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zhangkun9038@dingtalk.com
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config_examples/staticgrid.json
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50
config_examples/staticgrid.json
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{
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"strategy": "StaticGrid",
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"$schema": "https://schema.freqtrade.io/schema.json",
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"trading_mode": "spot",
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"margin_mode": "isolated",
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"max_open_trades": 100,
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"stake_currency": "USDT",
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"stake_amount": "unlimited",
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"tradable_balance_ratio": 1,
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"process_only_new_candles": false,
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"dry_run": true,
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"timeframe": "4h",
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"dry_run_wallet": 3500,
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"cancel_open_orders_on_exit": true,
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"max_entry_position_adjustment": -1,
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"position_adjustment_enable": true,
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"amount_reserve_percent": 0.05,
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"unfilledtimeout": {
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"entry": 5,
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"exit": 15
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},
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"startup_candle_count": 1,
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"exchange": {
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"name": "okx",
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"pair_whitelist": ["ETH/USDT"],
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"ccxt_config": {
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"enableRateLimit": true,
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"rateLimit": 500
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}
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},
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"entry_pricing": {
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"price_side": "same",
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"use_order_book": true,
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"order_book_top": 1
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},
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"exit_pricing": {
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"price_side": "other",
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"use_order_book": true,
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"order_book_top": 1
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},
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"pairlists": [{"method": "StaticPairList"}],
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"bot_name": "StaticGrid-ETH",
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"initial_state": "running",
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"force_entry_enable": false,
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"internals": {
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"process_throttle_secs": 5,
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"heartbeat_interval": 20,
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"loglevel": "INFO"
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}
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}
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@ -17,44 +17,3 @@ class SmartBBGrid(IStrategy):
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position_adjustment_enable = True
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max_entry_position_adjustment = 1
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# ADX & DI
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dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
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dataframe['plus_di'] = ta.PLUS_DI(dataframe, timeperiod=14)
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dataframe['minus_di'] = ta.MINUS_DI(dataframe, timeperiod=14)
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# 平衡布林带倍数：既要频繁，又要精准
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dataframe['bb_mult'] = np.where(
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dataframe['minus_di'] > dataframe['plus_di'],
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1.8, # 熊市适度
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1.6 # 牛市适度 ← 兼顾频率和准确率
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)
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# qtpylib 动态布林带
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bollinger = qtpylib.bollinger_bands(
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qtpylib.typical_price(dataframe),
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window=20,
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stds=dataframe['bb_mult']
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)
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dataframe['bb_lower'] = bollinger['lower']
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dataframe['bb_upper'] = bollinger['upper']
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dataframe['bb_mid'] = bollinger['mid']
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return dataframe
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# ← 这里结束
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# 纯网格逻辑：只要价格接近下轨就买入
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dataframe['enter_long'] = (
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(dataframe['close'] <= dataframe['bb_lower'] * 1.02) & # 下轨附近 2% 范围
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(dataframe['volume'] > 0)
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)
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# 纯网格逻辑：只要小幅反弹就卖出
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dataframe['exit_long'] = (
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(dataframe['close'] >= dataframe['bb_lower'] * 1.005) # 反弹 0.5% 就卖
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)
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return dataframe
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90
freqtrade/templates/staticgrid.py
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90
freqtrade/templates/staticgrid.py
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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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