简化逻辑
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zhangkun9038@dingtalk.com
parent
f6cca3b5bc
commit
2a7ba6a2e9
@ -27,165 +27,21 @@ class FreqaiPrimer(IStrategy):
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# 用于跟踪市场状态的数据框缓存
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_dataframe_cache = None
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# 用于存储币对的波动系数
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_volatility_coefficients = {}
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# 基准币对 (波动系数设为1.0)
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_benchmark_pair = 'BTC/USDT'
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# 稳定币列表 (波动系数设为0.0)
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_stablecoins = ['USDT', 'USDC', 'BUSD', 'DAI', 'TUSD', 'USDP', 'GUSD', 'USTC']
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# 波动系数缓存有效期 (分钟)
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_volatility_cache_ttl = 60
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# 上次计算波动系数的时间
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_last_volatility_calculation = 0
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def __init__(self, config=None):
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"""初始化策略参数，调用父类初始化方法并接受config参数"""
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super().__init__(config) # 调用父类的初始化方法并传递config
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# 存储从配置文件加载的默认值
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self._trailing_stop_positive_default = 0.004 # 降低默认值以更容易触发跟踪止盈
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# 初始化基准币对和稳定币的波动系数
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self._volatility_coefficients[self._benchmark_pair] = 1.0
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for stablecoin in self._stablecoins:
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# 处理所有稳定币交易对，如USDT/USDC等
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for quote in self._stablecoins:
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if stablecoin != quote:
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pair = f'{stablecoin}/{quote}'
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self._volatility_coefficients[pair] = 0.0
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def _is_stablecoin_pair(self, pair: str) -> bool:
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"""
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判断一个交易对是否为稳定币交易对
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参数:
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- pair: 交易对，如 BTC/USDT
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返回:
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- bool: 是否为稳定币交易对
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"""
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try:
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base, quote = pair.split('/')
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return base in self._stablecoins and quote in self._stablecoins
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except ValueError:
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return False
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def _calculate_volatility(self, pair: str, lookback_period: int = 200, timeframe: str = '1h') -> float:
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"""
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计算一个币对的波动率
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参数:
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- pair: 交易对，如 BTC/USDT
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- lookback_period: 回看期K线数量，默认200
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- timeframe: 时间框架，默认1h
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返回:
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- float: 波动率值
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"""
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try:
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# 获取K线数据
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dataframe = self.dp.get_pair_dataframe(pair=pair, timeframe=timeframe)
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# 确保有足够的K线数据
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if len(dataframe) < lookback_period:
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logger.warning(f"[{pair}] 没有足够的{timeframe} K线数据，需要{lookback_period}根，当前只有{len(dataframe)}根")
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# 如果数据不足，返回0.0或默认值
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return 0.0
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# 获取最近的K线数据
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recent_data = dataframe.iloc[-lookback_period:].copy()
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# 计算收益率 (收盘价变化百分比)
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recent_data['returns'] = recent_data['close'].pct_change()
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# 计算对数收益率 (更适合波动率计算)
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recent_data['log_returns'] = np.log(recent_data['close'] / recent_data['close'].shift(1))
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# 使用对数收益率的标准差作为波动率指标
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volatility = recent_data['log_returns'].std() * np.sqrt(24) # 年化波动率 (假设1天24小时)
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return volatility
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except Exception as e:
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logger.error(f"[{pair}] 计算波动率时出错: {str(e)}")
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return 0.0
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def _calculate_relative_volatility_coefficient(self, pair: str) -> float:
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"""
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计算相对波动系数（以BTC/USDT为基准）
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参数:
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- pair: 交易对，如 ETH/USDT
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返回:
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- float: 相对波动系数
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"""
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try:
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# 检查是否为稳定币交易对
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if self._is_stablecoin_pair(pair):
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return 0.0
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# 检查是否为基准币对
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if pair == self._benchmark_pair:
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return 1.0
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# 计算当前币对的波动率
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pair_volatility = self._calculate_volatility(pair)
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# 计算基准币对的波动率
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benchmark_volatility = self._calculate_volatility(self._benchmark_pair)
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# 避免除以0的情况
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if benchmark_volatility == 0:
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logger.warning(f"基准币对 {self._benchmark_pair} 的波动率为0，无法计算相对波动系数")
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return 0.0
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# 计算相对波动系数
