91 lines
3.1 KiB
Python
91 lines
3.1 KiB
Python
from freqtrade.strategy import IStrategy
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from pandas import DataFrame
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import numpy as np
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class StaticGrid(IStrategy):
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"""
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纯静态网格策略 - ETH/USDT
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价格范围：1500 ~ 4800
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网格间距：50点
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网格数量：66个
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"""
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INTERFACE_VERSION = 3
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timeframe = '4h'
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can_short = False
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# 基础配置
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minimal_roi = {"0": 100}
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stoploss = -0.99
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startup_candle_count = 1
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use_exit_signal = True
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exit_profit_only = False
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ignore_roi_if_entry_signal = True
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# 加仓配置
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position_adjustment_enable = True
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max_entry_position_adjustment = -1 # 无限加仓
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# ========== 静态网格参数 ==========
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GRID_LOW = 1500 # 网格下限
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GRID_HIGH = 4800 # 网格上限
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GRID_STEP = 50 # 网格间距
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GRID_COUNT = 66 # 网格数量
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# 计算网格点
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GRID_LEVELS = np.linspace(GRID_LOW, GRID_HIGH, GRID_COUNT)
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# 静态网格不需要复杂的指标，仅需要价格
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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入场逻辑：
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- 价格每次跌破一个网格点，就在该网格点买入
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- 从上往下遍历所有网格点
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"""
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dataframe['enter_long'] = False
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# 对每个网格点进行判断
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for i, grid_level in enumerate(self.GRID_LEVELS[:-1]): # 不包括最后一个点
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# 价格接近或低于该网格点就买入
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# 使用 <= grid_level * 1.001 来避免浮点数精度问题
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dataframe.loc[
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(dataframe['close'] <= grid_level * 1.001) &
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(dataframe['close'] > (grid_level - self.GRID_STEP)) &
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(dataframe['volume'] > 0),
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'enter_long'
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] = True
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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出场逻辑：
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- 每笔交易进场后，在上一个网格点卖出
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- 每层网格的利润 = GRID_STEP / 当前价格
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"""
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dataframe['exit_long'] = False
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# 简单策略：价格每上升 GRID_STEP，就卖出
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# 这里我们使用一个简化的方法：如果价格上升了 GRID_STEP 点以上，就卖出
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# 计算相对于入场价格的涨幅百分比（目标：50点收益）
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# 假设平均入场价在 2650 左右，50点 = 1.88%
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# 为了泛用，我们设定：只要有 0.9% 的利润就卖出
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dataframe['exit_long'] = (
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dataframe['close'] >= (dataframe['close'].shift(1) * 1.009)
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)
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return dataframe
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def custom_stake_amount(self, pair: str, current_time, current_rate,
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proposed_stake, min_stake, max_stake,
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entry_tag, **kwargs) -> float:
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"""
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每笔投入金额：固定 50 USDT
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目标：总投入 = 50 * 66 = 3300 USDT
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"""
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return 50.0
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