optimize trade strategy

auto_update_market_data.py

@@ -0,0 +1,29 @@
+import schedule
+import time
+import datetime
+import core.logger as logging
+import subprocess
+import os
+
+logger = logging.logger
+# 定义要执行的任务
+def run_script():
+    start_time = time.time()
+    logger.info(f"Executing script at: {datetime.datetime.now()}")
+    output_file = r'./output/auto_schedule.txt'
+    with open(output_file, 'a') as f:
+        f.write(f"Task ran at {datetime.datetime.now()}\n")
+    python_path = r"D:\miniconda3\envs\okx\python.exe"
+    script_path = r"D:\python_projects\crypto_quant\huge_volume_main.py"
+    subprocess.run([python_path, script_path])
+    end_time = time.time()
+    logger.info(f"Script execution time: {end_time - start_time} seconds")
+# 设置每小时运行一次
+interval = 60 * 60
+schedule.every(interval).seconds.do(run_script)
+
+# 保持程序运行并检查调度
+logger.info("Scheduler started. Press Ctrl+C to stop.")
+while True:
+    schedule.run_pending()
+    time.sleep(1)

core/statistics/ma_break_statistics.py

@@ -171,9 +171,9 @@ class MaBreakStatistics:
                     ma_cross = str(ma_cross)
                     buy_condition = False
                     if all_change:
-                        buy_condition = (ma5 > ma10 and ma10 > ma20 and ma20 > ma30) and (close > ma20)
+                        buy_condition = (ma_cross == "5上穿10") and (ma5 > ma10 and ma10 > ma20 and ma20 > ma30) and (close > ma20)
                     else:
-                        buy_condition = ma_cross == "5上穿10" and (ma5 > ma10)
+                        buy_condition = (ma_cross == "5上穿10") and (ma5 > ma10)
                     if buy_condition:
                         ma_break_market_data_pair = {}
                         ma_break_market_data_pair["symbol"] = symbol

core/trade/mean_reversion_sandbox.py

@@ -4,7 +4,7 @@ import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
-from datetime import datetime
+from datetime import datetime, timedelta
 import re
 from openpyxl import Workbook
 from openpyxl.drawing.image import Image
@@ -45,21 +45,22 @@ class MeanReversionSandbox:
         desc_dict = {
             "买入": [
                 "1. 窗口周期为100, 即100个K线",
-                "2. 当前low_10_low为1, 即当前最低价格在窗口周期的10分位以下",
+                "2. 当前close_10_low为1, 即当前收盘价在窗口周期的10分位以下",
                 "3. 之前2个K线与当前K线, 存在任意一个K线huge_volume为1, 即存在一个K线是巨量",
-                "4. 当前K线为阳线, 即close > open",
+                "4. 当前K线为阳线, 即close > open或者K线为一字, 长倒T线, 倒T线, 长十字星, 十字星",
+                # "5. 相同symbol的1H当前周期, ma5大于ma10",
+                # "5. KDJ, RSI, BOLL任意一个指标出现超卖",
             ],
             "止损": ["跌幅超过下跌周期跌幅中位数, 即down_median后卖出"],
             "止盈": {
                 "solution_1": [
                     "高位放量止盈 - 简易版",
-                    "1. 当前high_80_high为1或者high_90_high为1",
+                    "1. 当前close_80_high为1或者close_90_high为1",
                     "2. 之前2个K线与当前K线, 存在任意一个K线huge_volume为1, 即存在一个K线是巨量",
                 ],
                 "solution_2": [
                     "高位放量止盈 - 复杂版",
-                    "前提条件"
+                    "前提条件" "1. 当前close_80_high为1或者close_90_high为1",
-                    "1. 当前high_80_high为1或者high_90_high为1",
                     "2. 之前2个K线与当前K线, 存在任意一个K线huge_volume为1, 即存在一个K线是巨量",
                     "以下两个条件, 任一满足即可",
                     "1. K线为阴线, 即close < open",
@@ -68,9 +69,10 @@ class MeanReversionSandbox:
                 ],
                 "solution_3": [
                     "上涨波段盈利中位数止盈法",
-                    "1. 超过波段中位数涨幅, 即up_median后, 记录当前价格, 继续持仓",
+                    "1. 超过波段中位数涨幅, 即up_median/ 到达价位90分位/ 任意技术指标出现中度超卖, 记录当前价格, 继续持仓",
                     "2. 之后一个周期, 如果价格上涨, 则记录该价格继续持仓",
                     "3. 之后一个周期, 如果价格跌到记录价格之下, 则卖出",
+                    "4. 如果买入时ma5小于ma10, 过程中ma5大于ma10, 进行记录。之后出现ma5小于ma10, 则卖出",
                 ],
             },
         }
@@ -160,7 +162,8 @@ class MeanReversionSandbox:
                 trade_pair_dict["buy_huge_volume"] = row["huge_volume"]
                 trade_pair_dict["buy_volume_ratio"] = row["volume_ratio"]
                 trade_pair_dict["buy_k_shape"] = row["k_shape"]
-                trade_pair_dict["buy_low_10_low"] = row["low_10_low"]
+                trade_pair_dict["buy_close_10_low"] = row["close_10_low"]
+                trade_pair_dict["buy_ma5_lt_ma10"] = row["ma5"] < row["ma10"]
                 continue
 
