446 lines
17 KiB
Markdown
446 lines
17 KiB
Markdown
#量化交易 #策略研究 #Python学习 #DeepSeek
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---
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这是我现在写的一个关于数字货币的一个交易脚本。请帮我完善：一是因为网络原因，如果中断执行，加入重新运行的异常处理模块。
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![[My_strategy.py]]
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```python
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import time
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import pandas as pd
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import ccxt
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from datetime import timedelta
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# import pandas_ta
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import talib
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# ====================================================================================================
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# =====格式设置
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# ====================================================================================================
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pd.set_option('display.max_rows', 1000)
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pd.set_option('expand_frame_repr', False) # 当列太多时不换行
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# 设置命令行输出时的列对齐功能
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pd.set_option('display.unicode.ambiguous_as_wide', True)
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pd.set_option('display.unicode.east_asian_width', True)
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# ====================================================================================================
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# =====创建ccxt交易所
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# ====================================================================================================
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BINANCE_CONFIG = {
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'apiKey': '',
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'secret': '',
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'proxies': {'http': '127.0.0.1:7890', 'https': '127.0.0.1:7890'}
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}
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exchange = ccxt.binance(BINANCE_CONFIG)
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while True:
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# list = ['DOGEUSDT', 'ETHUSDT', 'BTCUSDT']
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#
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# # for symbol in list:
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symbol = 'ETHUSDT'
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time_interval = '1m' # 其他可以尝试的值：'1m', '5m', '15m', '30m', '1h', '2h', '1d', '1w', '1M', '1y'，并不是每个交易所都支持
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bar_num = 1000 # 获取K线的数量
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params = {'symbol': symbol, # 交易币对
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'interval': time_interval, # 时间间隔
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'limit': bar_num} # 数据条数
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# ====================================================================================================
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# =====获取K线数据
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# ====================================================================================================
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response = exchange.fapiPublicGetKlines(params=params)
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k_lines = pd.DataFrame(response)
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# print(k_lines)
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# =====整理K线数据
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df = pd.DataFrame(response, dtype=float) # 将数据转换为dataframe
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df.rename(columns={0: 'MTS', 1: 'Open', 2: 'High',
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3: 'Low', 4: 'Close', 5: 'Volume'}, inplace=True) # 重命名
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df['candle_begin_time'] = pd.to_datetime(df['MTS'], unit='ms') # 整理时间
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df['candle_begin_time_GMT8'] = df['candle_begin_time'] + timedelta(hours=8) # 北京时间
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df = df[['candle_begin_time_GMT8', 'Open', 'High', 'Low', 'Close', 'Volume']] # 整理列的顺序
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# ====================================================================================================
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# =====获取币对的最新价格
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# ====================================================================================================
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data = exchange.fapiPublicGetTickerPrice(params={'symbol': "ETHUSDT"})
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price_ETH = data['price']
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# ====================================================================================================
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# =====通过pandas-ta计算指标并加入相关指标计算列
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# ====================================================================================================
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## 计算均线
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# df.ta.sma(length=5, append=True, col_names="SMA_5") # pandas-ta 计算SM5指标
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df['MA5'] = talib.MA(df['Close'], timeperiod=5) # ta-lib计算MA5指标
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# # 计算14日相对强弱指数（RSI）
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# df.ta.rsi(length=14, append=True, col_names="RSI_14") # pandas-ta计算RSI指标
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df['RSI_14'] = talib.RSI(df['Close'], timeperiod=14) # ta-lib计算RSI指标
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# # 计算MACD（12/26/9周期）
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# df.ta.macd(fast=12, slow=26, signal=9, append=True) # 默认列名：MACD_12_26_9, MACDs_12_26_9, MACDh_12_26_9 [2,7](@ref)
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# # 计算布林带（20日，2倍标准差）
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# df.ta.bbands(length=20, std=2, append=True)
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# pandas-ta计算布林带指标
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# 默认列名：BBL_20_2.0, BBM_20_2.0, BBU_20_2.0
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## ta-lib 计算布林带指标
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upper_band, middle_band, lower_band = talib.BBANDS(
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df['Close'],
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timeperiod=20,
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nbdevup=2,
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nbdevdn=2,
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matype=0 # SMA
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)
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df['BB_Upper'] = upper_band
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df['BB_Middle'] = middle_band
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df['BB_Lower'] = lower_band
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## 计算ADX指标
