeframe helpers
+    timeframe_to_minutes,
+    timeframe_to_next_date,
+    timeframe_to_prev_date,
+    # Strategy helper functions
+    merge_informative_pair,
+    stoploss_from_absolute,
+    stoploss_from_open,
+)
+import logging  
+  
+logger = logging.getLogger(__name__) 
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta
+from technical import qtpylib
+
+
+class CCIMultiTimeframeSpotStrategy(IStrategy):
+    # 策略参数
+    INTERFACE_VERSION = 3
+
+    minimal_roi = {"0": 100}
+    stoploss = -1
+    trailing_stop = False
+    timeframe = '4h'
+
+    use_exit_signal = True
+    exit_profit_only = False
+    ignore_roi_if_entry_signal = False
+
+    def TD(self, dataframe:DataFrame):
+        close = dataframe['close'].to_list()
+        td = [0,0,0,0]
+        up = 0
+        down = 0
+        for i in range(4, len(close)):
+            if close[i] > close[i-4]:
+                up += 1
+                down = 0
+                td.append(up)
+            else:
+                down -= 1
+                up = 0
+                td.append(down)
+        return td
+
+    @informative('1d')  
+    def populate_indicators_1d(self, dataframe: DataFrame, metadata: dict) -> DataFrame:  
+        dataframe['cci'] = ta.CCI(dataframe, timeperiod=26)  
+        dataframe["adx"] = ta.ADX(dataframe)
+        macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9,)
+        dataframe["macd"] = macd["macd"]
+        dataframe["macdsignal"] = macd["macdsignal"]
+        dataframe["macdhist"] = macd["macdhist"]
+        logger.info(dataframe.tail())
+        return dataframe
+    
+    @informative('1w')  
+    def populate_indicators_1w(self, dataframe: DataFrame, metadata: dict) -> DataFrame:  
+        dataframe['cci'] = ta.CCI(dataframe, timeperiod=26)  
+        logger.info(dataframe.tail())
+        return dataframe
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # 计算4h CCI
+        dataframe['cci_4h'] = ta.CCI(dataframe, timeperiod=26)
+        dataframe['sma12_4h'] = ta.SMA(dataframe, timeperiod=12)
+        dataframe['sma26_4h'] = ta.SMA(dataframe, timeperiod=26)
+        dataframe["adx_4h"] = ta.ADX(dataframe)
+        
+        macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9,)
+        dataframe["macd_4h"] = macd["macd"]
+        dataframe["macdsignal_4h"] = macd["macdsignal"]
+        dataframe["macdhist_4h"] = macd["macdhist"]
+
+        dataframe['cci_hist_4h'] = ta.CCI(dataframe['macdhist_1d'], dataframe['macdhist_1d'], dataframe['macdhist_1d'], timeperiod=26)
+
+        # dataframe['adx_hist_4h'] = ta.ADX(dataframe['macdhist_4h'])
+        dataframe['TD'] = self.TD(dataframe)
+        
+        logger.info(dataframe.tail())
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # 入场：1d与4h CCI < -100，且4h CCI上升（当前 > 前一根）
+        dataframe.loc[
+            (
+                (dataframe['cci_4h'] < -100) &
+                (dataframe['cci_1d'] < -100) &
+                (dataframe['cci_4h'] > dataframe['cci_4h'].shift(1)) &
+                # (dataframe['macdhist_1d'] < dataframe['macdhist_1d'].shift(1)) &
+                (dataframe['volume'] > 0)
+            ),
+            'enter_long',
+        ] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # 离场：1d与4h CCI > 100，且4h CCI下降（当前 < 前一根）
+        dataframe.loc[
+            (
+                (dataframe['cci_4h'] > 100) &
+                (dataframe['cci_1d'] > 100) &
+                (dataframe['cci_4h'] < dataframe['cci_4h'].shift(1)) &
+                # (dataframe['macdhist_1d'] > dataframe['macdhist_1d'].shift(1)) &
+                (dataframe['volume'] > 0)
+            ),
+            'exit_long',
+        ] = 1
+
+        return dataframe
+

user_data/strategies/sample_strategy.py

@@ -0,0 +1,426 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+# flake8: noqa: F401
+# isort: skip_file
+# --- Do not remove these imports ---
+import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta, timezone
+from pandas import DataFrame
+from typing import Optional, Union
+
+from freqtrade.strategy import (
+    IStrategy,
+    Trade,
+    Order,
+    PairLocks,
+    informative,  # @informative decorator
+    # Hyperopt Parameters
+    BooleanParameter,
+    CategoricalParameter,
+    DecimalParameter,
+    IntParameter,
+    RealParameter,
+    # timeframe helpers
+    timeframe_to_minutes,
+    timeframe_to_next_date,
+    timeframe_to_prev_date,
+    # Strategy helper functions
+    merge_informative_pair,
+    stoploss_from_absolute,
+    stoploss_from_open,
+)
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta
+from technical import qtpylib
+
+
+# This class is a sample. Feel free to customize it.
