添加策略

.gitignore

@@ -0,0 +1,183 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# Data files (keep structure but ignore large data)
+data/
+
+# IDE files
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS generated files
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# Docker
+.dockerignore
+
+# Logs
+*.log
+logs/
+
+# Temporary files
+tmp/
+temp/
+*.tmp
+*.temp
+
+# API keys and secrets
+.env
+config.ini
+secrets.json
+api_keys.txt
+
+# Database files
+*.db
+*.sqlite
+*.sqlite3
+
+# Backup files
+*.bak
+*.backup
+
+
+user_data/data
+user_data/freqaimodels
+user_data/hyperopts
+user_data/logs
+user_data/backtest_results

Dockerfile

@@ -0,0 +1,14 @@
+FROM freqtradeorg/freqtrade:stable
+
+# 设置工作目录
+WORKDIR /freqtrade
+
+# 复制用户数据到镜像（策略、配置等），但排除 data 和 backtest_results 目录
+COPY user_data /freqtrade/user_data
+RUN rm -rf /freqtrade/user_data/data /freqtrade/user_data/backtest_results
+
+# 暴露 freqtrade WebUI 端口
+EXPOSE 8077
+
+# 设置启动命令，与 compose 保持一致
+CMD ["trade", "--config", "/freqtrade/user_data/config.json", "--strategy", "SimpleRSIStrategyFixed"]

docker-compose-trade.yml

@@ -10,4 +10,4 @@ services:
     command: >
       trade
       --config ./user_data/config.json
-      --strategy MACDStrategy
+      --strategy SimpleRSIStrategyFixed

user_data/config.json

@@ -1,7 +1,7 @@
 
 {
     "$schema": "https://schema.freqtrade.io/schema.json",
-    "max_open_trades": 1,
+    "max_open_trades": 3,
     "stake_currency": "USDT",
     "stake_amount": "unlimited",
     "tradable_balance_ratio": 0.99,
@@ -41,7 +41,8 @@
         "ccxt_config": {},
         "ccxt_async_config": {},
         "pair_whitelist": [
-            "ETH/USDT"
+            "ETH/USDT",
+            "BTC/USDT"
         ],
         "pair_blacklist": [
         ]

user_data/strategies/MACDStrategy.py

@@ -42,9 +42,9 @@ class MACDStrategy(IStrategy):
     INTERFACE_VERSION = 3
 
     minimal_roi = {"0": 100}
-    stoploss = -1
+    stoploss = -0.05
     trailing_stop = False
-    timeframe = '15m'
+    timeframe = '4h'
 
     use_exit_signal = True
     exit_profit_only = False
@@ -72,7 +72,7 @@ class MACDStrategy(IStrategy):
         dataframe["macd"] = macd["macd"]
         dataframe["macdsignal"] = macd["macdsignal"]
         dataframe["macdhist"] = macd["macdhist"]
-        dataframe['cci'] = ta.CCI(dataframe, 26)
+        dataframe['cci'] = ta.CCI(dataframe, 14)
         dataframe['TD'] = self.TD(dataframe)
         return dataframe
 
