support get US STOCK data

config.py

@@ -48,10 +48,9 @@ TIME_CONFIG = {
     "kline_limit": 100,             # K线数据条数
 }
 
-MONITOR_CONFIG = {
+OKX_MONITOR_CONFIG = {
     "volume_monitor":{
-        "symbols": ["XCH-USDT", "BONK-USDT", "PENGU-USDT", 
+        "symbols": ["XCH-USDT","SOL-USDT", 
-        "CFX-USDT", "PUMP-USDT", "SOL-USDT", 
         "BTC-USDT", "ETH-USDT", "DOGE-USDT"],
         "bars": ["5m", "15m", "30m", "1H"],
         "initial_date": "2025-05-15 00:00:00"
@@ -71,6 +70,14 @@ MONITOR_CONFIG = {
     }
 }
 
+US_STOCK_MONITOR_CONFIG = {
+    "volume_monitor":{
+        "symbols": ["QQQ", "TQQQ", "MSFT", "AAPL", "GOOG", "NVDA", "META", "AMZN", "TSLA", "AVGO"],
+        "bars": ["5m"],
+        "initial_date": "2015-08-31 00:00:00"
+    }
+}
+
 WINDOW_SIZE = {"window_sizes":[50, 80, 100, 120]}
 
 BAR_THRESHOLD = {
@@ -93,3 +100,5 @@ MYSQL_CONFIG = {
 WECHAT_CONFIG = {
     "key": "11e6f7ac-efa9-418a-904c-9325a9f5d324"
 }
+
+ITICK_API_KEY = "dfd4bc0caed148d6bc03b960224754ffb5356349e389431f828702b3a27e8a2b"

core/biz/market_data.py

@@ -4,7 +4,8 @@ from typing import Optional
 import pandas as pd
 import okx.MarketData as Market
 import okx.TradingData as TradingData
-from core.utils import transform_date_time_to_timestamp
+from core.utils import transform_date_time_to_timestamp, timestamp_to_datetime
+from core.biz.market_data_from_itick import MarketDataFromItick
 import core.logger as logging
 
 logger = logging.logger
@@ -14,7 +15,8 @@ class MarketData:
         api_key: str, 
         secret_key: str, 
         passphrase: str, 
-        sandbox: bool = True):
+        sandbox: bool = True,
+        is_us_stock: bool = False):
         self.flag = "1" if sandbox else "0" # 0:实盘环境 1:沙盒环境
         self.api_key = api_key
         self.secret_key = secret_key
@@ -23,6 +25,8 @@ class MarketData:
             api_key=api_key, api_secret_key=secret_key, passphrase=passphrase, 
             flag=self.flag
         )
+        self.is_us_stock = is_us_stock
+        
         # self.trade_api = TradingData.TradingDataAPI(
         #     api_key=api_key, api_secret_key=secret_key, passphrase=passphrase, 
         #     flag=flag
@@ -107,7 +111,11 @@ class MarketData:
         while start_time < end_time:
             try:
                 # after，真实逻辑是获得指定时间之前的数据 ！！！
-                response = self.get_historical_candlesticks_from_api(symbol, bar, end_time, limit)
+                if self.is_us_stock:
+                    market_data_from_itick = MarketDataFromItick(symbol=symbol, bar=bar, end_time=end_time, limit=limit)
+                    response = market_data_from_itick.get_historical_candlesticks_from_api()
+                else:
+                    response = self.get_historical_candlesticks_from_api(symbol, bar, end_time, limit)
                 if response is None:
                     logger.warning(f"请求失败，请稍后再试")
                     break
@@ -115,9 +123,15 @@ class MarketData:
                     logger.warning(f"请求失败或无数据: {response.get('msg', 'No message')}")
                     break
                 candles = response["data"]
-
+                if len(candles) == 0:
-                from_time = int(candles[-1][0])
+                    logger.warning(f"请求失败或无数据: {response.get('msg', 'No message')}")
-                to_time = int(candles[0][0])
+                    break
+                if self.is_us_stock:
+                    from_time = int(candles[-1]["timestamp"])
+                    to_time = int(candles[0]["timestamp"])
+                else:
+                    from_time = int(candles[-1][0])
+                    to_time = int(candles[0][0])
                 if latest_timestamp == -1:
                     latest_timestamp = from_time
                 else:
@@ -126,14 +140,25 @@ class MarketData:
                     else:
                         logger.warning(f"上一次数据最早时间戳 {latest_timestamp} 小于等于 from_time {from_time}， 停止获取数据")
                         break
-                from_time_str = pd.to_datetime(from_time, unit='ms', utc=True).tz_convert('Asia/Shanghai')
+                if self.is_us_stock:
-                to_time_str = pd.to_datetime(to_time, unit='ms', utc=True).tz_convert('Asia/Shanghai')
+                    from_time_str = pd.to_datetime(from_time, unit='ms', utc=True).tz_convert('America/New_York')
+                    to_time_str = pd.to_datetime(to_time, unit='ms', utc=True).tz_convert('America/New_York')
+                else:
+                    from_time_str = pd.to_datetime(from_time, unit='ms', utc=True).tz_convert('Asia/Shanghai')
+                    to_time_str = pd.to_datetime(to_time, unit='ms', utc=True).tz_convert('Asia/Shanghai')
                 logger.info(f"已获取{symbol}, 周期：{bar} {len(candles)} 条数据，从: {from_time_str} 到: {to_time_str}")
                 if from_time < start_time:
-                    start_time_str = pd.to_datetime(start_time, unit='ms', utc=True).tz_convert('Asia/Shanghai')
+                    if self.is_us_stock:
+                        start_time_str = pd.to_datetime(start_time, unit='ms', utc=True).tz_convert('America/New_York')
+                    else:
+                        start_time_str = pd.to_datetime(start_time, unit='ms', utc=True).tz_convert('Asia/Shanghai')
                     logger.warning(f"本轮获取{symbol}, {bar} 数据最早时间 {from_time_str} 早于 此次数据获取起始时间 {start_time_str}， 停止获取数据")
                     # candels中仅保留start_time之后的数据
-                    candles = [candle for candle in candles if int(candle[0]) >= start_time]
+                    if self.is_us_stock:
+                        if to_time > start_time:
+                            candles = [candle for candle in candles if int(candle["timestamp"]) >= start_time]
+                    else:
+                        candles = [candle for candle in candles if int(candle[0]) >= start_time]
                     if len(candles) > 0:
                         candles_pd = pd.DataFrame(candles, columns=columns)
                         all_data.append(candles_pd)
@@ -141,6 +166,9 @@ class MarketData:
                     else:
                         break
                 else:
+                    if end_time + 1 == from_time or end_time == from_time:
+                        logger.warning(f"本轮获取{symbol}, {bar} 数据最早时间 {from_time_str} 等于 此次数据获取结束时间， 停止获取数据")
+                        break
                     if len(candles) > 0:
                         candles_pd = pd.DataFrame(candles, columns=columns)
                         all_data.append(candles_pd)
@@ -161,6 +189,8 @@ class MarketData:
                 df[col] = pd.to_numeric(df[col], errors='coerce')
             dt_series = pd.to_datetime(df['timestamp'].astype(int), unit='ms', utc=True, errors='coerce').dt.tz_convert('Asia/Shanghai')
             df['date_time'] = dt_series.dt.strftime('%Y-%m-%d %H:%M:%S')
+            dt_us_series = pd.to_datetime(df['timestamp'].astype(int), unit='ms', utc=True, errors='coerce').dt.tz_convert('America/New_York')
+            df['date_time_us'] = dt_us_series.dt.strftime('%Y-%m-%d %H:%M:%S')
             # 将timestamp转换为整型
             df['timestamp'] = df['timestamp'].astype(int)
             # 添加虚拟货币名称列，内容为symbol
@@ -169,14 +199,35 @@ class MarketData:
             df['bar'] = bar
             df['create_time'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
 
