optimize ma_break quant logic

core/trade/ma_break_statistics.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
 import json
 from openpyxl import Workbook
@@ -12,8 +12,9 @@ from openpyxl.drawing.image import Image
 import openpyxl
 from openpyxl.styles import Font
 from PIL import Image as PILImage
-from config import OKX_MONITOR_CONFIG, US_STOCK_MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
+from config import OKX_MONITOR_CONFIG, US_STOCK_MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE, BINANCE_MONITOR_CONFIG
 from core.db.db_market_data import DBMarketData
+from core.db.db_binance_data import DBBinanceData
 from core.db.db_huge_volume_data import DBHugeVolumeData
 from core.utils import timestamp_to_datetime, transform_date_time_to_timestamp
 
@@ -32,7 +33,7 @@ class MaBreakStatistics:
     之间的涨跌幅
     """
 
-    def __init__(self, is_us_stock: bool = False):
+    def __init__(self, is_us_stock: bool = False, is_binance: bool = False):
         mysql_user = MYSQL_CONFIG.get("user", "xch")
         mysql_password = MYSQL_CONFIG.get("password", "")
         if not mysql_password:
@@ -42,28 +43,45 @@ class MaBreakStatistics:
         mysql_database = MYSQL_CONFIG.get("database", "okx")
 
         self.db_url = f"mysql+pymysql://{mysql_user}:{mysql_password}@{mysql_host}:{mysql_port}/{mysql_database}"
-        self.db_market_data = DBMarketData(self.db_url)
+        
         self.db_huge_volume_data = DBHugeVolumeData(self.db_url)
+        self.is_us_stock = is_us_stock
+        self.is_binance = is_binance
         if is_us_stock:
             self.symbols = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
                 "symbols", ["QQQ"]
             )
+            self.bars = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
+                "bars", ["5m"]
+            )
+            self.initial_date = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
+                "initial_date", "2014-11-30 00:00:00"
+            )
+            self.db_market_data = DBMarketData(self.db_url)
+        else:
+            if is_binance:
+                self.symbols = BINANCE_MONITOR_CONFIG.get("volume_monitor", {}).get(
+                    "symbols", ["BTC-USDT"]
+                )
+                self.bars = ["30m", "1H"]
+                self.initial_date = BINANCE_MONITOR_CONFIG.get("volume_monitor", {}).get(
+                    "initial_date", "2017-08-16 00:00:00"
+                )
+                self.db_market_data = DBBinanceData(self.db_url)
             else:
                 self.symbols = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
                 "symbols", ["XCH-USDT"]
                 )
-        if is_us_stock:
-            self.bars = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
-                "bars", ["5m"]
-            )
-        else:
                 self.bars = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
                 "bars", ["5m", "15m", "30m", "1H"]
                 )
-        self.stats_output_dir = "./output/trade_sandbox/ma_strategy/excel/"
+                self.initial_date = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
-        os.makedirs(self.stats_output_dir, exist_ok=True)
+                    "initial_date", "2025-05-15 00:00:00"
-        self.stats_chart_dir = "./output/trade_sandbox/ma_strategy/chart/"
+                )
-        os.makedirs(self.stats_chart_dir, exist_ok=True)
+                self.db_market_data = DBMarketData(self.db_url)
+        if len(self.initial_date) > 10:
+            self.initial_date = self.initial_date[:10]
+        self.end_date = datetime.now().strftime("%Y-%m-%d")
         self.trade_strategy_config = self.get_trade_strategy_config()
         self.main_strategy = self.trade_strategy_config.get("均线系统策略", None)
 
@@ -73,14 +91,18 @@ class MaBreakStatistics:
         return trade_strategy_config
 
     def batch_statistics(self, strategy_name: str = "全均线策略"):
-        self.stats_output_dir = (
+        if self.is_us_stock:
-            f"./output/trade_sandbox/ma_strategy/excel/{strategy_name}/"
+            self.stats_output_dir = f"./output/trade_sandbox/ma_strategy/us_stock/excel/{strategy_name}/"
-        )
+            self.stats_chart_dir = f"./output/trade_sandbox/ma_strategy/us_stock/chart/{strategy_name}/"
+        elif self.is_binance:
+            self.stats_output_dir = f"./output/trade_sandbox/ma_strategy/binance/excel/{strategy_name}/"
+            self.stats_chart_dir = f"./output/trade_sandbox/ma_strategy/binance/chart/{strategy_name}/"
+        else:
+            self.stats_output_dir = f"./output/trade_sandbox/ma_strategy/okx/excel/{strategy_name}/"
+            self.stats_chart_dir = f"./output/trade_sandbox/ma_strategy/okx/chart/{strategy_name}/"
         os.makedirs(self.stats_output_dir, exist_ok=True)
-        self.stats_chart_dir = (
-            f"./output/trade_sandbox/ma_strategy/chart/{strategy_name}/"
-        )
         os.makedirs(self.stats_chart_dir, exist_ok=True)
+
         ma_break_market_data_list = []
         market_data_pct_chg_list = []
         if strategy_name not in self.main_strategy.keys() or strategy_name is None:
@@ -276,16 +298,11 @@ class MaBreakStatistics:
         return strategy_info_df
 