@@ -80,15 +80,14 @@ class MACDStrategy(IStrategy):
         # 入场：1d与4h CCI < -100，且4h CCI上升（当前 > 前一根）
         dataframe.loc[
             (
-                # (dataframe['macdhist'] < 0) &
+                (dataframe['macdhist'] < 0) &
-                # (dataframe['macdhist'] > dataframe['macdhist'].shift(1)) &
+                (dataframe['macdhist'] > dataframe['macdhist'].shift(1)) &
                 # (dataframe['macdsignal'] < 0) &
                 # (dataframe['macd'] < dataframe['macdsignal']) &
-                # (dataframe['cci'] < -100) &
+                (dataframe['cci'] < -100) &
-                # (dataframe['cci'].shift(1) < dataframe['cci']) &
+                (dataframe['cci'] > dataframe['cci'].shift(1))
                 # (dataframe['macdhist'] < 0) &
-                (dataframe['TD'] == 1) &
+                # (dataframe['volume'] > 0)
-                (dataframe['volume'] > 0)
             ),
             'enter_long',
         ] = 1
@@ -99,15 +98,17 @@ class MACDStrategy(IStrategy):
         # 离场：1d与4h CCI > 100，且4h CCI下降（当前 < 前一根）
         dataframe.loc[
             (
-                # (dataframe['macdhist'] > 0) &
+                ((dataframe['macdhist'] < 0) &
+                (dataframe['macdhist'].shift(1) > 0) )
+                | (qtpylib.crossed_below(dataframe['macd'], dataframe['macdsignal']) )
                 # (dataframe['macdhist'] < dataframe['macdhist'].shift(1)) &
                 # (dataframe['macdsignal'] > 0) &
                 # (dataframe['macd'] > dataframe['macdsignal']) &
                 # (dataframe['cci'] > 100 )&
-                # (dataframe['cci'].shift(1) > dataframe['cci']) &
+                # (dataframe['cci'] < dataframe['cci'].shift(1)) &
                 # (dataframe['macdhist'] > 0) &
-                (dataframe['TD'] == -1) &
+                # (dataframe['TD'] == -1) &
-                (dataframe['volume'] > 0)
+                # (dataframe['volume'] > 0)
             ),
             'exit_long',
         ] = 1

user_data/strategies/SimpleRSIStrategyOptimized.py

@@ -0,0 +1,66 @@
+import talib.abstract as ta
+import pandas as pd
+import numpy as np
+from freqtrade.strategy import IStrategy
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class SimpleRSIStrategyOptimized(IStrategy):
+    INTERFACE_VERSION = 3
+    can_short: bool = False
+    stoploss = -0.05
+    minimal_roi = {"0": 100}  # 由止盈逻辑替代
+    timeframe = '1d'
+
+    # === 策略参数（可优化）===
+    ema_short = 10
+    ema_long = 20
+    rsi_period = 6
+    rsi_oversold = 20
+
+    def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
+        # EMA
+        dataframe['ema_short'] = ta.EMA(dataframe['close'], timeperiod=self.ema_short)
+        dataframe['ema_long'] = ta.EMA(dataframe['close'], timeperiod=self.ema_long)
+
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=self.rsi_period)
+
+        # 辅助：昨日值（shift 1）
+        dataframe['ema_short_prev'] = dataframe['ema_short'].shift(1)
+        dataframe['rsi_prev'] = dataframe['rsi'].shift(1)
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
+        dataframe.loc[
+            (
+            # 趋势条件：EMA 短期 > 长期 且 短期 EMA 上升
+            (dataframe['ema_short'] > dataframe['ema_long']) &
+            (dataframe['ema_short'] > dataframe['ema_short_prev']) &
+
+            # RSI 超卖后反弹
+            (dataframe['rsi_prev'] <= self.rsi_oversold) &
+            (dataframe['rsi'] > dataframe['rsi_prev'])
+            ),
+            'enter_long',
+        ] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
+
+        dataframe.loc[
+            (
+            # 趋势破坏：EMA 死叉
+            (dataframe['ema_short'] < dataframe['ema_long']) |
+            # 或价格跌破长期 EMA（支撑失效）
+            (dataframe['close'] < dataframe['ema_long'])
+        ),
+            'exit_long',
+        ] = 1
+
+
+        return dataframe

user_data/strategies/SimpleRSIStrategy_Fixed.py

@@ -118,8 +118,8 @@ class SimpleRSIStrategyFixed(IStrategy):
             f"方向: {side}, "  
             f"仓位: {position}"  
         )  
-        dingtalk.send_text(
+        # dingtalk.send_text(
-            content=f"订单成交 - 交易对: {trading_pair}, 时间: {fill_time}, 价格: {fill_price}, 方向: {side}, 仓位: {position}")
+        #     content=f"订单成交 - 交易对: {trading_pair}, 时间: {fill_time}, 价格: {fill_price}, 方向: {side}, 仓位: {position}")
         
         return None
 

user_data/strategies/cci_multi_tf_strategy.py

@@ -2,6 +2,7 @@
 # flake8: noqa: F401
 # isort: skip_file
 # --- Do not remove these imports ---
+from dingtalk import DingTalkBot
 import numpy as np
 import pandas as pd
 from datetime import datetime, timedelta, timezone
@@ -37,12 +38,22 @@ import talib.abstract as ta
 from technical import qtpylib
 