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relative_coefficient = pair_volatility / benchmark_volatility
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return relative_coefficient
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except Exception as e:
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logger.error(f"[{pair}] 计算相对波动系数时出错: {str(e)}")
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return 0.0
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# 只用于adjust_trade_position方法的波动系数获取
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def get_volatility_coefficient(self, pair: str) -> float:
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"""
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获取币对的波动系数（带缓存机制）
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参数:
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- pair: 交易对，如 ETH/USDT
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返回:
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- float: 波动系数（稳定币为0.0，BTC/USDT为1.0，其他币对相对于BTC波动程度）
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获取币对的波动系数（简化版，仅用于adjust_trade_position方法）
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返回固定值1.0，假设所有币对波动相当
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"""
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try:
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# 优先从缓存中获取，无需每次都检查时间戳
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# 这确保了在同一交易周期内多次调用时直接返回缓存值
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if pair in self._volatility_coefficients:
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# 检查是否为基准币对或稳定币对（它们的波动系数固定）
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if pair == self._benchmark_pair or self._is_stablecoin_pair(pair):
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return self._volatility_coefficients[pair]
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# 获取当前时间戳（分钟）
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current_time = int(datetime.datetime.now().timestamp() / 60)
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# 对于非固定波动系数的币对，检查缓存是否有效
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if current_time - self._last_volatility_calculation < self._volatility_cache_ttl:
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return self._volatility_coefficients[pair]
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# 计算新的波动系数
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coefficient = self._calculate_relative_volatility_coefficient(pair)
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# 更新缓存
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self._volatility_coefficients[pair] = coefficient
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# 如果是首次计算或者缓存已过期，更新最后计算时间
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current_time = int(datetime.datetime.now().timestamp() / 60)
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if current_time - self._last_volatility_calculation >= self._volatility_cache_ttl:
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self._last_volatility_calculation = current_time
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# 日志记录
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logger.info(f"更新了波动系数缓存，当前时间: {current_time}, 上一次计算时间: {self._last_volatility_calculation}")
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# 日志记录 - 仅在计算新值时记录
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logger.info(f"[{pair}] 波动系数: {coefficient:.4f}")
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return coefficient
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except Exception as e:
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logger.error(f"[{pair}] 获取波动系数时出错: {str(e)}")
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# 如果出错，返回默认值1.0（假设与BTC波动相当）
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return 1.0
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return 1.0
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# 其他辅助方法可以在这里添加
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@property
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def protections(self):
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@ -674,44 +530,23 @@ class FreqaiPrimer(IStrategy):
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last_candle = dataframe.iloc[-1]
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atr = last_candle['atr']
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# 获取当前币对的波动系数
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volatility_coef = self.get_volatility_coefficient(pair)
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# 获取当前市场状态
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current_state = dataframe['market_state'].iloc[-1] if 'market_state' in dataframe.columns else 'unknown'
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# 基础止损倍数
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base_multiplier = 1.2
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# 根据波动系数调整止损倍数
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adjusted_multiplier = base_multiplier * volatility_coef
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# 更激进的渐进式止损策略，同时考虑波动系数
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# 渐进式止损策略
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if current_profit > 0.05: # 利润超过5%时
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return -3.0 * adjusted_multiplier * atr / current_rate
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return -3.0 * atr / current_rate
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elif current_profit > 0.03: # 利润超过3%时
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return -2.5 * adjusted_multiplier * atr / current_rate
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return -2.5 * atr / current_rate
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elif current_profit > 0.01: # 利润超过1%时
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return -2.0 * adjusted_multiplier * atr / current_rate
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return -2.0 * atr / current_rate
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# 在强劲牛市中，即使小亏损也可以容忍更大回调
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if current_state == 'strong_bull' and current_profit > -0.01:
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return -1.5 * adjusted_multiplier * atr / current_rate
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# 动态调整止损范围
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if current_profit > 0.05: # 利润超过5%时
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return -3.0 * adjusted_multiplier * atr / current_rate
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elif current_profit > 0.03: # 利润超过3%时
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return -2.5 * adjusted_multiplier * atr / current_rate
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elif current_profit > 0.01: # 利润超过1%时
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return -2.0 * adjusted_multiplier * atr / current_rate
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# 在强劲牛市中，即使小亏损也可以容忍更大回调
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if current_state == 'strong_bull' and current_profit > -0.01:
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return -1.8 * adjusted_multiplier * atr / current_rate
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return -1.8 * atr / current_rate
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if atr > 0:
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return -adjusted_multiplier * atr / current_rate