+            if self.is_us_stock:
+                # 如果是美股数据，则仅保留date_time_us字中，在开盘时间内的数据，即开盘时间为美国时间9:30到16:00
+                # 请将date_time_us转换为datetime对象，然后判断是否在开盘时间内的数据，如果是，则保留，否则删除
+                df['date_time_us'] = pd.to_datetime(df['date_time_us'], errors='coerce')
+                # 使用 .loc 避免 SettingWithCopyWarning
+                mask = (df['date_time_us'].dt.hour >= 9) & (df['date_time_us'].dt.hour <= 16)
+                df = df.loc[mask].copy()
+                # 对于9点，只保留9:35与之后的数据
+                mask_9 = ~((df['date_time_us'].dt.hour == 9) & (df['date_time_us'].dt.minute <= 30))
+                df = df.loc[mask_9].copy()
+                # 对于16点，只保留16:00之前的数据
+                mask_16 = ~((df['date_time_us'].dt.hour == 16) & (df['date_time_us'].dt.minute > 0))
+                df = df.loc[mask_16].copy()
+                # 将date_time_us转换为字符串
+                df.loc[:, 'date_time_us'] = df['date_time_us'].dt.strftime('%Y-%m-%d %H:%M:%S')
+
+
             # 获取df中date_time的最早时间与最新时间 并保存到df中
-            df = df[['symbol', 'bar', 'timestamp', 'date_time', 'open', 'high', 'low', 'close', 'volume', 'volCcy', 'volCCyQuote', 'create_time']]
+            df = df[['symbol', 'bar', 'timestamp', 'date_time', 'date_time_us', 'open', 'high', 'low', 'close', 'volume', 'volCcy', 'volCCyQuote', 'create_time']]
             df.sort_values('timestamp', inplace=True)
             df.reset_index(drop=True, inplace=True)
             logger.info(f"总计获取 {len(df)} 条 K 线数据（仅confirm=1）")
             # 获取df中date_time的最早时间与最新时间
-            earliest_time = df['date_time'].min()
+            if self.is_us_stock:
-            latest_time = df['date_time'].max()
+                earliest_time = df['date_time_us'].min()
+                latest_time = df['date_time_us'].max()
+            else:
+                earliest_time = df['date_time'].min()
+                latest_time = df['date_time'].max()
             logger.info(f"本轮更新{symbol}, {bar} 数据最早时间: {earliest_time}， 最新时间: {latest_time}")
             return df
         else:

core/biz/market_data_from_itick.py

@@ -0,0 +1,138 @@
+import requests
+import pandas as pd
+from datetime import datetime, timedelta
+import time
+import json
+from typing import Optional
+import mplfinance as mpf
+import random
+
+from core.utils import transform_date_time_to_timestamp, datetime_to_timestamp, timestamp_to_datetime
+
+import core.logger as logging
+from config import ITICK_API_KEY
+
+logger = logging.logger
+
+class MarketDataFromItick:
+    """用于从 Yahoo Finance 下载 K 线数据的类，支持分段下载和数据处理。"""
+    
+    def __init__(self, symbol='QQQ', bar='5m', end_time: str=None, limit: int=1000):
+        """
+        初始化下载器。
+        
+        参数:
+            ticker (str): 股票或 ETF 代码，如 'QQQ'。
+            start_date (datetime): 开始日期，默认为 1 年前。
+            end_date (datetime): 结束日期，默认为当前日期。
+            bar (str): K 线间隔，如 '5m'（5 分钟）。
+            segment_days (int): 每次下载的天数，默认为 10 天。
+        """
+        self.symbol = symbol
+        self.time_stamp_unit = 0
+        if bar == '1m':
+            self.ktype = '1'
+            self.time_stamp_unit = 1000 * 60
+        elif bar == '5m':
+            self.ktype = '2'
+            self.time_stamp_unit = 1000 * 60 * 5
+        elif bar == '15m':
+            self.ktype = '3'
+            self.time_stamp_unit = 1000 * 60 * 15
+        elif bar == '30m':
+            self.ktype = '4'
+            self.time_stamp_unit = 1000 * 60 * 30
+        elif bar == '1H':
+            self.ktype = '5'
+            self.time_stamp_unit = 1000 * 60 * 60
+        elif bar == '2H':
+            self.ktype = '6'
+            self.time_stamp_unit = 1000 * 60 * 60 * 2
+        elif bar == '4H':
+            self.ktype = '7'
+            self.time_stamp_unit = 1000 * 60 * 60 * 4
+        elif bar == '1D':
+            self.ktype = '8'
+        elif bar == '1W':
+            self.ktype = '9'
+        elif bar == '1M':
+            self.ktype = '10'
+        else:
+            self.ktype = '2'
+        self.limit = limit
+        
+        # 设置默认时间范围
+        if end_time is None:
+            end_time = int(datetime.now().strftime('%Y-%m-%d %H:%M:%S').timestamp())
+        if isinstance(end_time, str):
+            end_time = int(datetime.strptime(end_time, '%Y-%m-%d %H:%M:%S').timestamp())
+        self.end_time = end_time
+        
+        self.headers = {
+            "accept": "application/json",
+            "token": ITICK_API_KEY
+            }
+        # 初始化空 DataFrame
+        self.data = pd.DataFrame()
+        
+        # 重试配置
+        self.max_retries = 3
+        self.base_delay = 5  # 基础延迟秒数
+        self.max_delay = 60  # 最大延迟秒数
+    
+    def get_historical_candlesticks_from_api(self):
+        response = None
+        count = 0
+        end_time_str = timestamp_to_datetime(self.end_time)
+        logger.info(f"请求数据: {self.symbol} {self.ktype} {end_time_str} {self.limit}")
+        while True:
+            try:
+                url = f"https://api.itick.org/stock/kline?region=US&code={self.symbol}&kType={self.ktype}&et={self.end_time}&limit={self.limit}"
+                response = requests.get(url, headers=self.headers)
+                if response:
+                    response = json.loads(response.text)
+                    if response["code"] != 0:
+                        raise Exception(f"请求出错: {response["msg"]}")
+                    data_list = response["data"]
+                    """
+                    {
+                        "tu": 1292317671.363,
+                        "c": 560.78,
+                        "t": 1752858600000,
+                        "v": 2305857,
+                        "h": 560.79,
+                        "l": 560.06,
+                        "o": 560.45
+                    }
+                    """
+                    result_list = []
+                    # itick的时间，分钟数据与市面上的分钟数据有1个周期的差距，即itick的时间是市面上的时间的前1个周期
+                    # 因此需要将itick的时间加上1个周期的单位
+                    for data in data_list:
+                        result_list.append({
+                            "timestamp": int(data["t"]) + self.time_stamp_unit,
+                            "open": float(data["o"]),
+                            "high": float(data["h"]),
+                            "low": float(data["l"]),
+                            "close": float(data["c"]),
+                            "volume": int(data["v"]),
+                            "volCcy": 0,
+                            "volCCyQuote": 0,
+                            "confirm": "1"
+                        })
+                    # result_list按照timestamp降序排列
+                    result_list = sorted(result_list, key=lambda x: x["timestamp"], reverse=True)
+                    response["data"] = result_list
+                    response["code"] = "0"
+                    response["msg"] = "success"
+                    break
+            except Exception as e:    
+                count += 1
+                logger.error(f"请求出错: {e}, 第{count}次重试，30秒后重试")
+                if count > 3:
+                    break
+                time.sleep(30)
+        logger.info(f"请求成功，等待12秒后返回")
+        time.sleep(12)
+        return response
+        