 
+webhook = "https://oapi.dingtalk.com/robot/send?access_token=21de667159edadd33172c6ec414a2addf9c6359189350ffd36819d2a20e8a0f4"  # 填写你的webhook
+secret = "SEC43a0fa0b29717f98637a119b92a0bd5f7b2b6da671bdd2bd1279ed8323454d5e"  # 填写你的加签token（如果有），否则留空
+
+# CTA 群机器人
+# webhook = "https://oapi.dingtalk.com/robot/send?access_token=87c7abfcdd69b699c32da4e4f5981cd2ca6b0445474fc6ffb36f2ed0f6262fbb"
+# secret = "SECf3d6b43f2f8a87ab91feffd052e71ec314fbf57a1842e483fe07af3c0a0e5aa6"
+dingtalk = DingTalkBot(webhook, secret)
+
+
 class CCIMultiTimeframeSpotStrategy(IStrategy):
     # 策略参数
     INTERFACE_VERSION = 3
 
     minimal_roi = {"0": 100}
     stoploss = -1
+    use_custom_stoploss = False
     trailing_stop = False
     timeframe = '4h'
 
@@ -50,6 +61,37 @@ class CCIMultiTimeframeSpotStrategy(IStrategy):
     exit_profit_only = False
     ignore_roi_if_entry_signal = False
 
+    def order_filled(self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs) -> None:  
+        # 交易对  
+        trading_pair = pair  
+        
+        # 时间  
+        fill_time = order.order_filled_date or current_time  
+        
+        # 价格  
+        fill_price = order.average or order.price  
+        
+        # 买入还是卖出  
+        side = order.ft_order_side  # 'buy' 或 'sell'  
+        
+        # 仓位(当前持仓数量)  
+        position = trade.amount  
+        
+        # 或者使用日志  
+        logger.info(  
+            f"订单成交 - 交易对: {trading_pair}, "  
+            f"时间: {fill_time}, "  
+            f"价格: {fill_price}, "  
+            f"方向: {side}, "  
+            f"仓位: {position}"  
+        )  
+        # if self.config["runmode"].value in ("live", "dry_run"):
+        logger.info(f"11111111{self.config['runmode']}")
+        # dingtalk.send_text(
+        #     content=f"订单成交 - 交易对: {trading_pair}, 时间: {fill_time}, 价格: {fill_price}, 方向: {side}, 仓位: {position}")
+        
+        return None
+
     def TD(self, dataframe:DataFrame):
         close = dataframe['close'].to_list()
         td = [0,0,0,0]
@@ -98,6 +140,7 @@ class CCIMultiTimeframeSpotStrategy(IStrategy):
 
         # dataframe['adx_hist_4h'] = ta.ADX(dataframe['macdhist_4h'])
         dataframe['TD'] = self.TD(dataframe)
+        dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)  
         
         logger.info(dataframe.tail())
         return dataframe
@@ -132,3 +175,14 @@ class CCIMultiTimeframeSpotStrategy(IStrategy):
 
         return dataframe
 
+    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,  
+                    current_rate: float, current_profit: float, after_fill: bool,  
+                    **kwargs) -> float | None:  
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)  
+        candle = dataframe.iloc[-1].squeeze()  
+        side = 1 if trade.is_short else -1  
+        return stoploss_from_absolute(current_rate + (side * candle["atr"] * 3),   
+                                    current_rate=current_rate,   
+                                    is_short=trade.is_short,  
+                                    leverage=trade.leverage)
+

