209 lines
8.9 KiB
Python
209 lines
8.9 KiB
Python
import time
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from datetime import datetime
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import logging
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from typing import Optional
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import pandas as pd
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from core.biz.quant_trader import QuantTrader
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logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s')
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class QuantStrategy:
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def __init__(self,
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api_key: str,
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secret_key: str,
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passphrase: str,
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sandbox: bool = True,
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symbol: str = "BTC-USDT",
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position_size: float = 0.001):
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"""
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初始化量化策略
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"""
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self.quant_trader = QuantTrader(
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api_key,
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secret_key,
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passphrase,
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sandbox,
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symbol,
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position_size)
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def calculate_sma(self, df: pd.DataFrame, period: int = 20) -> pd.Series:
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"""
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计算简单移动平均线
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"""
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if 'close' not in df:
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logging.error("DataFrame缺少'close'列，无法计算SMA")
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return pd.Series([float('nan')] * len(df))
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if len(df) < period:
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logging.warning(f"数据长度不足{period}，SMA结果将包含NaN")
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return df['close'].rolling(window=period).mean()
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def calculate_rsi(self, df: pd.DataFrame, period: int = 14) -> pd.Series:
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"""
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计算RSI指标
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"""
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if 'close' not in df:
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logging.error("DataFrame缺少'close'列，无法计算RSI")
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return pd.Series([float('nan')] * len(df))
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if len(df) < period:
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logging.warning(f"数据长度不足{period}，RSI结果将包含NaN")
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delta = df['close'].diff()
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gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
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loss = loss.replace(0, 1e-10) # 防止除零
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rs = gain / loss
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rsi = 100 - (100 / (1 + rs))
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return rsi
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def simple_moving_average_strategy(self, sma_short_period: int = 5, sma_long_period: int = 20) -> None:
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"""
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简单移动平均线策略
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"""
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logging.info("=== 执行移动平均线策略 ===")
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try:
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df = self.quant_trader.get_kline_data(bar='5m', limit=max(50, sma_long_period+2))
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except Exception as e:
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logging.error(f"获取K线数据失败: {e}")
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return
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if df is None or len(df) < max(sma_short_period, sma_long_period, 2):
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logging.warning("数据不足，无法执行策略")
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return
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df['sma_short'] = self.calculate_sma(df, sma_short_period)
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df['sma_long'] = self.calculate_sma(df, sma_long_period)
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if len(df) < 2:
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logging.warning("数据不足2条，无法判断金叉死叉")
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return
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latest = df.iloc[-1]
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prev = df.iloc[-2]
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if pd.isna(latest['sma_short']) or pd.isna(latest['sma_long']) or pd.isna(prev['sma_short']) or pd.isna(prev['sma_long']):
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logging.warning("均线数据存在NaN，跳过本次信号判断")
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return
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logging.info(f"短期均线: {latest['sma_short']:.2f}")
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logging.info(f"长期均线: {latest['sma_long']:.2f}")
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logging.info(f"当前价格: {latest['close']:.2f}")
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if (latest['sma_short'] > latest['sma_long'] and prev['sma_short'] <= prev['sma_long']):
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logging.info("信号: 买入")
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self.quant_trader.place_market_order('buy', self.quant_trader.position_size)
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elif (latest['sma_short'] < latest['sma_long'] and prev['sma_short'] >= prev['sma_long']):
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logging.info("信号: 卖出")
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self.quant_trader.place_market_order('sell', self.quant_trader.position_size)
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else:
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logging.info("信号: 持仓观望")
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def rsi_strategy(self, period: int = 14, oversold: int = 30, overbought: int = 70) -> None:
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"""
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RSI策略
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"""
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logging.info("=== 执行RSI策略 ===")
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try:
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df = self.quant_trader.get_kline_data(bar='5m', limit=max(50, period+2))
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except Exception as e:
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logging.error(f"获取K线数据失败: {e}")
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return
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if df is None or len(df) < period:
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logging.warning("数据不足，无法执行策略")
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return
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df['rsi'] = self.calculate_rsi(df, period)
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latest_rsi = df['rsi'].iloc[-1]
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if pd.isna(latest_rsi):
