ema双均线趋势策略

trend.py

@@ -0,0 +1,81 @@
+import vectorbt as vbt
+import pandas as pd
+
+# ========================
+# 数据加载
+# ========================
+# 从本地读取 ETH/USDT 1天K线数据（来自OKX）
+df = pd.read_feather("/Users/aszer/Documents/vscode/cta/user_data/data/okx/ADA_USDT-1d.feather")
+# 提取收盘价作为交易价格
+price = df["close"]
+
+# ========================
+# 技术指标计算
+# ========================
+# 计算10日指数移动平均线（EMA）
+ema_10 = vbt.MA.run(price, window=10, ewm=True).ma
+
+# 计算20日指数移动平均线（EMA）
+ema_20 = vbt.MA.run(price, window=20, ewm=True).ma
+
+# 计算6周期相对强弱指数（RSI）
+rsi_6 = vbt.RSI.run(price, window=6).rsi
+
+# 计算12周期相对强弱指数（RSI）
+rsi_12 = vbt.RSI.run(price, window=12).rsi
+
+# ========================
+# 买入条件定义
+# ========================
+# 条件1：EMA多头排列 + 短期趋势加速
+# - EMA10 > EMA20：短期趋势强于长期趋势（多头排列）
+# - EMA10昨日 > 前日：短期均线继续上行，显示动量增强
+ema_bullish_cross = (ema_10 > ema_20) & (ema_10.shift(1) > ema_10.shift(2))
+
+# 条件2：双周期RSI进入深度超卖区，暗示反弹可能
+# - 过去3天（含前天）的6周期RSI最高值 < 21，表示近期极度超卖
+# - 过去3天的12周期RSI最高值 < 26.25，确认中周期也处于超卖状态
+# 注：此条件未显式检查“今日反弹”，可后续增强
+rsi_oversold_bounce = (rsi_6.shift(1).rolling(3).max() < 21) & \
+                      (rsi_12.shift(1).rolling(3).max() < 26.25)
+
+# 合并所有买入信号：任一条件满足即产生买入信号
+entries = ema_bullish_cross | rsi_oversold_bounce
+
+# ========================
+# 卖出条件定义
+# ========================
+# 计算长期均线与短期均线的价差（空头趋势强度）
+diff = ema_20 - ema_10  # 当diff扩大，表示空头趋势加强
+
+# 条件1：EMA10连续5天下跌（动量走弱）
+# - 当前EMA10 < 过去5天（不含今日）的最低EMA10值
+ema10_falling = ema_10 < ema_10.shift(1).rolling(window=5).min()
+
+# 条件2：空头趋势加速（价差达到近期最大）
+# - 当前diff是过去6天（含今日）中的最大值，表示空头力量最强
+diff_expanding = diff == diff.rolling(window=6).max()
+
+# 合并卖出信号：两个条件同时满足才卖出
+sell_cond = ema10_falling & diff_expanding
+exits = sell_cond
+
+# ========================
+# 回测执行
+# ========================
+# 使用 vectorbt 的信号回测引擎，构建投资组合
+pf = vbt.Portfolio.from_signals(
+    price,                  # 价格序列
+    entries,                # 买入信号
+    exits,                  # 卖出信号
+    init_cash=100,          # 初始资金 100 USDT
+    fees=0.001,             # 交易手续费 0.1%（买卖均收）
+    sl_stop=0.05,           # 止损：从最高价回撤5%时触发
+    freq="1D"               # 数据频率为每日，用于复利和年化计算
+)
+
+# ========================
+# 输出回测统计结果
+# ========================
+print(pf.stats())
+