user_data/strategies/sample_strategy.py

@@ -36,23 +36,26 @@ import talib.abstract as ta
 from technical import qtpylib
 
 
-# This class is a sample. Feel free to customize it.
+# 基于x.py策略的MA60/MA120趋势跟踪策略
-class SampleStrategy(IStrategy):
+class MA60MA120TrendStrategy(IStrategy):
     """
-    This is a sample strategy to inspire you.
+    基于x.py策略的MA60/MA120趋势跟踪策略
-    More information in https://www.freqtrade.io/en/latest/strategy-customization/
+    
-
+    策略逻辑：
-    You can:
+    1. 买入条件：
-        :return: a Dataframe with all mandatory indicators for the strategies
+       - 价格在MA60上方且距离MA60不超过10%
-    - Rename the class name (Do not forget to update class_name)
+       - MA60呈上升趋势（过去5天中至少3天上升）
-    - Add any methods you want to build your strategy
+       - 成交量放大（当日成交量大于过去5日平均）
-    - Add any lib you need to build your strategy
+       - 前5天中下跌天数不超过2天
-
+       - 当天收盘价大于5天前的价格
-    You must keep:
+       - 价格在MA120上方
-    - the lib in the section "Do not remove these libs"
+       - MA120过去5天不是下降趋势
-    - the methods: populate_indicators, populate_entry_trend, populate_exit_trend
+       - RSI小于阈值（默认70）
-    You should keep:
+    
-    - timeframe, minimal_roi, stoploss, trailing_*
+    2. 卖出条件：
+       - 价格跌破MA60且跌幅超过10%
+    
+    更多信息: https://www.freqtrade.io/en/latest/strategy-customization/
     """
 
     # Strategy interface version - allow new iterations of the strategy interface.
@@ -63,26 +66,25 @@ class SampleStrategy(IStrategy):
     can_short: bool = False
 
     # Minimal ROI designed for the strategy.
-    # This attribute will be overridden if the config file contains "minimal_roi".
+    # 基于x.py策略，使用更保守的ROI设置
-    minimal_roi = {
+    # minimal_roi = {
-        # "120": 0.0,  # exit after 120 minutes at break even
+    #     "30": 0.3,  # 20% 目标收益
-        "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".
+    # 基于x.py策略的卖出条件：跌破MA60且跌幅超过10%
-    stoploss = -0.10
+    stoploss = -1
 
     # Trailing stoploss
-    trailing_stop = False
+    # trailing_stop = True
     # trailing_only_offset_is_reached = False
-    # trailing_stop_positive = 0.01
+    # trailing_stop_positive = 0.3
     # trailing_stop_positive_offset = 0.0  # Disabled / not configured
 
     # Optimal timeframe for the strategy.
-    timeframe = "5m"
+    # 使用日线数据，因为x.py策略基于日线
+    timeframe = "1d"
 
     # Run "populate_indicators()" only for new candle.
     process_only_new_candles = True
@@ -92,13 +94,15 @@ class SampleStrategy(IStrategy):
     exit_profit_only = False
     ignore_roi_if_entry_signal = False
 
-    # Hyperoptable parameters
+    # 移除原有的RSI参数，因为新策略使用固定值
-    buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
+    # 可以添加其他可优化的参数
-    sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True)
+    ma60_period = IntParameter(low=30, high=90, default=60, space="buy", optimize=True, load=True)
-    short_rsi = IntParameter(low=51, high=100, default=70, space="sell", optimize=True, load=True)
+    ma120_period = IntParameter(low=60, high=180, default=120, space="buy", optimize=True, load=True)
-    exit_short_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
+    rsi_threshold = IntParameter(low=50, high=80, default=70, space="buy", optimize=True, load=True)
+    volume_surge_multiplier = DecimalParameter(low=0.5, high=2.0, default=1.0, space="buy", optimize=True, load=True)
 
     # Number of candles the strategy requires before producing valid signals
+    # 需要更多数据来计算MA120和趋势指标
     startup_candle_count: int = 200
 