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logging.warning("最新RSI为NaN，跳过本次信号判断")
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return
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logging.info(f"当前RSI: {latest_rsi:.2f}")
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if latest_rsi < oversold:
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logging.info("信号: RSI超卖，买入")
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self.quant_trader.place_market_order('buy', self.quant_trader.position_size)
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elif latest_rsi > overbought:
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logging.info("信号: RSI超买，卖出")
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self.quant_trader.place_market_order('sell', self.quant_trader.position_size)
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else:
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logging.info("信号: RSI正常区间，持仓观望")
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def grid_trading_strategy(self, grid_levels: int = 5, grid_range: float = 0.02) -> None:
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"""
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网格交易策略
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"""
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if grid_levels <= 0:
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logging.error("网格数必须大于0")
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return
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if grid_range <= 0:
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logging.error("网格范围必须大于0")
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return
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logging.info(f"=== 执行网格交易策略 (网格数: {grid_levels}, 范围: {grid_range*100}%) ===")
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try:
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current_price = self.quant_trader.get_current_price()
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except Exception as e:
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logging.error(f"获取当前价格失败: {e}")
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return
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if current_price is None:
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logging.warning("当前价格获取失败")
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return
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grid_prices = []
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for i in range(grid_levels):
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price = current_price * (1 + grid_range * (i - grid_levels//2) / grid_levels)
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grid_prices.append(price)
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logging.info(f"网格价格: {[f'${p:.2f}' for p in grid_prices]}")
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try:
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df = self.quant_trader.get_kline_data(bar='1m', limit=10)
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except Exception as e:
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logging.error(f"获取K线数据失败: {e}")
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return
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if df is None or len(df) == 0 or 'close' not in df:
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logging.warning("K线数据无效，无法执行网格策略")
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return
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latest_price = df['close'].iloc[-1]
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if pd.isna(latest_price):
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logging.warning("最新价格为NaN，跳过本次信号判断")
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return
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closest_grid = min(grid_prices, key=lambda x: abs(x - latest_price))
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logging.info(f"当前价格: ${latest_price:.2f}, 最近网格: ${closest_grid:.2f}")
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if latest_price < closest_grid * 0.995:
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logging.info("信号: 价格下跌，网格买入")
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self.quant_trader.place_market_order('buy', self.quant_trader.position_size)
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elif latest_price > closest_grid * 1.005:
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logging.info("信号: 价格上涨，网格卖出")
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self.quant_trader.place_market_order('sell', self.quant_trader.position_size)
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else:
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logging.info("信号: 价格在网格内，持仓观望")
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def run_strategy_loop(self,
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strategy: str = 'sma',
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interval: int = 60,
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trading_config: dict = {}) -> None:
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"""
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运行策略循环
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"""
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if interval <= 0:
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logging.error("循环间隔必须大于0秒")
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return
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logging.info(f"开始运行{strategy}策略，间隔{interval}秒")
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while True:
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try:
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logging.info(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
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try:
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self.quant_trader.get_account_balance()
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except Exception as e:
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logging.error(f"获取账户余额失败: {e}")
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if strategy == 'sma':
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sma_short_period = trading_config.get("sma_short_period", 5)
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sma_long_period = trading_config.get("sma_long_period", 20)
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self.simple_moving_average_strategy(sma_short_period, sma_long_period)
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elif strategy == 'rsi':
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period = trading_config.get("rsi_period", 14)
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oversold = trading_config.get("rsi_oversold", 30)
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overbought = trading_config.get("rsi_overbought", 70)
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self.rsi_strategy(period, oversold, overbought)
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elif strategy == 'grid':
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grid_levels = trading_config.get("grid_levels", 5)
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grid_range = trading_config.get("grid_range", 0.02)
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self.grid_trading_strategy(grid_levels, grid_range)
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else:
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logging.error("未知策略")
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break
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logging.info(f"等待{interval}秒后继续...")
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time.sleep(interval)
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except KeyboardInterrupt:
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logging.info("策略运行被用户中断")
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break
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except Exception as e:
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logging.error(f"策略运行异常: {e}")
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time.sleep(interval)
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