Update US stock trading evaluation and add V5 stock strategy

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-08-23 06:58:42 +00:00
parent bd88a4e512
commit 1ecd296af3
3 changed files with 433 additions and 100 deletions
@@ -0,0 +1,144 @@
+import pandas as pd
+import talib.abstract as ta
+from functools import reduce
+from datetime import datetime
+from freqtrade.strategy import (IStrategy, IntParameter, DecimalParameter)
+from freqtrade.persistence import Trade
+
+
+class MultiAssetChannelBreakoutV5Stock(IStrategy):
+    """
+    多股票通道突破策略 V5-Stock（OKX 代币化美股版）。
+
+    基于 MultiAssetChannelBreakoutV5 改造，适配 OKX 现货代币化股票：
+    - spot 模式（1x，无杠杆）
+    - 增加美股交易时段过滤，排除盘前/盘后/周末的薄流动性噪声
+    - 参数面向股票市场特性调整（ADX 阈值降低、滑点容忍放宽）
+    - stoploss / custom_stoploss 逻辑不变（V5 已是 1x 价格维度，spot 直接适用）
+
+    前置条件：
+    - freqtrade spot 模式实例（trading_mode 留空，非 futures）
+    - pair_whitelist 使用 X 前缀代币化股票（如 XAAPL/USDT）
+    - 已验证 OKX 账户可交易 X 前缀标的（小额实单测试）
+    """
+
+    INTERFACE_VERSION = 3
+
+    # --- 1) 基础交易设置 ---
+    minimal_roi = {"0": 100}
+    stoploss = -0.08
+    trailing_stop = False
+    use_custom_stoploss = True
+    timeframe = "15m"
+    can_short = False
+    startup_candle_count = 600
+
+    # --- 2) 可优化参数（默认值面向股票市场调整） ---
+    up_line_span = IntParameter(100, 400, default=340, space="buy", optimize=True)
+    buy_stop_profit_span = IntParameter(50, 200, default=141, space="buy", optimize=True)
+    ma_span_long_days = IntParameter(1, 5, default=2, space="buy", optimize=True)
+    # 降低默认值：股票 ADX 普遍低于加密货币（BTC/ETH 15m 常态 ADX 30-50，股票 15m 常态 15-30）
+    adx_threshold = IntParameter(15, 40, default=25, space="buy", optimize=True)
+    up_line_offset = DecimalParameter(-0.2, 0.4, default=-0.18, decimals=2, space="buy", optimize=True)
+    buy_stop_profit_offset = DecimalParameter(-0.2, 0.2, default=0.19, decimals=2, space="sell", optimize=True)
+    # 滑点容忍：代币化股票流动性薄，从 V5 的 1% 放宽到 1.5%
+    max_slippage = DecimalParameter(0.005, 0.03, default=0.015, decimals=3, space="buy", optimize=True)
+
+    def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
+        dataframe["n_value"] = ta.ATR(dataframe, timeperiod=10)
+
+        ma_length = self.ma_span_long_days.value * 96
+        dataframe["ma_long"] = ta.SMA(dataframe, timeperiod=ma_length)
+
+        base_up_line = dataframe["high"].rolling(window=self.up_line_span.value).max().shift(1)
+        dataframe["up_line"] = base_up_line + (dataframe["n_value"] * self.up_line_offset.value)
+
+        dataframe["bottom_line"] = dataframe["low"].rolling(window=self.buy_stop_profit_span.value).min().shift(1)
+        dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
+
+        # --- 美股交易时段标记 ---
+        # OKX 代币化股票 24/7 有 K 线，但非美股时段流动性极薄、价格漂移。
+        # 只在美股正常交易时段（周一至周五 9:30-16:00 美东）允许入场。
+        if "date" in dataframe.columns:
+            df_date = pd.to_datetime(dataframe["date"], utc=True)
+            et_time = df_date.dt.tz_convert("US/Eastern")
+            et_hour = et_time.dt.hour
+            et_minute = et_time.dt.minute
+            et_weekday = et_time.dt.weekday  # 0=Mon, 6=Sun
+
+            is_market_hours = (
+                (et_weekday < 5)
+                & (
+                    (et_hour > 9)
+                    | ((et_hour == 9) & (et_minute >= 30))
+                )
+                & (et_hour < 16)
+            )
+            dataframe["is_market_hours"] = is_market_hours
+        else:
+            dataframe["is_market_hours"] = True
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
+        conditions = []
+        conditions.append(dataframe["n_value"].notnull())
+        conditions.append(dataframe["up_line"].notnull())
+        conditions.append(dataframe["close"] > dataframe["ma_long"])
+        conditions.append(dataframe["adx"] > self.adx_threshold.value)
+        conditions.append(dataframe["close"] > dataframe["up_line"])
+        conditions.append(dataframe["is_market_hours"])
+
+        if conditions:
+            is_entry = reduce(lambda x, y: x & y, conditions)
+            dataframe.loc[is_entry, "enter_long"] = 1
+            dataframe.loc[is_entry, "enter_tag"] = "trend_breakout"
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
+        offset = self.buy_stop_profit_offset.value
+        exit_line = dataframe["bottom_line"] + (dataframe["n_value"] * offset)
+
+        exit_condition = dataframe["close"] < exit_line
+        dataframe.loc[exit_condition, "exit_long"] = 1
+        dataframe.loc[exit_condition, "exit_tag"] = "channel_exit"
+        return dataframe
+
+    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+        if current_profit > 0.30:
+            return 0.10
+        if current_profit > 0.15:
+            return 0.10
+        if current_profit > 0.05:
+            breakeven_target = trade.open_rate * 1.005
+            return (current_rate - breakeven_target) / current_rate
+        return 1.0
+
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                            time_in_force: str, current_time: datetime, entry_tag: str,
+                            side: str, **kwargs) -> bool:
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+
+        if len(dataframe) < 3:
+            return False
+
+        signal_candle = None
+        for i in range(-1, -4, -1):
+            if dataframe["enter_long"].iloc[i] == 1:
+                signal_candle = dataframe.iloc[i]
+                break
+
+        if signal_candle is None:
+            return False
+
+        if rate <= signal_candle["ma_long"]:
+            return False
+        if rate <= signal_candle["up_line"]:
+            return False
+
+        if rate > signal_candle["close"] * (1 + self.max_slippage.value):
+            return False
+
+        return True