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# df.ta.adx(length=20, append=True) # pandas-ta计算adx
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df['ADX_14'] = talib.ADX(df['High'], df['Low'], df['Close'], timeperiod=14) # ta-lib计算ADX指标
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## 计算ATR指标
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# df.ta.atr(length=16, append=True) # pandas-ta计算atr，默认参数14，length也可以调整
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df['ATR_14'] = talib.ATR(df['High'], df['Low'], df['Close'], timeperiod=14)
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print(df)
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# df.to_csv('Ta4.csv')
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# ====================================================================================================
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# =====设置下单条件并执行
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# ====================================================================================================
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# if float(price_ETH) > float(df['Close'].iloc[-2]):
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# print('价格上升')
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# else:
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# print('价格下降')
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#
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# print(price_ETH)
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# print(df['Close'].iloc[-2])
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# print(df['RSI_14'].iloc[-2] > 30)
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# 以上为dataframe取数格式的测试，目前使用还不太熟练
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## 调整杠杆倍率
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# params = {'symbol': 'ETHUSDT', # 交易币对
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# 'leverage': 10,
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# 'timestamp': int(time.time() * 1000)}
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# leverage = exchange.fapiPrivatePostLeverage(params=params)
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# print('调整开仓杠杆\n', leverage, '\n')
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## 下单操作
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num = 0
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max_num = 3
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# 使用变量来控制下单次数
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params = {'side': 'BUY',
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'symbol': 'ETHUSDT', # 交易币对
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'type': 'LIMIT',
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# 'price': price_ETH,
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'price': df['Close'].iloc[-1],
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# 下单价格,在限价单的时候
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# 启用，将type换成LIMIT
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# 此处用变量取代了课程中的数值。以后下单的价格可能需要复杂计算再储存在变量中，做测试。
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'quantity': 0.01, # 下单数量
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'timestamp': int(time.time() * 1000),
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'timeInForce': 'GTC'} # https://academy.binance.com/zh/articles/understanding-the-different-order-types
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if df['RSI_14'].iloc[-2] > 30 and df['RSI_14'].iloc[-1] < 30:
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print('向下穿越rsi30，执行买入')
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response = exchange.fapiPrivatePostOrder(params=params)
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order_info = pd.DataFrame(response, index=['symbol'])
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order_id = order_info['orderId'].iloc[0]
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print('下单\n', order_info, '\n')
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num += 1
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# 此后这里还可以多个条件，组合多个指标的值
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# RSI的使用作为一个参考
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# 通过现有数据（开高收低量），已经可以计算出不少指标了。
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else:
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print('不符合交易条件，监控中')
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time.sleep(30)
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if num >= max_num:
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break
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## 这是我在办公室电脑上敲的注释
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## 测试能否通过iCloud同步成功，在家也可已经iCloud对同一个项目进行编辑。
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```
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---
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我将为您的数字货币交易脚本添加异常处理模块，确保在网络中断或API错误时能够自动重试。以下是完善后的代码，重点增强了健壮性和可靠性：
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```python
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import time
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import pandas as pd
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import ccxt
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from datetime import timedelta
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import talib
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import logging
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import random
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import os
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# ====================================================================================================
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# =====配置日志系统
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# ====================================================================================================
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s',
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handlers=[
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logging.FileHandler('crypto_trading.log'),
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logging.StreamHandler()
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]
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)
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logger = logging.getLogger('CryptoTrader')
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# ====================================================================================================
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# =====格式设置
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# ====================================================================================================
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pd.set_option('display.max_rows', 1000)
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pd.set_option('expand_frame_repr', False)
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pd.set_option('display.unicode.ambiguous_as_wide', True)
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pd.set_option('display.unicode.east_asian_width', True)
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# ====================================================================================================
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# =====创建ccxt交易所
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# ====================================================================================================
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def create_exchange():
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BINANCE_CONFIG = {