     # Optional order type mapping.
@@ -114,16 +118,21 @@ class SampleStrategy(IStrategy):
 
     plot_config = {
         "main_plot": {
-            "tema": {},
+            "ma60": {"color": "blue", "width": 2},
-            "sar": {"color": "white"},
+            "ma120": {"color": "orange", "width": 2},
         },
         "subplots": {
-            "MACD": {
-                "macd": {"color": "blue"},
-                "macdsignal": {"color": "orange"},
-            },
             "RSI": {
                 "rsi": {"color": "red"},
+                "rsi_threshold": {"color": "gray", "type": "line", "width": 1},
+            },
+            "Volume": {
+                "volume": {"color": "lightblue"},
+                "volume_5ma": {"color": "blue"},
+            },
+            "Signals": {
+                "buy_signal": {"color": "green", "type": "scatter", "marker": "^"},
+                "sell_signal": {"color": "red", "type": "scatter", "marker": "v"},
             },
         },
     }
@@ -143,284 +152,211 @@ class SampleStrategy(IStrategy):
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
-        Adds several different TA indicators to the given DataFrame
+        基于x.py策略的技术指标计算
-
+        实现MA60/MA120趋势跟踪策略的所有必要指标
-        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
+        # 基础移动平均线指标
+        # ------------------------------------
+        
+        # MA60 - 使用可优化参数
+        dataframe['ma60'] = ta.SMA(dataframe, timeperiod=60)
+        
+        # MA120 - 使用可优化参数
+        dataframe['ma120'] = ta.SMA(dataframe, timeperiod=120)
+        
+        # RSI指标
+        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
+        
+        # 成交量相关指标
+        # ------------------------------------
+        
+        # 成交量5日移动平均
+        dataframe['volume_5ma'] = dataframe['volume'].rolling(window=5).mean()
+        
+        # 成交量放大信号：当日成交量大于过去5日平均的倍数
+        dataframe['volume_surge'] = dataframe['volume'] > (dataframe['volume_5ma'] * 1)
+        
+        # 价格与MA60关系指标
         # ------------------------------------
 