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return -1.2 * atr / current_rate
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return self.stoploss
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def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
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@ -723,9 +558,6 @@ class FreqaiPrimer(IStrategy):
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if trade_age_minutes < 0:
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trade_age_minutes = 0
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# 获取当前币对的波动系数
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volatility_coef = self.get_volatility_coefficient(pair)
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# 使用可优化的线性函数: y = (a * (x + k)) + t
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a = self.roi_param_a.value # 系数a (可优化参数)
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k = self.roi_param_k.value # 偏移量k (可优化参数)
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@ -754,11 +586,6 @@ class FreqaiPrimer(IStrategy):
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exit_ratio = 1.0
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if entry_tag == 'strong_trend':
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exit_ratio *= 0.8
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# 根据波动系数调整退出比例
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# 波动率低的币对可以更激进（降低退出比例，持有更长时间）
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# 波动率高的币对需要更保守（增加退出比例，更快锁定利润）
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exit_ratio = max(0.0, min(1.0, exit_ratio * volatility_coef))
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if dynamic_roi_threshold < 0:
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exit_ratio = 1.0
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@ -826,13 +653,8 @@ class FreqaiPrimer(IStrategy):
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def custom_stake_amount(self, pair: str, current_time: datetime, **kwargs) -> float:
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"""
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根据波动系数动态调整初始仓位大小
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- 波动率高的币对分配较小的仓位
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- 波动率低的币对可以分配较大的仓位
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定义初始仓位大小
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"""
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# 获取当前币对的波动系数
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volatility_coef = self.get_volatility_coefficient(pair)
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# 获取默认的基础仓位大小
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default_stake = self.stake_amount
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@ -841,20 +663,19 @@ class FreqaiPrimer(IStrategy):
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max_stake = kwargs.get('max_stake', default_stake)
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# 根据波动系数调整仓位大小
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# 波动率与仓位大小成反比关系
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# 基础逻辑：波动率高的币对仓位较小，波动率低的币对仓位较大
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if volatility_coef > 0:
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# 使用平方根函数来降低极端波动的影响
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adjusted_stake = default_stake * min(1.5, max(0.5, 1.0 / (volatility_coef ** 0.5)))
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else:
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# 如果波动系数无法获取，使用默认仓位
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adjusted_stake = default_stake
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# 从kwargs获取最小和最大仓位限制
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min_stake = kwargs.get('min_stake', 0.0)
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max_stake = kwargs.get('max_stake', default_stake)
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<<<<<<< Updated upstream
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# 确保调整后的仓位在允许的范围内
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adjusted_stake = max(min_stake, min(adjusted_stake, max_stake))
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#logger.info(f"[{pair}] 基于波动系数调整仓位: 波动系数={volatility_coef:.2f}, 默认仓位={default_stake:.2f}, 调整后仓位={adjusted_stake:.2f}")
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=======
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# 确保仓位在允许的范围内
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adjusted_stake = max(min_stake, min(default_stake, max_stake))
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>>>>>>> Stashed changes
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return adjusted_stake
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@ -872,39 +693,19 @@ class FreqaiPrimer(IStrategy):
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) -> bool:
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"""
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交易买入前的确认函数，用于最终决定是否执行交易
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此处实现剧烈拉升检查逻辑，并根据波动系数调整入场条件
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此处实现剧烈拉升检查逻辑
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"""
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# 默认允许交易
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allow_trade = True
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# 仅对多头交易进行检查
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if side == 'long':
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# 获取当前币对的波动系数
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volatility_coef = self.get_volatility_coefficient(pair)
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# 检查是否处于剧烈拉升的不稳固区域
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is_unstable_region = self.detect_h1_rapid_rise(pair)
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# 基于波动系数调整入场条件的严格程度
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# 波动率高的币对，入场条件更加严格
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# 波动率低的币对，入场条件可以适当放宽
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if is_unstable_region:
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#logger.info(f"[{pair}] 由于检测到剧烈拉升，取消入场交易")
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allow_trade = False
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# 如果币对波动率非常高，可以增加额外的入场限制
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if volatility_coef > 2.0:
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# 对于高波动币对，可以要求更严格的入场条件
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# 例如，检查当前价格是否高于某个移动平均线
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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if len(dataframe) > 20:
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current_price = dataframe['close'].iloc[-1]
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sma20 = dataframe['sma'].iloc[-1] if 'sma' in dataframe.columns else 0
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# 对于高波动币对，要求当前价格必须显著高于均线才允许入场
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if sma20 > 0 and (current_price / sma20) < 1.01:
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#logger.info(f"[{pair}] 由于高波动率且价格未显著高于均线，取消入场交易")
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allow_trade = False
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# 如果没有阻止因素，允许交易
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return allow_trade
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