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'apiKey': os.getenv('BINANCE_API_KEY', ''),
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'secret': os.getenv('BINANCE_API_SECRET', ''),
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'proxies': {'http': '127.0.0.1:7890', 'https': '127.0.0.1:7890'},
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'options': {
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'defaultType': 'future',
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'adjustForTimeDifference': True
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},
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'timeout': 15000, # 增加超时时间
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'enableRateLimit': True # 启用速率限制
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}
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return ccxt.binance(BINANCE_CONFIG)
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# ====================================================================================================
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# =====重试装饰器
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# ====================================================================================================
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def retry(max_attempts=5, delay=2, backoff=2, exceptions=(Exception,)):
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"""自动重试装饰器"""
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def decorator(func):
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def wrapper(*args, **kwargs):
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attempts = 0
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current_delay = delay
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while attempts < max_attempts:
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try:
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return func(*args, **kwargs)
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except exceptions as e:
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attempts += 1
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logger.warning(f"操作失败: {str(e)}. 尝试 #{attempts}/{max_attempts}. {current_delay}秒后重试...")
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# 指数退避 + 随机抖动
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sleep_time = current_delay + random.uniform(0, 1)
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time.sleep(sleep_time)
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current_delay *= backoff
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logger.error(f"操作失败 {max_attempts} 次后放弃")
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raise
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return wrapper
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return decorator
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# ====================================================================================================
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# =====核心函数
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# ====================================================================================================
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@retry(max_attempts=5, delay=3, backoff=2, exceptions=(ccxt.NetworkError, ccxt.ExchangeNotAvailable))
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def fetch_klines(exchange, symbol, time_interval, bar_num):
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"""获取K线数据"""
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params = {
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'symbol': symbol,
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'interval': time_interval,
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'limit': bar_num
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}
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response = exchange.fapiPublicGetKlines(params=params)
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return pd.DataFrame(response, dtype=float)
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@retry(max_attempts=5, delay=2, backoff=2, exceptions=(ccxt.NetworkError, ccxt.ExchangeNotAvailable))
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def fetch_price(exchange, symbol):
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"""获取最新价格"""
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data = exchange.fapiPublicGetTickerPrice(params={'symbol': symbol})
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return float(data['price'])
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@retry(max_attempts=3, delay=5, backoff=2, exceptions=(ccxt.NetworkError, ccxt.ExchangeNotAvailable))
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def place_order(exchange, params):
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"""下订单"""
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return exchange.fapiPrivatePostOrder(params=params)
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# ====================================================================================================
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# =====数据处理函数
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# ====================================================================================================
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def process_data(df):
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"""处理K线数据并计算技术指标"""
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# 重命名列
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df.rename(columns={
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0: 'MTS', 1: 'Open', 2: 'High', 3: 'Low', 4: 'Close', 5: 'Volume'
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}, inplace=True)
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# 处理时间
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df['candle_begin_time'] = pd.to_datetime(df['MTS'], unit='ms')
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df['candle_begin_time_GMT8'] = df['candle_begin_time'] + timedelta(hours=8)
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# 保留必要列
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df = df[['candle_begin_time_GMT8', 'Open', 'High', 'Low', 'Close', 'Volume']]
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# 计算技术指标
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df['MA5'] = talib.MA(df['Close'], timeperiod=5)
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df['RSI_14'] = talib.RSI(df['Close'], timeperiod=14)
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# 计算布林带
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upper_band, middle_band, lower_band = talib.BBANDS(
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df['Close'], timeperiod=20, nbdevup=2, nbdevdn=2, matype=0
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)
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df['BB_Upper'] = upper_band
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df['BB_Middle'] = middle_band
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df['BB_Lower'] = lower_band
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# 计算ADX和ATR
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df['ADX_14'] = talib.ADX(df['High'], df['Low'], df['Close'], timeperiod=14)
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df['ATR_14'] = talib.ATR(df['High'], df['Low'], df['Close'], timeperiod=14)
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return df
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# ====================================================================================================
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# =====主交易逻辑