-        # ADX
+        # 价格在MA60上方且距离MA60不超过10%
-        dataframe["adx"] = ta.ADX(dataframe)
+        dataframe['price_above_ma60'] = (
-
+            (dataframe['close'] > dataframe['ma60']) & 
-        # # Plus Directional Indicator / Movement
+            ((dataframe['ma60'] - dataframe['close']) / dataframe['close'] < 0.1)
-        # 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"
+        # 价格在MA120上方
-        ]
+        dataframe['price_above_ma120'] = dataframe['close'] > dataframe['ma120']
-
+        
-        # Bollinger Bands - Weighted (EMA based instead of SMA)
+        # 前一天在MA60附近（95%-105%）
-        # weighted_bollinger = qtpylib.weighted_bollinger_bands(
+        dataframe['prev_near_ma'] = (
-        #     qtpylib.typical_price(dataframe), window=20, stds=2
+            (dataframe['close'].shift(1) >= dataframe['ma60'].shift(1) * 0.95) & 
-        # )
+            (dataframe['close'].shift(1) <= dataframe['ma60'].shift(1) * 1.05)
-        # dataframe["wbb_upperband"] = weighted_bollinger["upper"]
+        )
-        # dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
+        
-        # dataframe["wbb_middleband"] = weighted_bollinger["mid"]
+        # 前一天在MA60下方
-        # dataframe["wbb_percent"] = (
+        dataframe['prev_below_ma'] = dataframe['close'].shift(1) < dataframe['ma60'].shift(1)
-        #     (dataframe["close"] - dataframe["wbb_lowerband"]) /
+        
-        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
+        # 突破信号：前一天在MA60附近或下方，当天在MA60上方
-        # )
+        dataframe['breakthrough'] = (
-        # dataframe["wbb_width"] = (
+            (dataframe['prev_below_ma'] | dataframe['prev_near_ma']) & 
-        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) /
+            dataframe['price_above_ma60']
-        #     dataframe["wbb_middleband"]
+        )
-        # )
+        
-
+        # MA60趋势指标
-        # # 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)
+        # 计算MA60的上升趋势（过去5天中至少3天上升）
-        dataframe["htsine"] = hilbert["sine"]
+        trend_days = 5
-        dataframe["htleadsine"] = hilbert["leadsine"]
+        ma_trend = []
-
+        for i in range(len(dataframe)):
-        # Pattern Recognition - Bullish candlestick patterns
+            if i < trend_days - 1:
+                ma_trend.append(False)
+            else:
+                start_idx = i - trend_days + 1
+                ma_values = dataframe['ma60'].iloc[start_idx:i+1].values
+                if pd.isna(ma_values).any():
+                    ma_trend.append(False)
+                else:
+                    # 检查是否呈上升趋势（至少3天上升）
+                    ma_trend.append(sum(ma_values[1:] > ma_values[:-1]) > trend_days - 3)
+        dataframe['ma_uptrend'] = ma_trend
+        
+        # MA120趋势指标
         # ------------------------------------
-        # # Hammer: values [0, 100]
+        
-        # dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
+        # 计算MA120的趋势（过去5天）不能为下降
-        # # Inverted Hammer: values [0, 100]
+        ma120_trend = []
-        # dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
+        for i in range(len(dataframe)):
-        # # Dragonfly Doji: values [0, 100]
+            if i < 4:
-        # dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
+                ma120_trend.append(True)  # 数据不足时不限制
-        # # Piercing Line: values [0, 100]
+            else:
-        # dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
+                window = dataframe['ma120'].iloc[i-4:i+1].values
-        # # Morningstar: values [0, 100]
+                if pd.isna(window).any():
-        # dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
+                    ma120_trend.append(True)
-        # # Three White Soldiers: values [0, 100]
+                else:
-        # dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
+                    # 只要有一天不是上升就不是下降趋势
-
+                    ma120_trend.append((window[1:] >= window[:-1]).all())
-        # Pattern Recognition - Bearish candlestick patterns
+        dataframe['ma120_not_downtrend'] = ma120_trend
+        
+        # 价格动量指标
         # ------------------------------------
-        # # Hanging Man: values [0, 100]
+        
-        # dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
+        # 阳线条件：收盘价 > 开盘价，且阳线幅度至少0.5%
-        # # Shooting Star: values [0, 100]
+        dataframe['is_bullish'] = (
-        # dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
+            (dataframe['close'] > dataframe['open']) & 
-        # # Gravestone Doji: values [0, 100]
+            ((dataframe['close'] - dataframe['open']) / dataframe['open'] >= 0.005)
-        # dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
+        )
-        # # Dark Cloud Cover: values [0, 100]
+        
-        # dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
+        # 前5天的下跌天数不超过2天
-        # # Evening Doji Star: values [0, 100]
+        dataframe['is_down_day'] = dataframe['close'] < dataframe['open']
-        # dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
+        dataframe['down_days_in_5'] = dataframe['is_down_day'].rolling(window=5, min_periods=1).sum()
-        # # Evening Star: values [0, 100]