             if trade_pair_dict.get("buy_timestamp", None) is not None:
@@ -188,22 +191,27 @@ class MeanReversionSandbox:
                     trade_pair_dict["sell_huge_volume"] = row["huge_volume"]
                     trade_pair_dict["sell_volume_ratio"] = row["volume_ratio"]
                     trade_pair_dict["sell_k_shape"] = row["k_shape"]
-                    trade_pair_dict["sell_high_80_high"] = row["high_80_high"]
+                    trade_pair_dict["sell_close_80_high"] = row["close_80_high"]
-                    trade_pair_dict["sell_high_90_high"] = row["high_90_high"]
+                    trade_pair_dict["sell_close_90_high"] = row["close_90_high"]
-                    trade_pair_dict["sell_low_10_low"] = row["low_10_low"]
+                    trade_pair_dict["sell_close_10_low"] = row["close_10_low"]
-                    trade_pair_dict["sell_low_20_low"] = row["low_20_low"]
+                    trade_pair_dict["sell_close_20_low"] = row["close_20_low"]
                     trade_pair_dict["profit_pct"] = round(
                         (trade_pair_dict["sell_close"] - trade_pair_dict["buy_close"])
                         / trade_pair_dict["buy_close"]
                         * 100,
                         4,
                     )
-                    if trade_pair_dict["sell_type"] == "止盈" and trade_pair_dict["profit_pct"] < 0:
+                    if trade_pair_dict["profit_pct"] <= 0:
                         trade_pair_dict["sell_type"] = "止损"
+                    else:
+                        trade_pair_dict["sell_type"] = "止盈"
                     if trade_pair_dict.get("last_max_close", None) is not None:
                         # remove last_max_close
                         trade_pair_dict.pop("last_max_close")
 
+                    if trade_pair_dict.get("process_ma5_gt_ma10", None) is not None:
+                        trade_pair_dict.pop("process_ma5_gt_ma10")
+
                     trade_list.append(trade_pair_dict)
                     trade_pair_dict = {}
 
@@ -219,30 +227,75 @@ class MeanReversionSandbox:
     ):
         """
         买入条件
-        1. 窗口周期为100, 即100个K线
+        1. 窗口周期为100, 即100个K线,
-        2. 当前low_10_low为1, 即当前最低价格在窗口周期的10分位以下
+        2. 当前close_10_low为1, 即当前收盘价在窗口周期的10分位以下,
-        3. 之前2个K线与当前K线, 存在任意一个K线huge_volume为1, 即存在一个K线是巨量
+        3. 之前2个K线与当前K线, 存在任意一个K线huge_volume为1, 即存在一个K线是巨量,
-        4. 当前K线为阳线, 即close > open
+        4. (当前K线为阳线, 即close > open)或者K线为一字, 长倒T线, 倒T线, 长十字星, 十字星,
-        5. TODO: 考虑K线形态
         """
         if index < 2:
             return False
-        if row["close"] <= row["open"]:
+        if row["close"] <= row["open"] and row["k_shape"] not in [
+            "一字",
+            "长倒T线",
+            "倒T线",
+            "长十字星",
+            "十字星",
+        ]:
             return False
 
-        if row["low_10_low"] != 1:
+        if row["close_10_low"] != 1:
             return False
 