core/db/db_market_data.py

@@ -18,6 +18,7 @@ class DBMarketData:
         "bar",
         "timestamp",
         "date_time",
+        "date_time_us",
         "open",
         "high",
         "low",

core/statistics/price_volume_stats.py

@@ -11,7 +11,7 @@ from openpyxl.drawing.image import Image
 import openpyxl
 from openpyxl.styles import Font
 from PIL import Image as PILImage
-from config import MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
+from config import OKX_MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
 from core.db.db_market_data import DBMarketData
 from core.db.db_huge_volume_data import DBHugeVolumeData
 from core.utils import timestamp_to_datetime, transform_date_time_to_timestamp
@@ -35,10 +35,10 @@ class PriceVolumeStats:
         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.symbol_list = MONITOR_CONFIG.get("volume_monitor", {}).get("symbols", [])
+        self.symbol_list = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get("symbols", [])
-        self.bars_list = MONITOR_CONFIG.get("volume_monitor", {}).get("bars", [])
+        self.bars_list = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get("bars", [])
         self.windows_list = WINDOW_SIZE.get("window_sizes", [])
-        self.initial_date = MONITOR_CONFIG.get("volume_monitor", {}).get(
+        self.initial_date = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
             "initial_date", "2025-05-15 00:00:00"
         )
         self.end_date = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

core/trade/ma_break_statistics.py

@@ -12,7 +12,7 @@ from openpyxl.drawing.image import Image
 import openpyxl
 from openpyxl.styles import Font
 from PIL import Image as PILImage
-from config import MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
+from config import OKX_MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
 from core.db.db_market_data import DBMarketData
 from core.db.db_huge_volume_data import DBHugeVolumeData
 from core.utils import timestamp_to_datetime, transform_date_time_to_timestamp
@@ -44,10 +44,10 @@ class MaBreakStatistics:
         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.symbols = MONITOR_CONFIG.get("volume_monitor", {}).get(
+        self.symbols = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
             "symbols", ["XCH-USDT"]
         )
-        self.bars = MONITOR_CONFIG.get("volume_monitor", {}).get(
+        self.bars = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
             "bars", ["5m", "15m", "30m", "1H"]
         )
         self.stats_output_dir = "./output/trade_sandbox/ma_strategy/excel/"

core/trade/mean_reversion_sandbox.py

@@ -11,7 +11,7 @@ from openpyxl.drawing.image import Image
 import openpyxl
 from openpyxl.styles import Font
 from PIL import Image as PILImage
-from config import MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
+from config import OKX_MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
 import core.logger as logging
 from core.db.db_merge_market_huge_volume import DBMergeMarketHugeVolume
 from core.utils import timestamp_to_datetime, transform_date_time_to_timestamp