     def trade_simulate(self, symbol: str, bar: str, strategy_name: str = "全均线策略"):
-        market_data = self.db_market_data.query_market_data_by_symbol_bar(
+        market_data = self.get_full_data(symbol, bar)
-            symbol, bar, start=None, end=None
-        )
         if market_data is None or len(market_data) == 0:
             logger.warning(f"获取{symbol} {bar} 数据失败")
             return None, None
         else:
-            market_data = pd.DataFrame(market_data)
-            market_data.sort_values(by="timestamp", ascending=True, inplace=True)
-            market_data.reset_index(drop=True, inplace=True)
             logger.info(f"获取{symbol} {bar} 数据成功，数据条数: {len(market_data)}")
             # 获得ma5, ma10, ma20, ma30不为空的行
             market_data = market_data[
@@ -299,7 +316,7 @@ class MaBreakStatistics:
             )
             # 计算volume_ma5
             market_data["volume_ma5"] = market_data["volume"].rolling(window=5).mean()
-            # 获得5上穿10且ma5 > ma10 > ma20 > ma30且close > ma20的行,成交量较前5日均量放大20%以上
+
             market_data["volume_pct_chg"] = (
                 market_data["volume"] - market_data["volume_ma5"]
             ) / market_data["volume_ma5"]
@@ -311,9 +328,14 @@ class MaBreakStatistics:
             market_data.reset_index(drop=True, inplace=True)
             ma_break_market_data_pair_list = []
             ma_break_market_data_pair = {}
+            if self.is_us_stock:
+                date_time_field = "date_time_us"
+            else:
+                date_time_field = "date_time"
             for index, row in market_data.iterrows():
                 ma_cross = row["ma_cross"]
                 timestamp = row["timestamp"]
+                date_time = row[date_time_field]
                 close = row["close"]
                 ma5 = row["ma5"]
                 ma10 = row["ma10"]
@@ -336,9 +358,7 @@ class MaBreakStatistics:
                         ma_break_market_data_pair["symbol"] = symbol
                         ma_break_market_data_pair["bar"] = bar
                         ma_break_market_data_pair["begin_timestamp"] = timestamp
-                        ma_break_market_data_pair["begin_date_time"] = (
+                        ma_break_market_data_pair["begin_date_time"] = date_time
-                            timestamp_to_datetime(timestamp)
-                        )
                         ma_break_market_data_pair["begin_close"] = close
                         ma_break_market_data_pair["begin_ma5"] = ma5
                         ma_break_market_data_pair["begin_ma10"] = ma10
@@ -358,9 +378,7 @@ class MaBreakStatistics:
 
                     if sell_condition:
                         ma_break_market_data_pair["end_timestamp"] = timestamp
-                        ma_break_market_data_pair["end_date_time"] = (
+                        ma_break_market_data_pair["end_date_time"] = date_time
-                            timestamp_to_datetime(timestamp)
-                        )
                         ma_break_market_data_pair["end_close"] = close
                         ma_break_market_data_pair["end_ma5"] = ma5
                         ma_break_market_data_pair["end_ma10"] = ma10
@@ -412,6 +430,59 @@ class MaBreakStatistics:
             else:
                 return None, None
     
+    def get_full_data(self, symbol: str, bar: str = "5m"):
+        """
+        分段获取数据，并将数据合并为完整数据
+        分段依据：如果end_date与start_date相差超过一年，则每次取一年数据
+        """
+        data = pd.DataFrame()
+        start_date = datetime.strptime(self.initial_date, "%Y-%m-%d")
+        end_date = datetime.strptime(self.end_date, "%Y-%m-%d") + timedelta(days=1)
+        fields = [
+            "symbol",
+            "bar",
+            "timestamp",
+            "date_time",
+            "date_time_us",
+            "open",
+            "high",
+            "low",
+            "close",
+            "volume",
+            "sar_signal",
+            "ma5",
+            "ma10",
+            "ma20",
+            "ma30",
+            "ma_cross",
+            "dif",
+            "dea",
+            "macd",
+        ]
+        while start_date < end_date:
+            current_end_date = min(start_date + timedelta(days=180), end_date)
+            start_date_str = start_date.strftime("%Y-%m-%d")
+            current_end_date_str = current_end_date.strftime("%Y-%m-%d")
+            logger.info(
+                f"获取{symbol}数据：{start_date_str}至{current_end_date_str}"
+            )
+            current_data = self.db_market_data.query_market_data_by_symbol_bar(
+                symbol, bar, fields, start=start_date_str, end=current_end_date_str
+            )
+            if current_data is not None and len(current_data) > 0:
+                current_data = pd.DataFrame(current_data)
+                data = pd.concat([data, current_data])
+            start_date = current_end_date
+        data.drop_duplicates(inplace=True)
+        if self.is_us_stock:
+            date_time_field = "date_time_us"
+        else:
+            date_time_field = "date_time"
+        data.sort_values(by=date_time_field, inplace=True)
+        data.reset_index(drop=True, inplace=True)
+        return data
+
+
     def fit_strategy(
         self,
         strategy_name: str = "全均线策略",
@@ -734,12 +805,12 @@ class MaBreakStatistics:
                     