+class SampleStrategy(IStrategy):
+    """
+    This is a sample strategy to inspire you.
+    More information in https://www.freqtrade.io/en/latest/strategy-customization/
+
+    You can:
+        :return: a Dataframe with all mandatory indicators for the strategies
+    - Rename the class name (Do not forget to update class_name)
+    - Add any methods you want to build your strategy
+    - Add any lib you need to build your strategy
+
+    You must keep:
+    - the lib in the section "Do not remove these libs"
+    - the methods: populate_indicators, populate_entry_trend, populate_exit_trend
+    You should keep:
+    - timeframe, minimal_roi, stoploss, trailing_*
+    """
+
+    # 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
+
+    # Can this strategy go short?
+    can_short: bool = False
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi".
+    minimal_roi = {
+        # "120": 0.0,  # exit after 120 minutes at break even
+        "60": 0.01,
+        "30": 0.02,
+        "0": 0.04,
+    }
+
+    # Optimal stoploss designed for the strategy.
+    # This attribute will be overridden if the config file contains "stoploss".
+    stoploss = -0.10
+
+    # Trailing stoploss
+    trailing_stop = False
+    # trailing_only_offset_is_reached = False
+    # trailing_stop_positive = 0.01
+    # trailing_stop_positive_offset = 0.0  # Disabled / not configured
+
+    # Optimal timeframe for the strategy.
+    timeframe = "5m"
+
+    # 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
+
+    # Hyperoptable parameters
+    buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
+    sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True)
+    short_rsi = IntParameter(low=51, high=100, default=70, space="sell", optimize=True, load=True)
+    exit_short_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 200
+
+    # 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"}
+
+    plot_config = {
+        "main_plot": {
+            "tema": {},
+            "sar": {"color": "white"},
+        },
+        "subplots": {
+            "MACD": {
+                "macd": {"color": "blue"},
+                "macdsignal": {"color": "orange"},
+            },
+            "RSI": {
+                "rsi": {"color": "red"},
+            },
+        },
+    }
+
+    def informative_pairs(self):
+        """
+        Define additional, informative pair/interval combinations to be cached from the exchange.
+        These pair/interval combinations are non-tradeable, unless they are part
+        of the whitelist as well.