core/trade/orb_trade.py

@@ -0,0 +1,403 @@
+import yfinance as yf
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import core.logger as logging
+from config import OKX_MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
+from core.db.db_market_data import DBMarketData
+from core.db.db_huge_volume_data import DBHugeVolumeData
+from core.utils import timestamp_to_datetime, transform_date_time_to_timestamp
+
+# seaborn支持中文
+plt.rcParams["font.family"] = ["SimHei"]
+
+logger = logging.logger
+
+
+class ORBStrategy:
+    def __init__(
+        self,
+        initial_capital=25000,
+        max_leverage=4,
+        risk_per_trade=0.01,
+        commission_per_share=0.0005,
+    ):
+        """
+        初始化ORB策略参数
+        :param initial_capital: 初始账户资金（美元）
+        :param max_leverage: 最大杠杆倍数（默认4倍，符合FINRA规定）
+        :param risk_per_trade: 单次交易风险比例（默认1%）
+        :param commission_per_share: 每股交易佣金（美元，默认0.0005）
+        """
+        logger.info(f"初始化ORB策略参数：初始账户资金={initial_capital}，最大杠杆倍数={max_leverage}，单次交易风险比例={risk_per_trade}，每股交易佣金={commission_per_share}")
+        self.initial_capital = initial_capital
+        self.max_leverage = max_leverage
+        self.risk_per_trade = risk_per_trade
+        self.commission_per_share = commission_per_share
+        self.data = None  # 存储K线数据
+        self.trades = []  # 存储交易记录
+        self.equity_curve = None  # 存储账户净值曲线
+        mysql_user = MYSQL_CONFIG.get("user", "xch")
+        mysql_password = MYSQL_CONFIG.get("password", "")
+        if not mysql_password:
+            raise ValueError("MySQL password is not set")
+        mysql_host = MYSQL_CONFIG.get("host", "localhost")
+        mysql_port = MYSQL_CONFIG.get("port", 3306)
+        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)
+
+    def fetch_intraday_data(self, symbol, start_date, end_date, interval="5m"):
+        """
+        获取日内5分钟K线数据（需yfinance支持，部分数据可能有延迟）
+        :param ticker: 股票代码（如QQQ、TQQQ）
+        :param start_date: 起始日期（格式：YYYY-MM-DD）
+        :param end_date: 结束日期（格式：YYYY-MM-DD）
+        :param interval: K线周期（默认5分钟）
+        """
+        logger.info(f"开始获取{symbol}数据：{start_date}至{end_date}，间隔{interval}")
+        # data = yf.download(
+        #     symbol, start=start_date, end=end_date, interval=interval, progress=False
+        # )
+        data = self.db_market_data.query_market_data_by_symbol_bar(
+            symbol, interval, start=start_date, end=end_date
+        )
+        data = pd.DataFrame(data)
+        data.sort_values(by="date_time", inplace=True)
+        # 保留核心列：开盘价、最高价、最低价、收盘价、成交量
+        data["Open"] = data["open"]
+        data["High"] = data["high"]
+        data["Low"] = data["low"]
+        data["Close"] = data["close"]
+        data["Volume"] = data["volume"]
+        # 将data["date_time"]从字符串类型转换为日期
+        data["date_time"] = pd.to_datetime(data["date_time"])
+        # data["Date"]为日期，不包括时分秒，即date_time如果是2025-01-01 10:00:00，则Date为2025-01-01
+        data["Date"] = data["date_time"].dt.date
+        # 将Date转换为datetime64[ns]类型以确保类型一致
+        data["Date"] = pd.to_datetime(data["Date"])
+
+        self.data = data[["Date", "date_time", "Open", "High", "Low", "Close", "Volume"]].copy()
+        logger.info(f"成功获取{symbol}数据：{len(self.data)}根{interval}K线")
+
+    def calculate_shares(self, account_value, entry_price, stop_price):
+        """
+        根据ORB公式计算交易股数
+        :param account_value: 当前账户价值（美元）
+        :param entry_price: 交易entry价格（第二根5分钟K线开盘价）
+        :param stop_price: 止损价格（多头=第一根K线最低价，空头=第一根K线最高价）
+        :return: 整数股数（Shares）
+        """
+        logger.info(f"开始计算交易股数：账户价值={account_value}，entry价格={entry_price}，止损价格={stop_price}")
+        # 计算单交易风险金额（$R）
+        risk_per_trade_dollar = abs(entry_price - stop_price)  # 风险金额取绝对值
+        if risk_per_trade_dollar <= 0:
+            return 0  # 无风险时不交易
+
+        # 公式1：基于风险预算的最大股数（风险控制优先）
+        shares_risk = (account_value * self.risk_per_trade) / risk_per_trade_dollar
+        # 公式2：基于杠杆限制的最大股数（杠杆约束）
+        shares_leverage = (self.max_leverage * account_value) / entry_price
+        # 取两者最小值（满足风险和杠杆双重约束）
+        max_shares = min(shares_risk, shares_leverage)
+        # 扣除佣金影响（简化计算：假设佣金从可用资金中扣除）
+        commission_cost = max_shares * self.commission_per_share
+        if (account_value - commission_cost) < 0:
+            return 0  # 扣除佣金后资金不足，不交易
+
+        return int(max_shares)  # 股数取整
+
+    def generate_orb_signals(self):
+        """
+        生成ORB策略信号（每日仅1次交易机会）
+        - 第一根5分钟K线：确定开盘区间（High1, Low1）
+        - 第二根5分钟K线：根据第一根K线方向生成多空信号
+        """
+        logger.info("开始生成ORB策略信号")
+        if self.data is None:
+            raise ValueError("请先调用fetch_intraday_data获取数据")
+
+        signals = []
+        # 按日期分组处理每日数据
+        for date, daily_data in self.data.groupby("Date"):
+            daily_data = daily_data.sort_index()  # 按时间排序
+            if len(daily_data) < 2:
+                continue  # 当日K线不足2根，跳过
+
+            # 第一根5分钟K线（开盘区间）
+            first_candle = daily_data.iloc[0]
+            high1 = first_candle["High"]
+            low1 = first_candle["Low"]
+            open1 = first_candle["Open"]
+            close1 = first_candle["Close"]
+
+            # 第二根5分钟K线（entry信号）
+            second_candle = daily_data.iloc[1]
+            entry_price = second_candle["Open"]  # entry价格=第二根K线开盘价
+            entry_time = second_candle.date_time  # entry时间
+
+            # 生成信号：第一根K线方向决定多空（排除十字星：open1 == close1）
+            if open1 < close1:
+                # 第一根K线收涨→多头信号
+                signal = "Long"
+                stop_price = low1  # 多头止损=第一根K线最低价
+            elif open1 > close1:
+                # 第一根K线收跌→空头信号
+                signal = "Short"
+                stop_price = high1  # 空头止损=第一根K线最高价
+            else:
+                # 十字星→无信号
+                signal = "None"
+                stop_price = None
+
+            signals.append(
+                {
+                    "Date": date,
+                    "EntryTime": entry_time,
+                    "Signal": signal,
+                    "EntryPrice": entry_price,
+                    "StopPrice": stop_price,
+                    "High1": high1,
+                    "Low1": low1,
+                }
+            )
+
+        # 将信号合并到原始数据
+        signals_df = pd.DataFrame(signals)
+        # 确保Date列类型一致，将Date转换为datetime64[ns]类型
+        signals_df['Date'] = pd.to_datetime(signals_df['Date'])
+        # 使用merge而不是join来合并数据,根据signals_df的EntryTime与self.data的date_time进行匹配
+        # TODO: 这里需要优化
+        self.data = self.data.merge(signals_df, left_on="date_time", right_on="EntryTime", how="left")
+        # 将Date_x和Date_y合并为Date
+        self.data["Date"] = self.data["Date_x"].combine_first(self.data["Date_y"])
+        # 删除Date_x和Date_y
+        self.data.drop(columns=["Date_x", "Date_y"], inplace=True)
+        logger.info(
+            f"生成信号完成：共{len(signals_df)}个交易日，其中多头{sum(signals_df['Signal']=='Long')}次，空头{sum(signals_df['Signal']=='Short')}次"
+        )
+
+    def backtest(self, profit_target_multiple=10):
+        """
+        回测ORB策略
+        :param profit_target_multiple: 盈利目标倍数（默认10倍$R，即10R）
+        """
+        logger.info(f"开始回测ORB策略：盈利目标倍数={profit_target_multiple}")
+        if "Signal" not in self.data.columns:
+            raise ValueError("请先调用generate_orb_signals生成策略信号")
+
+        account_value = self.initial_capital  # 初始账户价值
+        current_position = None  # 当前持仓（None=空仓，Long/Short=持仓）
