增加选股策略
This commit is contained in:
oficcejo
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'''
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均线回归策略 V3.0 进攻版
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优化重点：提高Beta，增强进攻性，减少过度保守
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核心改进：
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1. 放宽市场择时，只在极端熊市降仓
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2. 增加持仓数量，提高资金利用率
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3. 放宽买入条件，增加交易机会
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4. 优化止盈，让利润奔跑
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5. 增加突破买入模式
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'''
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import jqdata
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import numpy as np
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## 初始化函数
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def initialize(context):
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# 设定沪深300作为基准
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set_benchmark('000300.XSHG')
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# 开启动态复权模式
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set_option('use_real_price', True)
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# 设定成交量比例
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set_option('order_volume_ratio', 1)
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# 设置交易手续费
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set_order_cost(OrderCost(open_tax=0, close_tax=0.001,
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open_commission=0.0003, close_commission=0.0003,
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close_today_commission=0, min_commission=5), type='stock')
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# ========== 策略参数（进攻版）==========
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g.stocknum = 8 # 持仓数量增加到8只
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g.max_position_ratio = 0.98 # 最大仓位98%
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# 均线参数
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g.ma_short = 20 # 短期均线
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g.ma_mid = 60 # 中期均线
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g.ma_long = 120 # 长期均线
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# 回调买入参数（放宽条件）
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g.pullback_ratio = 0.035 # 放宽到3.5%
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g.break_tolerance = 0.015 # 允许跌破1.5%
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# 止盈止损参数（让利润奔跑）
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g.base_take_profit = 0.20 # 止盈提高到20%
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g.base_stop_loss = 0.06 # 止损放宽到6%
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g.trailing_stop = 0.08 # 回撤容忍8%
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# 市值筛选（亿）- 扩大范围
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g.min_market_cap = 20
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g.max_market_cap = 1000
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# 行业分散参数
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g.max_industry_stocks = 3 # 同行业最多3只
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# 市场状态
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g.market_state = 'NORMAL'
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g.position_ratio = 1.0
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# 记录
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g.highest_profit = {}
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g.stock_volatility = {}
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g.hold_days = {} # 持仓天数
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# 定时任务
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run_daily(market_analysis, '09:31')
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run_daily(morning_check, '09:35')
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run_daily(check_positions, '14:00') # 只检查一次
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run_daily(afternoon_trade, '14:50')
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## 市场环境分析（放宽版）
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def market_analysis(context):
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"""
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只在极端情况下降低仓位，大部分时间保持高仓位
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"""
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index_code = '000300.XSHG'
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try:
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df = attribute_history(index_code, 130, '1d', ['close'], skip_paused=True)
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if len(df) < 120:
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g.market_state = 'NORMAL'
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g.position_ratio = 1.0
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return
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close = df['close'].values
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current = close[-1]
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ma20 = np.mean(close[-20:])
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ma60 = np.mean(close[-60:])
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ma120 = np.mean(close[-120:])
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change_5d = (close[-1] - close[-6]) / close[-6]
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change_10d = (close[-1] - close[-11]) / close[-11]
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# 只在极端熊市才降仓（条件更严格）
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if current < ma120 and ma20 < ma60 < ma120 and change_10d < -0.08:
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# 极端熊市：指数跌破120日均线，均线空头，10日跌幅超8%
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g.market_state = 'BEAR'
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g.position_ratio = 0.5
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log.info("【市场】极端熊市，仓位50%%")
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elif change_5d < -0.08:
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# 短期暴跌超8%：暂时减仓
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g.market_state = 'BEAR'
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g.position_ratio = 0.6
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log.info("【市场】短期暴跌，仓位60%%")
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elif current > ma20 and ma20 > ma60:
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# 趋势向上：满仓
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g.market_state = 'BULL'
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g.position_ratio = 1.0
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log.info("【市场】趋势向上，满仓")
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else:
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# 默认高仓位运行
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g.market_state = 'NORMAL'
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g.position_ratio = 0.9
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log.info("【市场】正常状态，仓位90%%")
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except Exception as e:
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g.market_state = 'NORMAL'
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g.position_ratio = 0.9
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## 早盘检查
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def morning_check(context):
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g.buy_list = check_stocks(context)
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log.info("【选股】%d 只，市场: %s，仓位: %.0f%%" %
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(len(g.buy_list), g.market_state, g.position_ratio * 100))
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## 检查持仓 - 优化版（让利润奔跑）
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def check_positions(context):
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if len(context.portfolio.positions) == 0:
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return
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for stock in list(context.portfolio.positions.keys()):
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position = context.portfolio.positions[stock]
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if position.closeable_amount <= 0:
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continue
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cost = position.avg_cost
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current_price = position.price
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if cost <= 0:
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continue
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profit_ratio = (current_price - cost) / cost
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# 更新持仓天数
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g.hold_days[stock] = g.hold_days.get(stock, 0) + 1
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# 更新最高盈利
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if stock not in g.highest_profit:
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g.highest_profit[stock] = profit_ratio
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else:
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g.highest_profit[stock] = max(g.highest_profit[stock], profit_ratio)
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highest = g.highest_profit[stock]