+        For more information, please consult the documentation
+        :return: List of tuples in the format (pair, interval)
+            Sample: return [("ETH/USDT", "5m"),
+                            ("BTC/USDT", "15m"),
+                            ]
+        """
+        return []
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Adds several different TA indicators to the given DataFrame
+
+        Performance Note: For the best performance be frugal on the number of indicators
+        you are using. Let uncomment only the indicator you are using in your strategies
+        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
+        :param dataframe: Dataframe with data from the exchange
+        :param metadata: Additional information, like the currently traded pair
+        :return: a Dataframe with all mandatory indicators for the strategies
+        """
+
+        # Momentum Indicators
+        # ------------------------------------
+
+        # ADX
+        dataframe["adx"] = ta.ADX(dataframe)
+
+        # # Plus Directional Indicator / Movement
+        # dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
+        # dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+
+        # # Minus Directional Indicator / Movement
+        # dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
+        # dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+        # # Aroon, Aroon Oscillator
+        # aroon = ta.AROON(dataframe)
+        # dataframe['aroonup'] = aroon['aroonup']
+        # dataframe['aroondown'] = aroon['aroondown']
+        # dataframe['aroonosc'] = ta.AROONOSC(dataframe)
+
+        # # Awesome Oscillator
+        # dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
+
+        # # Keltner Channel
+        # keltner = qtpylib.keltner_channel(dataframe)
+        # dataframe["kc_upperband"] = keltner["upper"]
+        # dataframe["kc_lowerband"] = keltner["lower"]
+        # dataframe["kc_middleband"] = keltner["mid"]
+        # dataframe["kc_percent"] = (
+        #     (dataframe["close"] - dataframe["kc_lowerband"]) /
+        #     (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
+        # )
+        # dataframe["kc_width"] = (
+        #     (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
+        # )
+
+        # # Ultimate Oscillator
+        # dataframe['uo'] = ta.ULTOSC(dataframe)
+
+        # # Commodity Channel Index: values [Oversold:-100, Overbought:100]
+        # dataframe['cci'] = ta.CCI(dataframe)
+
+        # RSI
+        dataframe["rsi"] = ta.RSI(dataframe)
+
+        # # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
+        # rsi = 0.1 * (dataframe['rsi'] - 50)
+        # dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
+
+        # # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
+        # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
+
+        # # Stochastic Slow
+        # stoch = ta.STOCH(dataframe)
+        # dataframe['slowd'] = stoch['slowd']
+        # dataframe['slowk'] = stoch['slowk']
+
+        # Stochastic Fast
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe["fastd"] = stoch_fast["fastd"]
+        dataframe["fastk"] = stoch_fast["fastk"]
+
+        # # Stochastic RSI
+        # Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
+        # STOCHRSI is NOT aligned with tradingview, which may result in non-expected results.
+        # stoch_rsi = ta.STOCHRSI(dataframe)
+        # dataframe['fastd_rsi'] = stoch_rsi['fastd']
+        # dataframe['fastk_rsi'] = stoch_rsi['fastk']
+
+        # MACD
+        macd = ta.MACD(dataframe)
+        dataframe["macd"] = macd["macd"]
+        dataframe["macdsignal"] = macd["macdsignal"]
+        dataframe["macdhist"] = macd["macdhist"]
+
+        # MFI
+        dataframe["mfi"] = ta.MFI(dataframe)
+
+        # # ROC
+        # dataframe['roc'] = ta.ROC(dataframe)
+
+        # Overlap Studies
+        # ------------------------------------
+
+        # Bollinger Bands
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        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"]
+        )
+        dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe[
+            "bb_middleband"
+        ]
+
+        # Bollinger Bands - Weighted (EMA based instead of SMA)
+        # weighted_bollinger = qtpylib.weighted_bollinger_bands(
+        #     qtpylib.typical_price(dataframe), window=20, stds=2
+        # )
+        # dataframe["wbb_upperband"] = weighted_bollinger["upper"]
+        # dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
+        # dataframe["wbb_middleband"] = weighted_bollinger["mid"]
+        # dataframe["wbb_percent"] = (
+        #     (dataframe["close"] - dataframe["wbb_lowerband"]) /
+        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
+        # )
+        # dataframe["wbb_width"] = (
+        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) /
+        #     dataframe["wbb_middleband"]
+        # )
+
+        # # EMA - Exponential Moving Average
+        # dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
+        # dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
+        # dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+        # dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
+        # dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
+        # dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+
+        # # SMA - Simple Moving Average
+        # dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
+        # dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
+        # dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
+        # dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
+        # dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
+        # dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
+
+        # Parabolic SAR
+        dataframe["sar"] = ta.SAR(dataframe)
+
+        # TEMA - Triple Exponential Moving Average