                     # 设置x轴标签
                     plt.xticks(symbol_bar_data["end_date_time"].iloc[label_indices], 
-                              symbol_bar_data["end_date_time"].iloc[label_indices].dt.strftime('%m-%d %H:%M'), 
+                              symbol_bar_data["end_date_time"].iloc[label_indices].dt.strftime('%Y%m%d %H:%M'), 
                               rotation=45, ha='right')
                 else:
                     # 如果数据点较少，全部显示
                     plt.xticks(symbol_bar_data["end_date_time"], 
-                              symbol_bar_data["end_date_time"].dt.strftime('%m-%d %H:%M'), 
+                              symbol_bar_data["end_date_time"].dt.strftime('%Y%m%d %H:%M'), 
                               rotation=45, ha='right')
                 
                 plt.tight_layout()

orb_trade_main.py

@@ -28,11 +28,20 @@ def main():
     else:
         start_date = "2024-01-01"
     end_date = datetime.now().strftime("%Y-%m-%d")
+
+    # 原值 盈利目标倍数（默认10倍$R，即10R）
     profit_target_multiple = 10
+    # 新值 盈利目标倍数（默认20倍$R，即10R） -- 20250909
+    # profit_target_multiple = 20
+
     initial_capital = 25000
     max_leverage = 4
     risk_per_trade = 0.01
-    commission_per_share = 0.0005
+    # if is_us_stock:
+    #     commission_per_share = 0.0005
+    # else:
+    #     commission_per_share = 0
+    # commission_per_share = 0
 
     trades_df_list = []
     trades_summary_df_list = []
@@ -47,6 +56,7 @@ def main():
                                 symbols = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
                                     "symbols", ["QQQ"]
                                 )
+                                commission_per_share = 0.0005
                             else:
                                 if is_binance:
                                     symbols = BINANCE_MONITOR_CONFIG.get("volume_monitor", {}).get(
@@ -56,6 +66,7 @@ def main():
                                     symbols = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
                                         "symbols", ["BTC-USDT"]
                                     )
+                                commission_per_share = 0
                             for symbol in symbols:
                                 logger.info(
                                     f"开始回测 {symbol}, 交易周期:{bar}, 开始日期:{start_date}, 结束日期:{end_date}, 是否是美股:{is_us_stock}, 交易方向:{direction}, 是否使用SAR:{by_sar}, 是否使用R为entry减stop:{price_range_mean_as_R}, 是否使用K线实体过50%:{by_big_k}"

trade_ma_strategy_main.py

@@ -25,8 +25,8 @@ from config import (
 logger = logging.logger
 
 class TradeMaStrategyMain:
-    def __init__(self, is_us_stock: bool = False):
+    def __init__(self, is_us_stock: bool = False, is_binance: bool = False):
-        self.ma_break_statistics = MaBreakStatistics(is_us_stock=is_us_stock)
+        self.ma_break_statistics = MaBreakStatistics(is_us_stock=is_us_stock, is_binance=is_binance)
     
     def batch_ma_break_statistics(self):
         """
@@ -36,6 +36,8 @@ class TradeMaStrategyMain:
         strategy_dict = self.ma_break_statistics.main_strategy
         pct_chg_df_list = []
         for strategy_name, strategy_info in strategy_dict.items():
+            if "macd" in strategy_name:
+                # 只计算macd策略
                 pct_chg_df = self.ma_break_statistics.batch_statistics(strategy_name=strategy_name)
                 pct_chg_df_list.append(pct_chg_df)
             
@@ -59,5 +61,5 @@ class TradeMaStrategyMain:
 
 
 if __name__ == "__main__":
-    trade_ma_strategy_main = TradeMaStrategyMain(is_us_stock=True)
+    trade_ma_strategy_main = TradeMaStrategyMain(is_us_stock=False, is_binance=True)
     trade_ma_strategy_main.batch_ma_break_statistics()