user_data/strategies/trend_strategy.py

@@ -0,0 +1,205 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+# flake8: noqa: F401
+# isort: skip_file
+# --- Do not remove these libs ---
+import numpy as np
+import pandas as pd
+from pandas import DataFrame
+from datetime import datetime
+from typing import Optional, Union
+
+from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
+                                IntParameter, merge_informative_pair, stoploss_from_absolute,
+                                stoploss_from_open)
+from freqtrade.strategy.interface import IStrategy
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class TrendStrategy(IStrategy):
+    """
+    趋势策略 - 基于EMA和RSI的趋势跟踪策略
+    
+    策略逻辑：
+    买入条件：
+    1. EMA多头排列 + 短期趋势加速：EMA10 > EMA20 且 EMA10继续上行
+    2. 双周期RSI深度超卖反弹：6周期RSI和12周期RSI都进入超卖区域
+    
+    卖出条件：
+    1. EMA10连续下跌（动量走弱）
+    2. 空头趋势加速（EMA价差达到近期最大）
+    """
+
+    # Strategy interface version - allow new iterations of the strategy
+    INTERFACE_VERSION = 3
+
+    # Optimal timeframe for the strategy
+    timeframe = '1d'
+
+    # Can this strategy go short?
+    can_short: bool = False
+
+    # Minimal ROI designed for the strategy.
+    # minimal_roi = {
+    #     "0": 0.1  # 10% ROI，让止损和卖出条件来控制退出
+    # }
+
+    # Optimal stoploss designed for the strategy
+    stoploss = -0.05  # 5% 止损
+
+    # Trailing stoploss
+    trailing_stop = False
+    trailing_stop_positive = None
+    trailing_stop_positive_offset = 0.0
+    trailing_only_offset_is_reached = False
+
+    # Run "populate_indicators" only for new candle
+    process_only_new_candles = False
+
+    # These values can be overridden in the config
+    use_exit_signal = True
+    exit_profit_only = False
+    ignore_roi_if_entry_signal = False
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 30
+
+    # 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'
+    }
+
+    # 策略参数
+    ema_short_window = IntParameter(8, 15, default=10, space="buy")
+    ema_long_window = IntParameter(15, 25, default=20, space="buy")
+    rsi_short_window = IntParameter(4, 8, default=6, space="buy")
+    rsi_long_window = IntParameter(10, 15, default=12, space="buy")
+    
+    # RSI超卖阈值
+    rsi_short_oversold = DecimalParameter(15.0, 25.0, default=21.0, space="buy")
+    rsi_long_oversold = DecimalParameter(20.0, 30.0, default=26.25, space="buy")
+    
+    # 趋势检查窗口
+    trend_check_window = IntParameter(3, 7, default=3, space="buy")
+    momentum_check_window = IntParameter(3, 7, default=5, space="sell")
+    diff_check_window = IntParameter(4, 8, default=6, space="sell")
+
+    def informative_pairs(self):
+        """
+        Define additional, informative pair/interval combinations to be cached from the exchange.
+        These pairs will automatically be available for use in the `populate_indicators` method.
+        """
+        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 strategy
+        """
+
+        # EMA指标
+        dataframe['ema_short'] = ta.EMA(dataframe, timeperiod=self.ema_short_window.value)
+        dataframe['ema_long'] = ta.EMA(dataframe, timeperiod=self.ema_long_window.value)
+        
+        # RSI指标
+        dataframe['rsi_short'] = ta.RSI(dataframe, timeperiod=self.rsi_short_window.value)
+        dataframe['rsi_long'] = ta.RSI(dataframe, timeperiod=self.rsi_long_window.value)
+        
+        # 计算EMA价差（用于卖出条件）
+        dataframe['ema_diff'] = dataframe['ema_long'] - dataframe['ema_short']
+        
+        # 计算EMA短期趋势（用于买入条件）
+        dataframe['ema_short_rising'] = dataframe['ema_short'] > dataframe['ema_short'].shift(1)
+        dataframe['ema_short_rising_2'] = dataframe['ema_short'].shift(1) > dataframe['ema_short'].shift(2)
+        
+        # 计算RSI超卖条件
+        dataframe['rsi_short_oversold'] = (
+            dataframe['rsi_short'].shift(1).rolling(window=self.trend_check_window.value).max() < self.rsi_short_oversold.value
+        )
+        dataframe['rsi_long_oversold'] = (
+            dataframe['rsi_long'].shift(1).rolling(window=self.trend_check_window.value).max() < self.rsi_long_oversold.value
+        )
+        
+        # 计算EMA10连续下跌条件（用于卖出）
+        dataframe['ema_short_falling'] = (
+            dataframe['ema_short'] < dataframe['ema_short'].shift(1).rolling(window=self.momentum_check_window.value).min()
+        )
+        
+        # 计算价差扩大条件（用于卖出）
+        dataframe['ema_diff_expanding'] = (
+            dataframe['ema_diff'] == dataframe['ema_diff'].rolling(window=self.diff_check_window.value).max()
+        )
+
+        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 populated with indicators
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with entry columns populated
+        """
+        
+        # 条件1：EMA多头排列 + 短期趋势加速
+        ema_bullish_cross = (
+            (dataframe['ema_short'] > dataframe['ema_long']) &
+            (dataframe['ema_short_rising'] & dataframe['ema_short_rising_2'])
+        )
+        
+        # 条件2：双周期RSI深度超卖反弹
+        rsi_oversold_bounce = (
+            dataframe['rsi_short_oversold'] &
+            dataframe['rsi_long_oversold']
+        )
+        
+        # 合并买入条件：任一条件满足即产生买入信号
+        dataframe.loc[
+            ema_bullish_cross | rsi_oversold_bounce,
+            '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 populated with indicators
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with exit columns populated
+        """
+        
+        # 卖出条件：EMA10连续下跌 + 空头趋势加速
+        sell_condition = (
+            dataframe['ema_short_falling'] &
+            dataframe['ema_diff_expanding']
+        )
+        
+        dataframe.loc[
+            sell_condition,
+            'exit_long'
+        ] = 1
+
+        return dataframe
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+        """
+        自定义止损逻辑
+        这里保持原有的5%止损，但可以根据需要添加更复杂的止损逻辑
+        """
+        return self.stoploss