+        equity_history = [account_value]  # 净值历史
+        trade_id = 0  # 交易ID
+
+        # 按时间遍历数据（每日仅处理第二根K线后的信号）
+        for date, daily_data in self.data.groupby("Date"):
+            daily_data = daily_data.sort_index()
+            if len(daily_data) < 2:
+                continue
+
+            # 获取当日信号（第二根K线的信号）
+            signal_row = (
+                daily_data[~pd.isna(daily_data["Signal"])].iloc[0]
+                if sum(~pd.isna(daily_data["Signal"])) > 0
+                else None
+            )
+            if signal_row is None:
+                # 无信号→当日不交易，净值保持不变
+                equity_history.append(account_value)
+                continue
+
+            # 提取信号参数
+            signal = signal_row["Signal"]
+            if pd.isna(signal):
+                continue
+            entry_price = signal_row["EntryPrice"]
+            stop_price = signal_row["StopPrice"]
+            high1 = signal_row["High1"]
+            low1 = signal_row["Low1"]
+            risk_assumed = abs(entry_price - stop_price)  # 计算$R
+            profit_target = (
+                entry_price + (risk_assumed * profit_target_multiple)
+                if signal == "Long"
+                else entry_price - (risk_assumed * profit_target_multiple)
+            )
+
+            # 计算交易股数
+            shares = self.calculate_shares(account_value, entry_price, stop_price)
+            if shares == 0:
+                # 股数为0→不交易
+                equity_history.append(account_value)
+                continue
+
+            # 计算佣金（买入/卖出各收一次）
+            total_commission = shares * self.commission_per_share * 2  # 往返佣金
+
+            # 模拟日内持仓：寻找止损/止盈触发点，或当日收盘平仓
+            daily_prices = daily_data[
+                daily_data.date_time > signal_row.date_time
+            ]  # 从entry时间开始遍历
+            exit_price = None
+            exit_time = None
+            exit_reason = None
+
+            for idx, (time, row) in enumerate(daily_prices.iterrows()):
+                high = row["High"]
+                low = row["Low"]
+                close = row["Close"]
+
+                # 检查止损/止盈条件
+                if signal == "Long":
+                    # 多头：跌破止损→止损；突破止盈→止盈
+                    if low <= stop_price:
+                        exit_price = stop_price
+                        exit_reason = "Stop Loss"
+                        exit_time = time
+                        break
+                    elif high >= profit_target:
+                        exit_price = profit_target
+                        exit_reason = "Profit Target (10R)"
+                        exit_time = time
+                        break
+                elif signal == "Short":
+                    # 空头：突破止损→止损；跌破止盈→止盈
+                    if high >= stop_price:
+                        exit_price = stop_price
+                        exit_reason = "Stop Loss"
+                        exit_time = time
+                        break
+                    elif low <= profit_target:
+                        exit_price = profit_target
+                        exit_reason = "Profit Target (10R)"
+                        exit_time = time
+                        break
+
+            # 若未触发止损/止盈，当日收盘平仓
+            if exit_price is None:
+                exit_price = daily_prices.iloc[-1]["Close"]
+                exit_reason = "End of Day (EoD)"
+                exit_time = daily_prices.iloc[-1].date_time
+
+            # 计算盈亏
+            if signal == "Long":
+                profit_loss = (exit_price - entry_price) * shares - total_commission
+            else:  # Short
+                profit_loss = (entry_price - exit_price) * shares - total_commission
+
+            # 更新账户价值
+            account_value += profit_loss
+            account_value = max(account_value, 0)  # 账户价值不能为负
+
+            # 记录交易
+            self.trades.append(
+                {
+                    "TradeID": trade_id,
+                    "Date": date,
+                    "Signal": signal,
+                    "EntryTime": signal_row.date_time,
+                    "EntryPrice": entry_price,
+                    "ExitTime": exit_time,
+                    "ExitPrice": exit_price,
+                    "Shares": shares,
+                    "RiskAssumed": risk_assumed,
+                    "ProfitLoss": profit_loss,
+                    "ExitReason": exit_reason,
+                    "AccountValueAfter": account_value,
+                }
+            )
+
+            # 记录净值
+            equity_history.append(account_value)
+            trade_id += 1
+
+        # 生成净值曲线
+        self.equity_curve = pd.Series(
+            equity_history,
+            index=pd.date_range(
+                start=self.data.index[0].date(), periods=len(equity_history), freq="D"
+            ),
+        )
+
+        # 输出回测结果
+        trades_df = pd.DataFrame(self.trades)
+        total_return = (
+            (account_value - self.initial_capital) / self.initial_capital * 100
+        )
+        win_rate = (
+            (trades_df["ProfitLoss"] > 0).sum() / len(trades_df) * 100
+            if len(trades_df) > 0
+            else 0
+        )
+
+        logger.info("\n" + "=" * 50)
+        logger.info("ORB策略回测结果")
+        logger.info("=" * 50)
+        logger.info(f"初始资金：${self.initial_capital:,.2f}")
+        logger.info(f"最终资金：${account_value:,.2f}")
+        logger.info(f"总收益率：{total_return:.2f}%")
+        logger.info(f"总交易次数：{len(trades_df)}")
+        logger.info(f"胜率：{win_rate:.2f}%")
+        if len(trades_df) > 0:
+            logger.info(f"平均每笔盈亏：${trades_df['ProfitLoss'].mean():.2f}")
+            logger.info(f"最大单笔盈利：${trades_df['ProfitLoss'].max():.2f}")
+            logger.info(f"最大单笔亏损：${trades_df['ProfitLoss'].min():.2f}")
+
+    def plot_equity_curve(self):
+        """
+        绘制账户净值曲线
+        """
+        logger.info("开始绘制账户净值曲线")
+        if self.equity_curve is None:
+            raise ValueError("请先调用backtest进行回测")
+        
+        # seaborn风格设置
+        sns.set_theme(style="whitegrid")
+        # plt.rcParams['font.family'] = "SimHei"
+        plt.rcParams["font.sans-serif"] = ["SimHei"]  # 也可直接用字体名
+        plt.rcParams["font.size"] = 11  # 设置字体大小
+        plt.rcParams["axes.unicode_minus"] = False  # 解决负号显示问题
+
+        plt.figure(figsize=(12, 6))
+        plt.plot(
+            self.equity_curve.index,
+            self.equity_curve.values,
+            label="ORB策略净值",
+            color="blue",
+        )
+        plt.axhline(
+            y=self.initial_capital, color="red", linestyle="--", label="初始资金"
+        )
+        plt.title("ORB策略账户净值曲线", fontsize=14)
+        plt.xlabel("日期", fontsize=12)
+        plt.ylabel("账户价值（美元）", fontsize=12)
+        plt.legend()
+        plt.grid(True, alpha=0.3)
+        plt.show()
+
+
+# ------------------- 策略示例：回测QQQ的ORB策略（2016-2023） -------------------
+if __name__ == "__main__":
+
+    # 初始化ORB策略
+    orb_strategy = ORBStrategy(
+        initial_capital=25000,
+        max_leverage=4,
+        risk_per_trade=0.01,
+        commission_per_share=0.0005,
+    )
+
+    # 1. 获取QQQ的5分钟日内数据（2024-2025，注意：yfinance免费版可能限制历史日内数据，建议用专业数据源）
+    orb_strategy.fetch_intraday_data(
+        symbol="ETH-USDT", start_date="2025-05-15", end_date="2025-08-20", interval="5m"
+    )
+
+    # 2. 生成ORB策略信号
+    orb_strategy.generate_orb_signals()
+
+    # 3. 回测策略（盈利目标10R）
+    orb_strategy.backtest(profit_target_multiple=10)
+
+    # 4. 绘制净值曲线
+    orb_strategy.plot_equity_curve()

