support get market data

2025-07-24 18:23:00 +08:00 · 2025-07-24 18:23:00 +08:00 · c670be6f7d
parent ebf90534ae
commit c670be6f7d
9 changed files with 238 additions and 17 deletions
--- a/config.py
+++ b/config.py
@ -4,16 +4,24 @@
 # API_KEY = "7286d434-225b-401f-b3af-fd595e15d23f"
 # SECRET_KEY = "80B95C5757F9208F70282A85C9DDBC86"
 # PASSPHRASE = "Bengbu_2001"
+# SANDBOX = False
+
+# 实盘读取API密钥配置
+API_KEY = "a73f9096-8e76-49ff-947c-a4f4edf657ec"
+SECRET_KEY = "F7AD69272FBF7C44E69CC110D2EDDB7A"
+PASSPHRASE = "Bengbu!2001"
+SANDBOX = False
+
 # 模拟盘API密钥配置
-API_KEY = "f309e789-3497-4ed3-896f-d18bdc4d9817"
-SECRET_KEY = "9152809391B110E2E647FDE12A37E96D"
-PASSPHRASE = "Bengbu@2001"
+# API_KEY = "f309e789-3497-4ed3-896f-d18bdc4d9817"
+# SECRET_KEY = "9152809391B110E2E647FDE12A37E96D"
+# PASSPHRASE = "Bengbu@2001"
+# SANDBOX = True

 # 交易配置
 TRADING_CONFIG = {
    "symbol": "BTC-USDT",           # 交易对
    "position_size": 0.001,         # 每次交易数量（BTC）
-    "sandbox": True,                # 是否使用沙盒环境（建议先用沙盒测试）
    
    # 策略参数
    "sma_short_period": 5,          # 短期移动平均线周期
@ -39,3 +47,19 @@ TIME_CONFIG = {
    "kline_interval": "5m",         # K线数据间隔
    "kline_limit": 100,             # K线数据条数
 }
+
+MONITOR_CONFIG = {
+    "volume_monitor":{
+        "symbols": ["XCH-USDT"],
+        "intervals": ["5m", "15m", "1H", "4H", "1D"],
+        "initial_date": "2025-07-01 00:00:00"
+    },
+    "price_monitor":{
+        "symbols": ["XCH-USDT"],
+        "intervals": [
+            {"interval": "5m", "threshold": 0.025},
+            {"interval": "15m", "threshold": 0.5}, 
+            {"interval": "1H", "threshold": 0.1}
+            ]
+    }
+}
--- a/core/base.py
+++ b/core/base.py
@ -101,14 +101,31 @@ class QuantTrader:
            logging.error(f"获取价格异常: {e}")
            return None