+        dataframe['not_too_many_downs'] = dataframe['down_days_in_5'] <= 2
-        # dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
+        
-
+        # 当天收盘价大于第五天前的价格
-        # Pattern Recognition - Bullish/Bearish candlestick patterns
+        dataframe['price_higher_than_5days_ago'] = dataframe['close'] > dataframe['close'].shift(5)
+        
+        # RSI条件 - 使用可优化参数
+        dataframe['rsi_lt_threshold'] = dataframe['rsi'] < 70
+        
+        # 综合买入信号
         # ------------------------------------
-        # # Three Line Strike: values [0, -100, 100]
+        
-        # dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
+        dataframe["buy_signal"] = (
-        # # Spinning Top: values [0, -100, 100]
+            dataframe["price_above_ma60"] &
-        # dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
+            dataframe["ma_uptrend"] &
-        # # Engulfing: values [0, -100, 100]
+            dataframe['ma60'].notna() &
-        # dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
+            dataframe["volume_surge"] &
-        # # Harami: values [0, -100, 100]
+            dataframe["not_too_many_downs"] &
-        # dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
+            dataframe["price_higher_than_5days_ago"] &
-        # # Three Outside Up/Down: values [0, -100, 100]
+            dataframe['price_above_ma120'] &
-        # dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
+            dataframe['ma120_not_downtrend'] 
-        # # Three Inside Up/Down: values [0, -100, 100]
+            # &
-        # dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
+            # dataframe['rsi_lt_threshold']
-
+        )
-        # # Chart type
+        
-        # # ------------------------------------
+        # 卖出信号：价格跌破MA60且跌幅超过10%
-        # # Heikin Ashi Strategy
+        dataframe['sell_signal'] = (
-        # heikinashi = qtpylib.heikinashi(dataframe)
+            (~dataframe['price_above_ma60']) & 
-        # dataframe['ha_open'] = heikinashi['open']
+            ((dataframe['ma60'] - dataframe['close']) / dataframe['ma60'] > 0.1) & 
-        # dataframe['ha_close'] = heikinashi['close']
+            dataframe['ma60'].notna()
-        # 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
+        基于x.py策略的买入信号逻辑
+        实现MA60/MA120趋势跟踪策略的买入条件
         :param dataframe: DataFrame
         :param metadata: Additional information, like the currently traded pair
         :return: DataFrame with entry columns populated
         """
+        # 多头买入信号 - 基于x.py的buy_signal逻辑
         dataframe.loc[
             (
-                # Signal: RSI crosses above 30
+                # 价格在MA60上方且距离MA60不超过10%
-                (qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value))
+                dataframe["price_above_ma60"] &
-                & (dataframe["tema"] <= dataframe["bb_middleband"])  # Guard: tema below BB middle
+                # MA60呈上升趋势（过去5天中至少3天上升）
-                & (dataframe["tema"] > dataframe["tema"].shift(1))  # Guard: tema is raising
+                dataframe["ma_uptrend"] &
-                & (dataframe["volume"] > 0)  # Make sure Volume is not 0
+                # MA60数据有效
+                dataframe['ma60'].notna() &
+                # 成交量放大（当日成交量大于过去5日平均）
+                dataframe["volume_surge"] &
+                # 前5天中下跌天数不超过2天
+                dataframe["not_too_many_downs"] &
+                # 当天收盘价大于5天前的价格
+                dataframe["price_higher_than_5days_ago"] &
+                # 价格在MA120上方
+                dataframe['price_above_ma120'] &
+                # MA120过去5天不是下降趋势
+                dataframe['ma120_not_downtrend'] &
+                # RSI小于阈值
+                dataframe['rsi_lt_threshold'] &
+                # 确保成交量不为0
+                (dataframe["volume"] > 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
+        基于x.py策略的卖出信号逻辑
+        实现MA60/MA120趋势跟踪策略的卖出条件
         :param dataframe: DataFrame
         :param metadata: Additional information, like the currently traded pair
         :return: DataFrame with exit columns populated
         """
+        # 多头卖出信号 - 基于x.py的sell_signal逻辑
+        # dataframe.loc[
+        #     (
+        #         # 价格跌破MA60且跌幅超过10%
+        #         (~dataframe['price_above_ma60']) & 
+        #         ((dataframe['ma60'] - dataframe['close']) / dataframe['ma60'] > 0.1) & 
+        #         dataframe['ma60'].notna() &
+        #         # 确保成交量不为0
+        #         (dataframe["volume"] > 0)
+        #     ),
+        #     "exit_long",
+        # ] = 1
+
         dataframe.loc[
             (
-                # Signal: RSI crosses above 70
+                # 价格跌破MA60且跌幅超过10%
-                (qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value))
+                (dataframe['close'] < dataframe['ma120']) & 
-                & (dataframe["tema"] > dataframe["bb_middleband"])  # Guard: tema above BB middle
+                dataframe['ma120'].notna() &
-                & (dataframe["tema"] < dataframe["tema"].shift(1))  # Guard: tema is falling
+                # 确保成交量不为0
-                & (dataframe["volume"] > 0)  # Make sure Volume is not 0
+                (dataframe["volume"] > 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