huge_volume_main.py

@@ -6,7 +6,7 @@ from core.utils import timestamp_to_datetime, transform_date_time_to_timestamp
 from market_data_main import MarketDataMain
 from core.wechat import Wechat
 import core.logger as logging
-from config import MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
+from config import OKX_MONITOR_CONFIG, US_STOCK_MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
 from datetime import datetime, timedelta
 import pandas as pd
 import os
@@ -16,7 +16,7 @@ logger = logging.logger
 
 
 class HugeVolumeMain:
-    def __init__(self, threshold: float = 2.0):
+    def __init__(self, threshold: float = 2.0, is_us_stock: bool = False):
         mysql_user = MYSQL_CONFIG.get("user", "xch")
         mysql_password = MYSQL_CONFIG.get("password", "")
         if not mysql_password:
@@ -29,7 +29,7 @@ class HugeVolumeMain:
         self.huge_volume = HugeVolume()
         self.db_market_data = DBMarketData(self.db_url)
         self.db_huge_volume_data = DBHugeVolumeData(self.db_url)
-        self.market_data_main = MarketDataMain()
+        self.market_data_main = MarketDataMain(is_us_stock=is_us_stock)
         self.threshold = threshold
 
         self.output_folder = "./output/huge_volume_statistics/"
@@ -41,9 +41,14 @@ class HugeVolumeMain:
         for symbol in self.market_data_main.symbols:
             for bar in self.market_data_main.bars:
                 if start is None:
-                    start = MONITOR_CONFIG.get("volume_monitor", {}).get(
+                    if self.is_us_stock:
-                        "initial_date", "2025-05-15 00:00:00"
+                        start = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
-                    )
+                            "initial_date", "2015-08-30 00:00:00"
+                        )
+                    else:
+                        start = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
+                            "initial_date", "2025-05-15 00:00:00"
+                        )
                 data = self.detect_volume_spike(
                     symbol,
                     bar,
@@ -68,9 +73,15 @@ class HugeVolumeMain:
         is_update: bool = False,
     ):
         if start is None:
-            start = MONITOR_CONFIG.get("volume_monitor", {}).get(
+            if self.is_us_stock:
-                "initial_date", "2025-05-01 00:00:00"
+                start = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
-            )
+                    "initial_date", "2015-08-31 00:00:00"
+                )
+            else:
+                start = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
+                    "initial_date", "2025-05-01 00:00:00"
+                )
+
         if end is None:
             end = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
         logger.info(
@@ -169,7 +180,7 @@ class HugeVolumeMain:
                     logger.info(f"此次更新巨量交易数据为空")
         except Exception as e:
             logger.error(
-                f"更新巨量交易数据失败: {symbol} {bar} 窗口大小: {window_size} 从 {earliest_date_time} 到 {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}: {e}"
+                f"更新巨量交易数据失败: {symbol} {bar} 窗口大小: {window_size} 到 {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}: {e}"
             )
 
     def get_seconds_by_bar(self, bar: str):
@@ -226,7 +237,7 @@ class HugeVolumeMain:
         periods: list = [3, 5],
     ):
         if start is None:
-            start = MONITOR_CONFIG.get("volume_monitor", {}).get(
+            start = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
                 "initial_date", "2025-05-01 00:00:00"
             )
         if end is None:
@@ -286,7 +297,7 @@ class HugeVolumeMain:
 
     def send_huge_volume_data_to_wechat(self, start: str = None, end: str = None):
         if start is None:
-            start = MONITOR_CONFIG.get("volume_monitor", {}).get(
+            start = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
                 "initial_date", "2025-05-01 00:00:00"
             )
         if end is None:
@@ -395,7 +406,7 @@ class HugeVolumeMain:
         output_excel: bool = False,
     ):
         if start is None:
-            start = MONITOR_CONFIG.get("volume_monitor", {}).get(
+            start = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
                 "initial_date", "2025-05-01 00:00:00"
             )
         if end is None:
@@ -464,7 +475,7 @@ def batch_initial_detect_volume_spike(threshold: float = 2.0):
     ):
         window_sizes = [50, 80, 100, 120]
     huge_volume_main = HugeVolumeMain(threshold)
-    start_date = MONITOR_CONFIG.get("volume_monitor", {}).get(
+    start_date = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
         "initial_date", "2025-05-15 00:00:00"
     )
     for window_size in window_sizes:
@@ -474,7 +485,7 @@ def batch_initial_detect_volume_spike(threshold: float = 2.0):
         )
 
 
-def batch_update_volume_spike(threshold: float = 2.0):
+def batch_update_volume_spike(threshold: float = 2.0, is_us_stock: bool = False):
     window_sizes = WINDOW_SIZE.get("window_sizes", None)
     if (
         window_sizes is None
@@ -482,7 +493,7 @@ def batch_update_volume_spike(threshold: float = 2.0):
         or len(window_sizes) == 0
     ):
         window_sizes = [50, 80, 100, 120]
-    huge_volume_main = HugeVolumeMain(threshold)
+    huge_volume_main = HugeVolumeMain(threshold, is_us_stock)
     for window_size in window_sizes:
         huge_volume_main.batch_update_volume_spike(window_size=window_size)
 
@@ -501,7 +512,7 @@ def test_send_huge_volume_data_to_wechat():
 if __name__ == "__main__":
     # test_send_huge_volume_data_to_wechat()
     # batch_initial_detect_volume_spike(threshold=2.0)
-    batch_update_volume_spike(threshold=2.0)
+    batch_update_volume_spike(threshold=2.0, is_us_stock=True)
     # huge_volume_main = HugeVolumeMain(threshold=2.0)
     # huge_volume_main.batch_next_periods_rise_or_fall(output_excel=True)
     # data_file_path = "./output/huge_volume_statistics/next_periods_rise_or_fall_stat_20250731200304.xlsx"

market_data_from_itick_main.py

@@ -0,0 +1,60 @@
+import requests
+import core.logger as logging
+
+from datetime import datetime, timedelta
+
+from futu import KLType
+
+logger = logging.logger
+
+
+def main():
+    # 配置参数
+    symbol = "QQQ"
+    start_date = "2025-08-01 00:00:00"
+    end_date = "2025-08-20 00:00:00"
+    interval = "5m"
+    segment_days = 5  # 减少每段天数，降低单次请求的数据量
+    
+    print(f"开始下载 {symbol} 数据")
+    print(f"时间范围: {start_date} 到 {end_date}")
+    print(f"数据间隔: {interval}")
+    print(f"分段天数: {segment_days}")
+    print("-" * 50)
+    
+    try:
+        market_data_from_futu = MarketDataFromAlphaVantage(
+            symbol=symbol,
+            start_date=start_date,
+            end_date=end_date,
+            interval=interval,
+            segment_days=segment_days,    
+        )
+        
+        # 下载数据
+        market_data_from_futu.download_all()
+        
+        # 处理数据
+        processed_data = market_data_from_futu.process_data()
+        
+        if not processed_data.empty:
+            logger.info(f"成功下载 {len(processed_data)} 条数据")
+            
+            # 保存数据
+            filename = f"{symbol}_{interval}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
+            market_data_from_futu.save_to_csv(filename)
+            
+            # 显示数据统计
+            market_data_from_futu.show_head()
+            market_data_from_futu.show_stats()
+            
+        else:
+            logger.warning("未获取到任何数据")
+            
+    except Exception as e:
+        logger.error(f"下载过程中发生错误: {e}")
+        print(f"错误详情: {e}")
+
+
+if __name__ == "__main__":
+    main()