+        dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
+
+        # Cycle Indicator
+        # ------------------------------------
+        # Hilbert Transform Indicator - SineWave
+        hilbert = ta.HT_SINE(dataframe)
+        dataframe["htsine"] = hilbert["sine"]
+        dataframe["htleadsine"] = hilbert["leadsine"]
+
+        # Pattern Recognition - Bullish candlestick patterns
+        # ------------------------------------
+        # # Hammer: values [0, 100]
+        # dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
+        # # Inverted Hammer: values [0, 100]
+        # dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
+        # # Dragonfly Doji: values [0, 100]
+        # dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
+        # # Piercing Line: values [0, 100]
+        # dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
+        # # Morningstar: values [0, 100]
+        # dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
+        # # Three White Soldiers: values [0, 100]
+        # dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
+
+        # Pattern Recognition - Bearish candlestick patterns
+        # ------------------------------------
+        # # Hanging Man: values [0, 100]
+        # dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
+        # # Shooting Star: values [0, 100]
+        # dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
+        # # Gravestone Doji: values [0, 100]
+        # dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
+        # # Dark Cloud Cover: values [0, 100]
+        # dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
+        # # Evening Doji Star: values [0, 100]
+        # dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
+        # # Evening Star: values [0, 100]
+        # dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
+
+        # Pattern Recognition - Bullish/Bearish candlestick patterns
+        # ------------------------------------
+        # # Three Line Strike: values [0, -100, 100]
+        # dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
+        # # Spinning Top: values [0, -100, 100]
+        # dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
+        # # Engulfing: values [0, -100, 100]
+        # dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
+        # # Harami: values [0, -100, 100]
+        # dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
+        # # Three Outside Up/Down: values [0, -100, 100]
+        # dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
+        # # Three Inside Up/Down: values [0, -100, 100]
+        # dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
+
+        # # Chart type
+        # # ------------------------------------
+        # # Heikin Ashi Strategy
+        # heikinashi = qtpylib.heikinashi(dataframe)
+        # dataframe['ha_open'] = heikinashi['open']
+        # dataframe['ha_close'] = heikinashi['close']
+        # dataframe['ha_high'] = heikinashi['high']
+        # dataframe['ha_low'] = heikinashi['low']
+
+        # Retrieve best bid and best ask from the orderbook
+        # ------------------------------------
+        """
+        # first check if dataprovider is available
+        if self.dp:
+            if self.dp.runmode.value in ('live', 'dry_run'):
+                ob = self.dp.orderbook(metadata['pair'], 1)
+                dataframe['best_bid'] = ob['bids'][0][0]
+                dataframe['best_ask'] = ob['asks'][0][0]
+        """
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the entry signal for the given dataframe
+        :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with entry columns populated
+        """
+        dataframe.loc[
+            (
+                # Signal: RSI crosses above 30
+                (qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value))
+                & (dataframe["tema"] <= dataframe["bb_middleband"])  # Guard: tema below BB middle
+                & (dataframe["tema"] > dataframe["tema"].shift(1))  # Guard: tema is raising
+                & (dataframe["volume"] > 0)  # Make sure Volume is not 0
+            ),
+            "enter_long",
+        ] = 1
+
+        dataframe.loc[
+            (
+                # Signal: RSI crosses above 70
+                (qtpylib.crossed_above(dataframe["rsi"], self.short_rsi.value))
+                & (dataframe["tema"] > dataframe["bb_middleband"])  # Guard: tema above BB middle
+                & (dataframe["tema"] < dataframe["tema"].shift(1))  # Guard: tema is falling
+                & (dataframe["volume"] > 0)  # Make sure Volume is not 0
+            ),
+            "enter_short",
+        ] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the exit signal for the given dataframe
+        :param dataframe: DataFrame
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with exit columns populated
+        """
+        dataframe.loc[
+            (
+                # Signal: RSI crosses above 70
+                (qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value))
+                & (dataframe["tema"] > dataframe["bb_middleband"])  # Guard: tema above BB middle
+                & (dataframe["tema"] < dataframe["tema"].shift(1))  # Guard: tema is falling
+                & (dataframe["volume"] > 0)  # Make sure Volume is not 0
+            ),
+            "exit_long",
+        ] = 1
+
+        dataframe.loc[
+            (
+                # Signal: RSI crosses above 30
+                (qtpylib.crossed_above(dataframe["rsi"], self.exit_short_rsi.value))
+                &
+                # Guard: tema below BB middle
+                (dataframe["tema"] <= dataframe["bb_middleband"])
+                & (dataframe["tema"] > dataframe["tema"].shift(1))  # Guard: tema is raising
+                & (dataframe["volume"] > 0)  # Make sure Volume is not 0
+            ),
+            "exit_short",
+        ] = 1
+
+        return dataframe