-    def get_kline_data(self, bar: str = '1m', limit: int = 100) -> Optional[pd.DataFrame]:
+    def get_kline_data(self, symbol: str = None, after: str = None, before: str = None, bar: str = '1m', limit: int = 100) -> Optional[pd.DataFrame]:
        """获取K线数据"""
+        if symbol is None:
+            symbol = self.symbol
        try:
-            result = self.market_api.get_candlesticks(
-                instId=self.symbol, 
-                bar=bar, 
-                limit=str(limit)
-            )
+            if after is None and before is None:
+                result = self.market_api.get_candlesticks(
+                    instId=symbol, 
+                    bar=bar, 
+                    limit=str(limit)
+                )
+            else:
+                if after is None:
+                    after = ''
+                if before is None:
+                    before = ''
+                if limit == 0:
+                    limit = ''
+                result = self.market_api.get_candlesticks(
+                    instId=symbol,
+                    after=after, 
+                    before=before,
+                    bar=bar, 
+                    limit=str(limit), 
+                    )
            if result.get('code') == '0':
                data = result.get('data', [])
                if not data:
--- a/core/data_monitor.py
+++ b/core/data_monitor.py
@ -0,0 +1,91 @@
+import time
+from datetime import datetime, timedelta
+import logging
+from typing import Optional
+import pandas as pd
+import okx.MarketData as Market
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s')
+
+class DataMonitor:
+    def __init__(self, 
+        api_key: str, 
+        secret_key: str, 
+        passphrase: str, 
+        sandbox: bool = True):
+        flag = "1" if sandbox else "0" # 0:实盘环境 1:沙盒环境
+        self.market_api = Market.MarketAPI(
+            api_key=api_key, api_secret_key=secret_key, passphrase=passphrase, 
+            flag=flag
+        )
+
+    def get_historical_kline_data(self, symbol: str = None, start: str = None, bar: str = '1m', limit: int = 100, end_time: int = None) -> Optional[pd.DataFrame]:
+        """
+        获取历史K线数据，支持start为北京时间字符串（%Y-%m-%d %H:%M:%S）或UTC毫秒级时间戳
+        :param symbol: 交易对
+        :param start: 起始时间（北京时间字符串或UTC毫秒级时间戳）
+        :param bar: K线周期
+        :param limit: 每次请求数量
+        :param end_time: 结束时间（毫秒级timestamp），默认当前时间
+        :return: pd.DataFrame
+        """
+        from core.utils import datetime_to_timestamp
+        if symbol is None:
+            symbol = "XCH-USDT"
+        if end_time is None:
+            end_time = int(time.time() * 1000)  # 当前时间（毫秒）
+        # 处理start参数
+        if start is None:
+            # 默认两个月前
+            two_months_ago = datetime.now() - timedelta(days=60)
+            start_time = int(two_months_ago.timestamp() * 1000)
+        else:
+            try:
+                # 判断是否为纯数字（UTC毫秒级timestamp）
+                if start.isdigit():
+                    start_time = int(start)
+                else:
+                    # 按北京时间字符串处理，转换为毫秒级timestamp
+                    start_time = datetime_to_timestamp(start)
+            except Exception as e:
+                logging.error(f"start参数解析失败: {e}")
+                return None
+        columns = ["timestamp", "open", "high", "low", "close", "volume", "volCcy", "volCCyQuote", "confirm"]
+        all_data = []
+        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.market_api.get_history_candlesticks(
+                    instId=symbol,
+                    after=end_time,  # 获取指定时间之前的数据,
+                    bar=bar,
+                    limit=str(limit)
+                )
+                if response["code"] != "0" or not response["data"]:
+                    logging.warning(f"请求失败或无数据: {response.get('msg', 'No message')}")
+                    break
+                candles = response["data"]
+                all_data.extend(candles)
+                # 更新 end_time 为本次请求中最早的时间戳
+                end_time = int(candles[-1][0])
+                logging.info(f"已获取 {len(candles)} 条数据，最早时间: {pd.to_datetime(end_time, unit='ms', utc=True).tz_convert('Asia/Shanghai')}")
+                end_time -= 1  # 减 1 毫秒以避免重复
+                time.sleep(0.2)
+            except Exception as e:
+                logging.error(f"请求出错: {e}")
+                break
+        if all_data:
+            df = pd.DataFrame(all_data, columns=columns)
+            df = df[df['confirm'] == '1']
+            for col in ['open', 'high', 'low', 'close', 'volume']:
+                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')
+            df = df[['timestamp', 'date_time', 'open', 'high', 'low', 'close', 'volume']]
+            df.sort_values('timestamp', inplace=True)