market_data_main.py

@@ -16,7 +16,8 @@ from config import (
     SECRET_KEY,
     PASSPHRASE,
     SANDBOX,
-    MONITOR_CONFIG,
+    OKX_MONITOR_CONFIG,
+    US_STOCK_MONITOR_CONFIG,
     MYSQL_CONFIG,
     BAR_THRESHOLD,
 )
@@ -25,22 +26,34 @@ logger = logging.logger
 
 
 class MarketDataMain:
-    def __init__(self):
+    def __init__(self, is_us_stock: bool = False):
         self.market_data = MarketData(
             api_key=API_KEY,
             secret_key=SECRET_KEY,
             passphrase=PASSPHRASE,
             sandbox=SANDBOX,
+            is_us_stock=is_us_stock,
         )
-        self.symbols = MONITOR_CONFIG.get("volume_monitor", {}).get(
+        if is_us_stock:
-            "symbols", ["XCH-USDT"]
+            self.symbols = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
-        )
+                "symbols", ["QQQ"]
-        self.bars = MONITOR_CONFIG.get("volume_monitor", {}).get(
+            )
-            "bars", ["5m", "15m", "1H", "1D"]
+            self.bars = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
-        )
+                "bars", ["5m"]
-        self.initial_date = MONITOR_CONFIG.get("volume_monitor", {}).get(
+            )
-            "initial_date", "2025-07-01 00:00:00"
+            self.initial_date = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
-        )
+                "initial_date", "2015-08-30 00:00:00"
+            )
+        else:
+            self.symbols = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
+                "symbols", ["XCH-USDT"]
+            )
+            self.bars = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
+                "bars", ["5m", "15m", "1H", "1D"]
+            )
+            self.initial_date = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
+                "initial_date", "2025-07-01 00:00:00"
+            )
         mysql_user = MYSQL_CONFIG.get("user", "xch")
         mysql_password = MYSQL_CONFIG.get("password", "")
         if not mysql_password:
@@ -52,6 +65,7 @@ class MarketDataMain:
         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.trade_data_main = TradeDataMain()
+        self.is_us_stock = is_us_stock
 
     def initial_data(self):
         """
@@ -96,7 +110,15 @@ class MarketDataMain:
         # 获取数据，直到end_time_ts
         threshold = None
         if bar in ["5m", "15m", "30m"]:
-            threshold = 86400000
+            if self.is_us_stock:
+                if bar == "5m":
+                    threshold = 86400000 * 4
+                elif bar == "15m":
+                    threshold = 86400000 * 4 * 3
+                elif bar == "30m":
+                    threshold = 86400000 * 4 * 6
+            else:
+                threshold = 86400000
         elif bar in ["1H", "4H"]:
             threshold = 432000000
         elif bar == "1D":
@@ -105,7 +127,7 @@ class MarketDataMain:
         get_data = False
         min_start_time_ts = start_time_ts
         while start_time_ts < end_time_ts:
-            current_start_time_ts = end_time_ts - threshold
+            current_start_time_ts = int(end_time_ts - threshold)
             if current_start_time_ts < start_time_ts:
                 current_start_time_ts = start_time_ts
             start_date_time = timestamp_to_datetime(current_start_time_ts)
@@ -113,46 +135,21 @@ class MarketDataMain:
             logger.info(
                 f"获取行情数据: {symbol} {bar} 从 {start_date_time} 到 {end_date_time}"
             )
+            if self.is_us_stock:
+                limit = 1000
+            else:
+                limit = 100
             data = self.market_data.get_historical_kline_data(
                 symbol=symbol,
                 start=current_start_time_ts,
                 bar=bar,
                 end_time=end_time_ts,
+                limit=limit,
             )
             if data is not None and len(data) > 0:
                 data["buy_sz"] = -1
                 data["sell_sz"] = -1
 
-            # 根据交易数据，设置buy_sz和sell_sz
-            # 比特币的数据获取过慢，暂时不获取交易数据
-            # if not symbol.endswith("-SWAP"):
-            #     # trade_data的end_time需要比market_data的end_time大一个周期
-            #     trade_data = self.trade_data_main.get_trade_data(
-            #         symbol=symbol, start_time=current_start_time_ts, end_time=end_time_ts
-            #     )
-            #     for index, row in data.iterrows():
-            #         try:
-            #             current_from_time = int(row["timestamp"])
-            #             if index == len(data) - 1:
-            #                 current_to_time = current_from_time + BAR_THRESHOLD[bar]
-            #             else:
-            #                 current_to_time = int(data.iloc[index + 1]["timestamp"])
-            #             current_trade_data = trade_data[
-            #                 (trade_data["ts"] >= current_from_time)
-            #                 & (trade_data["ts"] <= current_to_time)
-            #             ]
-            #             if current_trade_data is not None and len(current_trade_data) > 0:
-            #                 current_buy_sz = current_trade_data[
-            #                     current_trade_data["side"] == "buy"
-            #                 ]["sz"].sum()
-            #                 current_sell_sz = current_trade_data[
-            #                     current_trade_data["side"] == "sell"
-            #                 ]["sz"].sum()
-            #                 data.loc[index, "buy_sz"] = current_buy_sz
-            #                 data.loc[index, "sell_sz"] = current_sell_sz
-            #         except Exception as e:
-            #             logger.error(f"设置buy_sz和sell_sz失败: {e}")
-            #             continue
             if data is not None and len(data) > 0:
                 data = data[
                     [
@@ -160,6 +157,7 @@ class MarketDataMain:
                         "bar",
                         "timestamp",
                         "date_time",
+                        "date_time_us",
                         "open",
                         "high",
                         "low",
@@ -174,13 +172,22 @@ class MarketDataMain:
                 ]
                 data = self.add_new_columns(data)
                 self.db_market_data.insert_data_to_mysql(data)
-                current_min_start_time_ts = data["timestamp"].min()
+                current_min_start_time_ts = int(data["timestamp"].min())
                 if current_min_start_time_ts < min_start_time_ts:
                     min_start_time_ts = current_min_start_time_ts
+
                 get_data = True
+            else:
+                logger.warning(f"获取行情数据为空: {symbol} {bar} 从 {start_date_time} 到 {end_date_time}")
+                break
+
             if current_start_time_ts == start_time_ts:
                 break
-            end_time_ts = current_start_time_ts
+            
+            if current_min_start_time_ts < current_start_time_ts:
+                end_time_ts = current_min_start_time_ts
+            else:
+                end_time_ts = current_start_time_ts
         if min_start_time_ts is not None and get_data:
             # 补充技术指标数据
             # 获得min_start_time_ts之前30条数据
@@ -374,7 +381,7 @@ class MarketDataMain:
         批量计算技术指标
         """
         logger.info("开始批量计算技术指标")
-        start_date_time = MONITOR_CONFIG.get("volume_monitor", {}).get(
+        start_date_time = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
             "initial_date", "2025-05-15 00:00:00"
         )
         start_timestamp = transform_date_time_to_timestamp(start_date_time)

market_monitor_main.py

@@ -4,7 +4,7 @@ from huge_volume_main import HugeVolumeMain
 from core.biz.market_monitor import create_metrics_report
 from core.db.db_market_monitor import DBMarketMonitor
 from core.wechat import Wechat
-from config import MONITOR_CONFIG, MYSQL_CONFIG
+from config import OKX_MONITOR_CONFIG, MYSQL_CONFIG
 from core.utils import timestamp_to_datetime, transform_date_time_to_timestamp
 import core.logger as logging
 