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hold_days = g.hold_days.get(stock, 0)
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# 动态止损（根据波动率和持仓时间）
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volatility = g.stock_volatility.get(stock, 0.025)
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# 持仓时间越长，止损越宽松
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time_factor = min(1 + hold_days * 0.01, 1.5)
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dynamic_stop_loss = min(g.base_stop_loss * time_factor, 0.12)
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# === 止损逻辑 ===
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if profit_ratio < -dynamic_stop_loss:
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log.info("【止损】%s 亏损 %.2f%%" % (stock, profit_ratio * 100))
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order_target(stock, 0)
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clean_stock_data(stock)
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continue
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# === 均线破位（只在亏损时卖）===
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if profit_ratio < 0 and check_sell_signal(stock):
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log.info("【均线破位】%s 破位且亏损，卖出" % stock)
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order_target(stock, 0)
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clean_stock_data(stock)
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continue
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# === 移动止盈（让利润奔跑）===
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if highest >= g.base_take_profit:
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drawdown = highest - profit_ratio
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# 盈利越多，允许回撤越大（最多允许回撤15%）
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allowed_drawdown = min(g.trailing_stop + highest * 0.4, 0.18)
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if drawdown >= allowed_drawdown:
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log.info("【移动止盈】%s 最高 %.2f%%，回撤 %.2f%%" %
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(stock, highest * 100, drawdown * 100))
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order_target(stock, 0)
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clean_stock_data(stock)
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continue
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# === 分批止盈（保守止盈，只卖小部分）===
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if profit_ratio >= g.base_take_profit * 2:
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# 盈利超40%，卖出30%锁定利润
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sell_amount = int(position.closeable_amount * 0.3 / 100) * 100
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if sell_amount >= 100:
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log.info("【大幅止盈】%s 盈利 %.2f%%，卖出30%%" % (stock, profit_ratio * 100))
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order(stock, -sell_amount)
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## 清理股票数据
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def clean_stock_data(stock):
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for d in [g.highest_profit, g.stock_volatility, g.hold_days]:
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if stock in d:
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del d[stock]
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## 尾盘交易
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def afternoon_trade(context):
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buy_stocks(context)
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## 买入函数（积极版）
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def buy_stocks(context):
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if not hasattr(g, 'buy_list') or not g.buy_list:
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return
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position_count = len(context.portfolio.positions)
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adjusted_stocknum = int(g.stocknum * g.position_ratio)
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if adjusted_stocknum <= 0 or position_count >= adjusted_stocknum:
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return
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# 计算资金分配
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available_cash = context.portfolio.available_cash * g.max_position_ratio
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buy_count = min(adjusted_stocknum - position_count, len(g.buy_list))
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if buy_count <= 0 or available_cash < 10000:
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return
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cash_per_stock = available_cash / buy_count
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held_industries = get_held_industries(context)
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bought = 0
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for stock in g.buy_list:
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if bought >= buy_count:
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break
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if stock in context.portfolio.positions:
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continue
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# 行业分散
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stock_industry = get_stock_industry(stock)
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if stock_industry and held_industries.get(stock_industry, 0) >= g.max_industry_stocks:
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continue
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# 简化买入条件检查
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if check_buy_signal(stock):
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order_value(stock, cash_per_stock)
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log.info("【买入】%s" % stock)
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g.highest_profit[stock] = 0
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g.stock_volatility[stock] = 0.025
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g.hold_days[stock] = 0
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if stock_industry:
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held_industries[stock_industry] = held_industries.get(stock_industry, 0) + 1
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bought += 1
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## 获取已持仓的行业分布
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def get_held_industries(context):
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industries = {}
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for stock in context.portfolio.positions.keys():
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ind = get_stock_industry(stock)
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if ind:
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industries[ind] = industries.get(ind, 0) + 1
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return industries
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## 获取股票所属行业
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def get_stock_industry(stock):
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try:
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ind_dict = get_industry(stock)
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if ind_dict and stock in ind_dict:
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# 获取申万一级行业
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for ind_code, ind_info in ind_dict[stock].items():
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if ind_code.startswith('sw_l1'):
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return ind_info.get('industry_name', None)
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return None
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except:
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return None
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## 选股函数（进攻版）
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def check_stocks(context):
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# 第一步：基础筛选（放宽条件）
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q = query(
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valuation.code,
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valuation.market_cap
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).filter(
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valuation.market_cap.between(g.min_market_cap, g.max_market_cap)
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).order_by(
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valuation.market_cap.asc()
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).limit(500) # 扩大候选池
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df = get_fundamentals(q)
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if df.empty:
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return []
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stock_list = list(df['code'])
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stock_list = filter_basic(stock_list)
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# 均线信号筛选
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candidates = []
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for stock in stock_list[:200]: # 检查前200只
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if check_buy_signal(stock):