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# ====================================================================================================
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def main_trading_loop():
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"""主交易循环"""
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exchange = create_exchange()
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logger.info("交易所连接成功")
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symbol = 'ETHUSDT'
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time_interval = '1m'
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bar_num = 1000
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max_num = 3
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num = 0
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while num < max_num:
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try:
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# 获取数据
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kline_df = fetch_klines(exchange, symbol, time_interval, bar_num)
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df = process_data(kline_df)
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# 获取最新价格
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price_ETH = fetch_price(exchange, symbol)
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logger.info(f"ETH当前价格: {price_ETH}")
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# 交易条件判断
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if len(df) >= 2: # 确保有足够的数据
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rsi_prev = df['RSI_14'].iloc[-2]
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rsi_current = df['RSI_14'].iloc[-1]
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if rsi_prev > 30 and rsi_current < 30:
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logger.info("向下穿越RSI30，执行买入")
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# 准备订单参数
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order_params = {
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'side': 'BUY',
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'symbol': symbol,
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'type': 'LIMIT',
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'price': df['Close'].iloc[-1],
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'quantity': 0.01,
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'timestamp': int(time.time() * 1000),
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'timeInForce': 'GTC'
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}
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# 下订单
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response = place_order(exchange, order_params)
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logger.info(f"订单执行成功: {response}")
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num += 1
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else:
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logger.info("不符合交易条件，继续监控")
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time.sleep(30) # 常规等待
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else:
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logger.warning("数据不足，跳过本次循环")
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time.sleep(10)
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except ccxt.BaseError as e:
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logger.error(f"交易所API错误: {str(e)}")
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# 交易所相关错误，可能重新创建连接
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exchange = create_exchange()
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logger.info("已重新创建交易所连接")
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time.sleep(10)
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except Exception as e:
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logger.error(f"意外错误: {str(e)}", exc_info=True)
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time.sleep(30)
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except KeyboardInterrupt:
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logger.info("用户中断，程序退出")
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break
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# ====================================================================================================
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# =====程序入口
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# ====================================================================================================
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if __name__ == "__main__":
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logger.info("程序启动")
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while True:
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try:
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main_trading_loop()
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logger.info("交易次数达到上限，程序正常退出")
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break
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except Exception as e:
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logger.critical(f"主循环崩溃: {str(e)}", exc_info=True)
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logger.info("30秒后重启主循环...")
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time.sleep(30)
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```
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### 主要改进说明：
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1. **异常处理架构**：
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- 三层异常处理：函数级、主循环级、程序级
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- 使用装饰器自动重试网络操作
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- 针对不同错误类型采取不同恢复策略
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2. **健壮性增强**：
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- 指数退避 + 随机抖动重试机制
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- 交易所连接重建功能
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- 数据完整性检查（确保有足够数据）
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- 速率限制和超时设置
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3. **日志系统**：
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- 详细记录所有操作和错误
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- 同时输出到文件和终端
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- 包含时间戳和日志级别
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4. **配置优化**：
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- 使用环境变量存储API密钥
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- 增加API超时时间
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- 启用ccxt内置速率限制
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5. **代码结构**：
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- 模块化设计，分离数据获取、处理和交易逻辑
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- 函数单一职责原则
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- 清晰的错误处理流程
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### 使用建议：
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1. 将API密钥存储在环境变量中更安全：
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```
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export BINANCE_API_KEY='your_api_key'
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export BINANCE_API_SECRET='your_api_secret'
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```
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2. 监控日志文件`crypto_trading.log`了解程序运行情况
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3. 对于高频交易，考虑：
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- 增加`bar_num`减少API调用频率
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- 使用WebSocket替代轮询获取实时数据
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- 添加仓位管理和风险管理模块
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这个完善版本能够在网络中断、API限制或临时错误后自动恢复，大大提高了脚本的稳定性和可靠性。 |