@@ -21,9 +21,9 @@ class MarketMonitorMain:
         self.market_data_main = MarketDataMain()
         self.huge_volume_main = HugeVolumeMain()
         self.wechat = Wechat()
-        self.monitor_config = MONITOR_CONFIG
+        self.monitor_config = OKX_MONITOR_CONFIG
         self.window_size = 100
-        self.start_date = MONITOR_CONFIG.get("volume_monitor", {}).get(
+        self.start_date = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
             "initial_date", "2025-05-01 00:00:00"
         )
         self.latest_record_file_path = "./output/record/latest_record.json"

orb_trade_main.py

@@ -0,0 +1,28 @@
+from core.trade.orb_trade import ORBStrategy
+
+def main():
+    # 初始化ORB策略
+    orb_strategy = ORBStrategy(
+        initial_capital=25000,
+        max_leverage=4,
+        risk_per_trade=0.01,
+        commission_per_share=0.0005,
+    )
+
+    # 1. 获取QQQ的5分钟日内数据（2024-2025，注意：yfinance免费版可能限制历史日内数据，建议用专业数据源）
+    orb_strategy.fetch_intraday_data(
+        symbol="ETH-USDT", start_date="2025-05-15", end_date="2025-08-20", interval="5m"
+    )
+
+    # 2. 生成ORB策略信号
+    orb_strategy.generate_orb_signals()
+
+    # 3. 回测策略（盈利目标10R）
+    orb_strategy.backtest(profit_target_multiple=10)
+
+    # 4. 绘制净值曲线
+    orb_strategy.plot_equity_curve()
+
+
+if __name__ == "__main__":
+    main()

sql/query/sql_playground.sql

@@ -1,12 +1,19 @@
-select * from crypto_market_monitor
+select * from crypto_market_data
-order by date_time desc;
+where symbol = "TQQQ" and bar="5m"
+order by date_time_us DESC;
+
+select * from crypto_huge_volume
+where symbol = "QQQ" and bar="5m"
+order by date_time;
+
+
 
 select symbol, bar, date_time, close, 
 pct_chg, ma_cross, ma5, ma10, ma20, ma30, dif, dea, macd, kdj_k, kdj_d, kdj_k, kdj_pattern,
 rsi_14, rsi_signal,
 boll_upper, boll_middle, boll_lower, boll_pattern, boll_signal
 from crypto_market_data
-WHERE close > boll_upper
+WHERE ma_cross in ("20下穿10", "10下穿5")
 order by timestamp desc;
 
 select symbol, bar, window_size, date_time, close, 
@@ -14,8 +21,8 @@ volume, volume_ratio, huge_volume,
 close_20_low, low_20_low, close_10_low, low_10_low,
 close_80_high, close_90_high, high_80_high, high_90_high 
 from crypto_huge_volume
-WHERE symbol='XCH-USDT' and bar='5m' and window_size=120# and low_10_low=1
+WHERE symbol='BTC-USDT' and bar='5m' and window_size=120# and low_10_low=1
-order by timestamp desc;
+order by timestamp;
 
 select * from crypto_huge_volume
 WHERE symbol='BTC-USDT' and bar='5m' #and date_time > '2025-08-04 15:00:00'

sql/table/crypto_market_data.sql

@@ -7,6 +7,7 @@ CREATE TABLE IF NOT EXISTS crypto_market_data (
     bar VARCHAR(20) NOT NULL,
     timestamp BIGINT NOT NULL,
     date_time VARCHAR(50) NOT NULL,
+    date_time_us VARCHAR(50) NULL COMMENT '美国时间格式的日期时间',
     open DECIMAL(20,10) NOT NULL,
     high DECIMAL(20,10) NOT NULL,
     low DECIMAL(20,10) NOT NULL,
@@ -60,3 +61,6 @@ CREATE TABLE IF NOT EXISTS crypto_market_data (
 
 --修改ma_cross字段长度为150
 ALTER TABLE crypto_market_data MODIFY COLUMN ma_cross VARCHAR(150) DEFAULT NULL COMMENT '均线交叉信号';
+
+--添加date_time_us字段
+ALTER TABLE crypto_market_data ADD COLUMN date_time_us VARCHAR(50) NULL COMMENT '美国时间格式的日期时间' AFTER date_time;

test_ma_methods.py

@@ -10,7 +10,7 @@ import matplotlib.pyplot as plt
 from core.db.db_market_data import DBMarketData
 from core.biz.metrics_calculation import MetricsCalculation
 import logging
-from config import MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
+from config import OKX_MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE
 # plt支持中文
 plt.rcParams['font.family'] = ['SimHei']
 

trade_data_main.py

@@ -9,7 +9,7 @@ from config import (
     SECRET_KEY,
     PASSPHRASE,
     SANDBOX,
-    MONITOR_CONFIG,
+    OKX_MONITOR_CONFIG,
     MYSQL_CONFIG,
 )
 
@@ -48,7 +48,7 @@ class TradeDataMain:
         # 处理start参数
         if start_time is None:
             # 默认两个月前
-            start_time_str = MONITOR_CONFIG.get("volume_monitor", {}).get("initial_date", "2025-05-01 00:00:00")
+            start_time_str = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get("initial_date", "2025-05-01 00:00:00")
             start_time = transform_date_time_to_timestamp(start_time_str)
         else:
             start_time = transform_date_time_to_timestamp(start_time)

trade_ma_strategy_main.py

@@ -17,7 +17,7 @@ from config import (
     SECRET_KEY,
     PASSPHRASE,
     SANDBOX,
-    MONITOR_CONFIG,
+    OKX_MONITOR_CONFIG,
     MYSQL_CONFIG,
     BAR_THRESHOLD,
 )

trade_sandbox_main.py

@@ -12,7 +12,7 @@ from openpyxl.drawing.image import Image
 import openpyxl
 from openpyxl.styles import Font
 from PIL import Image as PILImage
-from config import MONITOR_CONFIG
+from config import OKX_MONITOR_CONFIG
 from core.trade.mean_reversion_sandbox import MeanReversionSandbox
 from core.utils import timestamp_to_datetime, transform_date_time_to_timestamp
 
@@ -24,14 +24,14 @@ logger = logging.logger
 
 class MeanReversionSandboxMain:
     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(
+        self.symbols = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
             "symbols", ["XCH-USDT"]
         )
         self.only_5m = only_5m
         if only_5m:
             self.bars = ["5m"]
         else:
-            self.bars = MONITOR_CONFIG.get("volume_monitor", {}).get(
+            self.bars = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
                 "bars", ["5m", "15m", "30m", "1H"]
             )
         if solution_list is None: