65 lines
3.7 KiB
Diff
65 lines
3.7 KiB
Diff
--- original_strategy.py
|
||
+++ modified_strategy.py
|
||
@@ -297,6 +297,9 @@ class FreqaiPrimer(IStrategy):
|
||
dataframe["bb_lowerband"] = lowerband
|
||
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
|
||
dataframe["volume_mean_20"] = dataframe["volume"].rolling(20).mean()
|
||
dataframe["volume_std_20"] = dataframe["volume"].rolling(20).std()
|
||
dataframe["volume_z_score"] = (dataframe["volume"] - dataframe["volume_mean_20"]) / dataframe["volume_std_20"]
|
||
+ # 计算15分钟ATR和3分钟EMA10、EMA20
|
||
+ dataframe_15m = self.dp.get_pair_dataframe(pair=pair, timeframe="15m")
|
||
+ cuyuan
|
||
+ dataframe_15m['atr15m'] = ta.ATR(dataframe_15m, timeperiod=14)
|
||
+ dataframe['atr15m'] = dataframe_15m['atr15m'].reindex(dataframe.index, method="ffill")
|
||
+ dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
|
||
+ dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
|
||
|
||
@@ -390,8 +390,11 @@ class FreqaiPrimer(IStrategy):
|
||
conditions = []
|
||
|
||
if "&-price_value_divergence" in dataframe.columns:
|
||
cond1 = (dataframe["&-price_value_divergence"] > self.sell_threshold * 1.025)
|
||
- cond2 = (dataframe["rsi"] > 75)
|
||
+ rsi_threshold = 70 if trend_score > 50 else 65 # 动态RSI阈值
|
||
+ cond2 = (dataframe["rsi"] > rsi_threshold)
|
||
+ cond3 = (dataframe['ema10'] < dataframe['ema20']) # 短期均线交叉
|
||
sell_condition = cond1 | cond2
|
||
conditions.append(sell_condition)
|
||
|
||
@@ -442,6 +442,13 @@ class FreqaiPrimer(IStrategy):
|
||
initial_stake_amount = trade.stake_amount / 3
|
||
logger.debug(f"{pair} 首次入场金额: {initial_stake_amount:.2f}, 当前持仓金额: {trade.stake_amount:.2f}, "
|
||
f"加仓次数: {trade.nr_of_successful_entries - 1}")
|
||
|
||
+ # 分批出场：短期涨幅触发
|
||
+ short_term_return = (current_rate - dataframe['close'].shift(3).iloc[-1]) / dataframe['close'].shift(3).iloc[-1]
|
||
+ if short_term_return >= 0.03: # 3%涨幅
|
||
+ reduce_amount = -0.5 * trade.stake_amount # 卖出50%仓位
|
||
+ logger.info(f"{pair} 短期涨幅 {short_term_return*100:.2f}%，卖出50%仓位 {abs(reduce_amount):.2f}")
|
||
+ return (reduce_amount, f"Short-term gain {short_term_return*100:.2f}%")
|
||
+
|
||
# 加仓逻辑
|
||
max_entry_adjustments = self.MAX_ENTRY_POSITION_ADJUSTMENT.value
|
||
@@ -484,12 +484,11 @@ class FreqaiPrimer(IStrategy):
|
||
# 追踪止损逻辑
|
||
trailing_stop_start = self.TRAILING_STOP_START.value
|
||
trailing_stop_distance = self.TRAILING_STOP_DISTANCE.value
|
||
- # 使用 Sigmoid 映射调整追踪止损参数
|
||
- sigmoid = 1 / (1 + np.exp(-0.1 * (trend_score - 50)))
|
||
- trailing_factor = 0.8 + (1.2 - 0.8) * sigmoid # 牛市(100) -> 1.2, 熊市(0) -> 0.8
|
||
- distance_factor = 0.7 + (1.5 - 0.7) * sigmoid # 牛市(100) -> 1.5, 熊市(0) -> 0.7
|
||
- trailing_stop_start *= trailing_factor
|
||
- trailing_stop_distance *= distance_factor
|
||
+ # 使用15分钟ATR动态调整止损距离
|
||
+ atr15m = dataframe['atr15m'].iloc[-1]
|
||
+ trailing_stop_distance = min(max(2 * atr15m / current_rate, 0.01), 0.05) # 2倍ATR，限制在0.01-0.05
|
||
+ if trend_score > 50: # 强趋势时放宽止损
|
||
+ trailing_stop_distance *= 1.2
|
||
+ trailing_stop_price = max_rate * (1 - trailing_stop_distance)
|
||
|
||
if profit_ratio >= trailing_stop_start and not self.trailing_stop_enabled:
|
||
self.trailing_stop_enabled = True
|
||
trade.adjust_min_max_rates(current_rate, current_rate)
|
||
logger.info(f"{pair} 价格上涨超过 {trailing_stop_start*100:.1f}%，启动追踪止损")
|
||
return None
|