-        # 如果当前与前两个K线，huge_volume都不为1，则返回False
+        # 如果当前与前两个K线, huge_volume都不为1, 则返回False
         if (
             row["huge_volume"] != 1
             and market_data.loc[index - 1, "huge_volume"] != 1
             and market_data.loc[index - 2, "huge_volume"] != 1
         ):
             return False
-        logger.info(f"符合买入条件")
+
+        # if not self.check_metrics_over_sell(row):
+        #     return False
+        # latest_1h_data = self.get_latest_1h_data(row["symbol"], row["date_time"])
+        # if latest_1h_data is None or len(latest_1h_data) == 0:
+        #     logger.info(f"符合买入条件")
+        #     return True
+        # # 当前小时周期的ma5小于ma10, 表明空头趋势, 则返回False
+        # elif (
+        #     not pd.isna(latest_1h_data["ma5"])
+        #     and not pd.isna(latest_1h_data["ma10"])
+        #     and latest_1h_data["ma5"] < latest_1h_data["ma10"]
+        # ):
+        #     # logger.info(f"当前小时周期的ma5小于ma10, 空头趋势, 不符合买入条件")
+        #     return False
+        # else:
+        #     logger.info(f"符合买入条件")
+        #     return True
+
         return True
 
+    def get_latest_1h_data(self, symbol: str, current_date_time: str):
+        bar = "1H"
+        # 根据current_date_time, 获取当前时间往前推1H的日期时间,
+        # 如当前时间为2025-08-20 10:20:05, 则获取2025-08-20 09:00:00
+        before_date_time = datetime.strptime(current_date_time, "%Y-%m-%d %H:%M:%S")
+        before_date_time = before_date_time - timedelta(hours=1)
+        # current_date_time取整数小时，如2025-08-20 10:20:05, 取2025-08-20 10:00:00
+        before_date_time = before_date_time.replace(minute=0, second=0, microsecond=0)
+        before_date_time = before_date_time.strftime("%Y-%m-%d %H:%M:%S")
+        end_date_time = datetime.strptime(current_date_time, "%Y-%m-%d %H:%M:%S")
+        end_date_time = end_date_time.replace(minute=0, second=0, microsecond=0)
+        end_date_time = end_date_time - timedelta(seconds=1)
+        end_date_time = end_date_time.strftime("%Y-%m-%d %H:%M:%S")
+        latest_1h_data = self.db_merge_market_huge_volume.merge_market_huge_volume(
+            symbol, bar, 100, before_date_time, end_date_time
+        )
+        if latest_1h_data is None or len(latest_1h_data) == 0:
+            return None
+        # 只获取第一行数据
+        latest_1h_data = latest_1h_data.iloc[0]
+        return latest_1h_data
+
     def check_stop_loss_condition(self, trade_pair_dict: dict, row: pd.Series):
         symbol = trade_pair_dict["symbol"]
         bar = trade_pair_dict["bar"]
@@ -282,9 +335,7 @@ class MeanReversionSandbox:
                     market_data, row, index
                 )
             elif self.solution == "solution_3":
-                return self.check_take_profit_condition_solution_3(
+                return self.check_take_profit_condition_solution_3(trade_pair_dict, row)
-                    trade_pair_dict, row
-                )
             else:
                 raise ValueError(f"Invalid strategy name: {self.solution}")
         except Exception as e:
@@ -299,10 +350,10 @@ class MeanReversionSandbox:
     ):
         """
         高位放量止盈 - 简易版
-        1. 当前high_80_high为1或者high_90_high为1
+        1. 当前close_80_high为1或者close_90_high为1
         2. 之前2个K线与当前K线, 存在任意一个K线huge_volume为1, 即存在一个K线是巨量
         """
-        if row["high_80_high"] != 1 and row["high_90_high"] != 1:
+        if row["close_80_high"] != 1 and row["close_90_high"] != 1:
             return False
         if (
             row["huge_volume"] != 1
@@ -322,7 +373,7 @@ class MeanReversionSandbox:
         """
         高位放量止盈 - 复杂版
         前提条件
-        1. 当前high_80_high为1或者high_90_high为1
+        1. 当前close_80_high为1或者close_90_high为1
         2. 之前2个K线与当前K线, 存在任意一个K线huge_volume为1, 即存在一个K线是巨量
         以下两个条件, 任一满足即可
         1. K线为阴线, 即close < open
@@ -334,25 +385,46 @@ class MeanReversionSandbox:
         if row["close"] < row["open"]:
             logger.info(f"符合高位放量止盈 - 复杂版条件")
             return True
-        elif row["k_shape"] in ["一字", "长吊锤线", "吊锤线", "长倒T线", "倒T线", "长十字星", "十字星", "长上影线纺锤体", "长下影线纺锤体"]:
+        elif row["k_shape"] in [
+            "一字",
+            "长吊锤线",
+            "吊锤线",
+            "长倒T线",
+            "倒T线",
+            "长十字星",
+            "十字星",
+            "长上影线纺锤体",
+            "长下影线纺锤体",
+        ]:
             logger.info(f"符合高位放量止盈 - 复杂版条件")
             return True
         else:
             return False
 