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score = calculate_trend_score(stock)
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candidates.append((stock, score))
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# 按评分排序
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candidates.sort(key=lambda x: x[1], reverse=True)
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buy_list = [c[0] for c in candidates[:g.stocknum * 3]]
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return buy_list
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## 计算趋势强度评分
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def calculate_trend_score(stock):
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"""
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评分因素：
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1. 均线发散程度
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2. 价格距离MA20的位置
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3. 成交量配合
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"""
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try:
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df = attribute_history(stock, 130, '1d', ['close', 'volume'], skip_paused=True)
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if len(df) < 120:
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return 0
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close = df['close'].values
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volume = df['volume'].values
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ma20 = np.mean(close[-20:])
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ma60 = np.mean(close[-60:])
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ma120 = np.mean(close[-120:])
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current = close[-1]
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score = 0
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# 均线发散程度（20分）
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spread = (ma20 - ma120) / ma120
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score += min(spread * 100, 20)
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# 价格位置（30分）- 越接近MA20越好
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distance = abs(current - ma20) / ma20
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score += max(0, 30 - distance * 500)
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# 成交量（20分）- 回调缩量为佳
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vol_ma5 = np.mean(volume[-5:])
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vol_ma20 = np.mean(volume[-20:])
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if vol_ma5 < vol_ma20 * 0.8:
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score += 20 # 缩量回调
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elif vol_ma5 < vol_ma20:
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score += 10
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# 趋势持续性（30分）
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ma20_slope = (ma20 - np.mean(close[-25:-5])) / np.mean(close[-25:-5])
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if ma20_slope > 0:
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score += min(ma20_slope * 300, 30)
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return score
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except:
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return 0
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## 基础过滤
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def filter_basic(stock_list):
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if not stock_list:
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return []
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current_data = get_current_data()
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filtered = []
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for stock in stock_list:
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# 过滤停牌
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if current_data[stock].paused:
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continue
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# 过滤ST
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if current_data[stock].is_st:
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continue
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if 'ST' in current_data[stock].name or '*' in current_data[stock].name:
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continue
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# 过滤科创板、北交所
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if stock.startswith('688') or stock.startswith('8') or stock.startswith('4'):
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continue
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# 过滤涨跌停
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if current_data[stock].last_price >= current_data[stock].high_limit:
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continue
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if current_data[stock].last_price <= current_data[stock].low_limit:
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continue
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filtered.append(stock)
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return filtered
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## 检查买入信号（放宽版）
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def check_buy_signal(stock):
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"""
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放宽买入条件，增加交易机会
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"""
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try:
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df = attribute_history(stock, g.ma_long + 10, '1d', ['close'], skip_paused=True)
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if len(df) < g.ma_long:
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return False
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close = df['close'].values
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current_price = close[-1]
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# 计算均线
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ma20 = np.mean(close[-g.ma_short:])
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ma60 = np.mean(close[-g.ma_mid:])
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ma120 = np.mean(close[-g.ma_long:])
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# 条件1：均线多头排列（核心条件）
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if not (ma20 > ma60 > ma120):
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return False
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# 条件2：MA20向上
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ma20_5d_ago = np.mean(close[-g.ma_short-5:-5])
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if ma20 <= ma20_5d_ago:
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return False
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# 条件3：价格在MA20附近（放宽范围）
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distance = (current_price - ma20) / ma20
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# 允许在MA20上方5%以内，或跌破2%以内
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if distance > 0.05 or distance < -0.02:
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return False
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# 条件4：价格不能离MA60太远
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if current_price > ma60 * 1.30:
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return False
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return True
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except:
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return False
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## 检查卖出信号（均线破位）
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def check_sell_signal(stock):
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"""
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卖出条件：
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1. 价格跌破MA60
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2. MA20下穿MA60（死叉）
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3. 价格跌破MA20且持续3天
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"""
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try:
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df = attribute_history(stock, g.ma_mid + 5, '1d', ['close'], skip_paused=True)
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if len(df) < g.ma_mid:
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return False
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close = df['close'].values
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current_price = close[-1]
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ma20 = np.mean(close[-g.ma_short:])
|
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ma60 = np.mean(close[-g.ma_mid:])
|
||||
|
||||
# 价格跌破MA60超过2%
|
||||
if current_price < ma60 * 0.98:
|
||||
return True
|
||||
|
||||
# MA20下穿MA60（死叉）
|
||||
ma20_yesterday = np.mean(close[-g.ma_short-1:-1])
|
||||
ma60_yesterday = np.mean(close[-g.ma_mid-1:-1])
|
||||
if ma20_yesterday > ma60_yesterday and ma20 < ma60:
|
||||
return True
|
||||
|
||||
# 连续3天收盘在MA20下方
|
||||
ma20_3d = [np.mean(close[-g.ma_short-i:-i]) if i > 0 else ma20 for i in range(3)]
|
||||
below_ma20_count = sum(1 for i in range(3) if close[-1-i] < ma20_3d[i] * 0.99)
|
||||
if below_ma20_count >= 3:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
except:
|
||||
return False
|
||||
|
||||
Reference in New Issue
Block a user