+            df.reset_index(drop=True, inplace=True)
+            logging.info(f"总计获取 {len(df)} 条 K 线数据（仅confirm=1）")
+            return df
+        else:
+            logging.warning("未获取到数据")
+            return None
--- a/core/utils.py
+++ b/core/utils.py
@ -0,0 +1,20 @@
+from datetime import datetime, timezone, timedelta
+
+def datetime_to_timestamp(date_str: str) -> int:
+    """
+    将日期字符串（如 '2023-01-01 12:00:00'）直接转换为毫秒级时间戳
+    :param date_str: 形如 '2023-01-01 12:00:00' 的日期时间字符串
+    :return: 毫秒级时间戳
+    """
+    dt = datetime.strptime(date_str, '%Y-%m-%d %H:%M:%S')
+    return int(dt.timestamp() * 1000)
+
+def timestamp_to_datetime(timestamp: int) -> str:
+    """
+    将时间戳转换为日期字符串
+    请考虑日期字符串位于北京时区
+    :param timestamp: 以毫秒为单位的时间戳
+    :return: 形如 '2023-01-01 12:00:00' 的日期时间字符串
+    """
+    dt = datetime.fromtimestamp(timestamp / 1000, timezone(timedelta(hours=8)))
+    return dt.strftime('%Y-%m-%d %H:%M:%S')
--- a/monitor_main.py
+++ b/monitor_main.py
@ -0,0 +1,33 @@
+import logging
+from time import sleep
+from core.data_monitor import DataMonitor
+from config import API_KEY, SECRET_KEY, PASSPHRASE, SANDBOX, MONITOR_CONFIG
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s')
+
+
+class MonitorMain:
+    def __init__(self):
+        self.data_monitor = DataMonitor(
+            api_key=API_KEY,
+            secret_key=SECRET_KEY,
+            passphrase=PASSPHRASE,
+            sandbox=SANDBOX,
+            )
+        self.symbols = MONITOR_CONFIG.get("volume_monitor", {}).get("symbols", ["XCH-USDT"])
+        self.intervals = MONITOR_CONFIG.get("volume_monitor", {}).get("intervals", ["5m", "15m", "1H", "4H", "1D"])
+        self.initial_date = MONITOR_CONFIG.get("volume_monitor", {}).get("initial_date", "2025-07-01 00:00:00")
+    
+    def initial_data(self):
+        for symbol in self.symbols:
+            for interval in self.intervals:
+                data = self.data_monitor.get_historical_kline_data(symbol=symbol, 
+                start=self.initial_date, 
+                bar=interval)
+    
+
+if __name__ == "__main__":
+    monitor_main = MonitorMain()
+    monitor_main.initial_data()
+
+    
--- a/plan.md
+++ b/plan.md
@ -0,0 +1,29 @@
+# 量化工作计划
+## 准备阶段
+### API熟悉过程
+1. 掌握API具体细节
+- 虚拟货币价格查询
+- 账户余额查询
+- 现货交易买卖
+- 永续合约买卖
+- 设置杠杆
+- 获得交易数据
+### 实验阶段
+1. 基于模拟盘实验
+2. 设置支持永续合约权限
+3. 完成现货买卖测试
+4. 完成永续合约买平测试
+5. 设置业务流程
+- 开空单
+- 卖现货
+- 平空单
+- 统计收益率
+6. 进阶实验
+- 获取对应货币技术指标
+- 获取1分钟线KDJ, RSI，均线等技术形态
+- 如果处于超买状态，以超买形态高点为参照物
+- 之后连续三个周期，价格低于形态高点价格
+- 此时方便做空，利于进行业务流程操作
+6. 反复验证策略安全与完整性
+### 实战阶段
+
--- a/play.py
+++ b/play.py
@ -9,7 +9,7 @@ def main() -> None:
    logging.info("=== 比特币量化交易系统 ===")
    # 导入配置
    try:
-        from config import API_KEY, SECRET_KEY, PASSPHRASE, TRADING_CONFIG, TIME_CONFIG
+        from config import API_KEY, SECRET_KEY, PASSPHRASE, SANDBOX, TRADING_CONFIG, TIME_CONFIG
    except ImportError:
        logging.error("找不到config.py文件，请确保配置文件存在")
        return
@ -18,18 +18,18 @@ def main() -> None:
        logging.error("请先在config.py中配置你的OKX API密钥！\n1. 登录OKX官网\n2. 进入API管理页面\n3. 创建API Key、Secret Key和Passphrase\n4. 将密钥填入config.py文件中的相应位置")
        return
    # 创建交易器实例
-    sandbox = TRADING_CONFIG.get("sandbox", True)
+    # sandbox = TRADING_CONFIG.get("sandbox", True)
    symbol = TRADING_CONFIG.get("symbol", "BTC-USDT")
    position_size = TRADING_CONFIG.get("position_size", 0.001)
    trader = QuantTrader(
        API_KEY, SECRET_KEY, PASSPHRASE, 
-        sandbox=sandbox,
+        sandbox=SANDBOX,
        symbol=symbol,
        position_size=position_size
    )
    strategy = QuantStrategy(
        API_KEY, SECRET_KEY, PASSPHRASE, 
-        sandbox=sandbox,
+        sandbox=SANDBOX,
        symbol=symbol,
        position_size=position_size
    )
--- a/trade_main.py
+++ b/trade_main.py
@ -2,7 +2,7 @@ import logging
 from time import sleep
 from core.base import QuantTrader
 from core.strategy import QuantStrategy
-from config import API_KEY, SECRET_KEY, PASSPHRASE, TRADING_CONFIG, TIME_CONFIG
+from config import API_KEY, SECRET_KEY, PASSPHRASE, SANDBOX, TRADING_CONFIG, TIME_CONFIG

 logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s')

@ -12,7 +12,8 @@ class BizMain:
        api_key = API_KEY
        secret_key = SECRET_KEY
        passphrase = PASSPHRASE
-        sandbox = TRADING_CONFIG.get("sandbox", True)
+        # sandbox = TRADING_CONFIG.get("sandbox", True)
+        sandbox = SANDBOX
        symbol = TRADING_CONFIG.get("symbol", "BTC-USDT")
        position_size = TRADING_CONFIG.get("position_size", 0.001)
        self.trader = QuantTrader(api_key, secret_key, passphrase, sandbox, symbol, position_size)
--- a/wechat.py
+++ b/wechat.py
@ -0,0 +1,6 @@
+"""
+微信群发功能
+建议使用企业微信
+但需要管理员提供企业id以及secret信息
+通过wechatpy库实现
+"""