     def check_take_profit_condition_solution_3(
-        self,
+        self, trade_pair_dict: dict, row: pd.Series
-        trade_pair_dict: dict,
-        row: pd.Series
     ):
         """
-        上涨波段盈利中位数止盈法
+        上涨波段盈利阶段止盈法
-        1. 超过波段中位数涨幅, 即up_median后, 记录当前价格, 继续持仓
+        1. 超过波段中位数涨幅, 即up_median/ 到达价位90分位/ 任意技术指标出现中度超卖, 记录当前价格, 继续持仓
         2. 之后一个周期, 如果价格上涨, 则记录该价格继续持仓
         3. 之后一个周期, 如果价格跌到记录价格之下, 则卖出
+        4. 如果买入时ma5小于ma10, 过程中ma5大于ma10, 进行记录。之后出现ma5小于ma10, 则卖出
         """
         current_close = row["close"]
         last_max_close = trade_pair_dict.get("last_max_close", None)
+
+        if trade_pair_dict["buy_ma5_lt_ma10"]:
+            if trade_pair_dict.get("process_ma5_gt_ma10", None):
+                if row["ma5"] < row["ma10"]:
+                    logger.info(f"MA5小于MA10发生转势, 卖出")
+                    return True
+
+            if row["ma5"] > row["ma10"]:
+                trade_pair_dict["process_ma5_gt_ma10"] = True
+            else:
+                trade_pair_dict["process_ma5_gt_ma10"] = False
+
         if last_max_close is not None:
             if current_close >= last_max_close:
                 logger.info(f"价格上涨, 继续持仓")
@@ -372,10 +444,73 @@ class MeanReversionSandbox:
                 ].values[0]
                 / 100
             )
-            
+
+            need_record = False
             buy_close = trade_pair_dict["buy_close"]
             price_chg = (current_close - buy_close) / buy_close
             if price_chg > up_median:
                 logger.info(f"当前价格上涨超过波段中位数涨幅, 记录当前价格")
+                need_record = True
+            elif self.check_metrics_over_buy(row):
+                logger.info(f"技术指标超买, 记录当前价格")
+                need_record = True
+            elif row["close_90_high"] == 1:
+                logger.info(f"到达价位90分位, 记录当前价格")
+                need_record = True
+            else:
+                need_record = False
+            if need_record:
                 trade_pair_dict["last_max_close"] = current_close
             return False
+
+    def check_metrics_over_buy(self, row: pd.Series):
+        """
+        检查技术指标是否出现中度超买
+        KDJ
+        K：85.00
+        D：80.00
+        J：100.00
+        说明：K 和 D 进一步上升, J 显著高于100, 表示超买加剧, 回调概率增加, 但可能仍需确认。
+        RSI 14
+        RSI：80.00
+        说明：RSI 进一步上升, 超买程度加深, 市场可能接近短期顶部, 回调概率增加。
+        BOLL
+        价格位置：价格突破上轨, 偏离上轨约 +2.00%（即价格 = 上轨 × 1.02）
+        说明：价格显著突破上轨, 超买程度加深, 可能预示短期回调或反转
+        """
+        if row["kdj_k"] > 85 and row["kdj_d"] > 80 and row["kdj_j"] > 100:
+            logger.info(f"KDJ超买")
+            return True
+        if row["rsi_14"] > 80:
+            logger.info(f"RSI超买")
+            return True
+        if row["boll_upper"] * 1.02 < row["close"]:
+            logger.info(f"BOLL超买")
+            return True
+        return False
+
+    def check_metrics_over_sell(self, row: pd.Series):
+        """
+        检查技术指标是否出现超卖
+        KDJ
+        K: 25.00
+        D: 30.00
+        J: 20.00
+
+        RSI 14
+        RSI:30.00
+        说明: RSI 进一步下降, 超卖程度加深, 市场可能接近短期底部, 反弹概率增加。
+
+        BOLL
+        价格位置: 价格接近下轨
+        """
+        if row["kdj_k"] < 25 and row["kdj_d"] < 30 and row["kdj_j"] < 20:
+            logger.info(f"KDJ超卖")
+            return True
+        if row["rsi_14"] < 30:
+            logger.info(f"RSI超卖")
+            return True
+        if row["boll_lower"] >= row["close"]:
+            logger.info(f"BOLL超卖")
+            return True
+        return False

requirements.txt

@@ -1,8 +1,12 @@
 okx>=2.1.2
+python-okx >= 0.3.9
 pandas>=2.0.0
 requests>=2.25.0
 sqlalchemy >= 2.0.41
 pymysql >= 1.1.1
 wechatpy >= 1.8.18
 seaborn >= 0.13.2
-schedule >= 1.2.2
+schedule >= 1.2.2
+xlsxwriter >= 3.2.5
+openpyxl >= 3.1.5
+cryptography >= 3.4.8

trade_sandbox_main.py

@@ -23,14 +23,21 @@ logger = logging.logger
 
 
 class MeanReversionSandboxMain:
-    def __init__(self, start_date: str, end_date: str, window_size: int):
+    def __init__(self, start_date: str, end_date: str, window_size: int, only_5m: bool = False, solution_list: list = None):
         self.symbols = MONITOR_CONFIG.get("volume_monitor", {}).get(
             "symbols", ["XCH-USDT"]
         )
-        self.bars = MONITOR_CONFIG.get("volume_monitor", {}).get(
+        self.only_5m = only_5m
-            "bars", ["5m", "15m", "30m", "1H"]
+        if only_5m:
-        )
+            self.bars = ["5m"]
-        self.solution_list = ["solution_1", "solution_2", "solution_3"]
+        else:
+            self.bars = MONITOR_CONFIG.get("volume_monitor", {}).get(
+                "bars", ["5m", "15m", "30m", "1H"]
+            )
+        if solution_list is None:
+            self.solution_list = ["solution_1", "solution_2", "solution_3"]
+        else:
+            self.solution_list = solution_list
         self.start_date = start_date
         self.end_date = end_date
         self.window_size = window_size
@@ -176,8 +183,13 @@ class MeanReversionSandboxMain:
         sheet_name = f"{solution}_chart"
         chart_dict[sheet_name] = {}
         for y_axis_field in y_axis_fields:
-            # 绘制2x2的画布
+            if self.only_5m:
-            fig, axs = plt.subplots(2, 2, figsize=(10, 10))
+                fig, axs = plt.subplots(1, 1, figsize=(10, 10))
+                # 当只有一个子图时，将axs包装成数组以便统一处理
+                axs = np.array([[axs]])
+            else:
+                # 绘制2x2的画布
+                fig, axs = plt.subplots(2, 2, figsize=(10, 10))
             for j, bar in enumerate(bars_in_order):
                 ax = axs[j // 2, j % 2]
                 bar_data = stat_data[stat_data["bar"] == bar].copy()
@@ -191,6 +203,21 @@ class MeanReversionSandboxMain:
                     palette=colors,
                     ax=ax,
                 )
+                
+                # 在柱子上方添加数值标签
+                for i, (idx, row) in enumerate(bar_data.iterrows()):
+                    value = row[y_axis_field]
+                    # 根据数值类型格式化标签
+                    if "ratio" in y_axis_field:
+                        label = f"{value:.2f}%"
+                    else:
+                        label = f"{value:.4f}"
+                    
+                    # 在柱子上方显示数值
+                    ax.text(i, value, label, 
+                           ha='center', va='bottom',
+                           fontsize=9, fontweight='bold')
+                
                 ax.set_ylabel(y_axis_field)
                 ax.set_xlabel("symbol")
                 ax.set_title(f"{solution} {bar}")
@@ -203,9 +230,10 @@ class MeanReversionSandboxMain:
                     label.set_horizontalalignment("right")
             # 隐藏未使用的subplot
             total_used = len(bars_in_order)
-            for k in range(total_used, 4):
+            if not self.only_5m:
-                ax = axs[k // 2, k % 2]
+                for k in range(total_used, 4):
-                ax.axis("off")
+                    ax = axs[k // 2, k % 2]
+                    ax.axis("off")
             fig.tight_layout()
             file_name = f"{solution}_{y_axis_field}.png"
             fig.savefig(os.path.join(save_path, file_name))
@@ -271,7 +299,8 @@ class MeanReversionSandboxMain:
 if __name__ == "__main__":
     start_date = "2025-05-15 00:00:00"
     end_date = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    solution_list = ["solution_3"]
     mean_reversion_sandbox_main = MeanReversionSandboxMain(
-        start_date=start_date, end_date=end_date, window_size=100
+        start_date=start_date, end_date=end_date, window_size=100, only_5m=True, solution_list=solution_list
     )
     mean_reversion_sandbox_main.batch_mean_reversion_sandbox()