oficcejo
417 lines
12 KiB
Python
417 lines
12 KiB
Python
'''
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三重滤网交易系统 V2.0
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by Alexander Elder (优化版)
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核心改进：
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1. 第一重滤网：月线MACD金叉/多头（更稳定的趋势判断）
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2. 第二重滤网：日线RSI/KDJ超卖回调（放宽条件）
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3. 第三重滤网：价格企稳或突破（更灵活的入场）
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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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set_benchmark('000300.XSHG')
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set_option('use_real_price', True)
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set_option('order_volume_ratio', 1)
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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 ==========
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g.stocknum = 10 # 增加持仓数
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g.max_position_ratio = 0.98
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# 第一重滤网参数（月线MACD）
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g.macd_fast = 12
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g.macd_slow = 26
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g.macd_signal = 9
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# 第二重滤网参数（日线震荡 - 放宽）
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g.rsi_period = 14
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g.rsi_oversold = 50 # 放宽到50
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g.rsi_low = 35 # 极度超卖
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g.kdj_oversold = 40 # 放宽到40
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# 第三重滤网参数（入场确认 - 放宽）
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g.ma_short = 5 # 5日均线
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g.ma_mid = 20 # 20日均线
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# 止盈止损参数
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g.stop_loss = 0.06
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g.take_profit = 0.20
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g.trailing_stop = 0.08
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# 市值筛选（亿）
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g.min_market_cap = 20
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g.max_market_cap = 1500
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# 记录
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g.highest_profit = {}
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g.hold_days = {}
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# 定时任务
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run_daily(morning_screen, '09:35')
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run_daily(check_positions, '14:00')
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run_daily(afternoon_trade, '14:50')
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## 早盘筛选
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def morning_screen(context):
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g.buy_list = triple_screen_filter(context)
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log.info("【三重滤网】筛选出 %d 只股票" % len(g.buy_list))
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## 三重滤网筛选
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def triple_screen_filter(context):
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# 基础选股
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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)
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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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# 三重滤网筛选
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candidates = []
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for stock in stock_list[:150]:
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result = check_triple_screen(stock)
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if result['pass']:
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candidates.append((stock, result['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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return [c[0] for c in candidates[:g.stocknum * 3]]
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## 检查三重滤网信号
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def check_triple_screen(stock):
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"""
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三重滤网检查 V2.0：
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1. 第一重：月线MACD金叉/多头
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2. 第二重：日线RSI/KDJ回调
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3. 第三重：价格企稳或突破
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"""
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result = {'pass': False, 'score': 0}
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try:
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# 获取更多日线数据（计算月线MACD需要约130天）
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df = attribute_history(stock, 150, '1d', ['close', 'high', 'low'], skip_paused=True)
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if len(df) < 130:
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return result
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close = df['close'].values
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high = df['high'].values
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low = df['low'].values
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# ========== 第一重滤网：月线MACD ==========
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# 模拟月线：每20天取收盘价（或用月末价格）
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monthly_close = []
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for i in range(19, len(close), 20):
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monthly_close.append(close[i])
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if len(monthly_close) < 6:
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# 数据不足，改用日线MACD判断大趋势
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dif, dea, macd = calculate_macd(close)
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if dif[-1] <= dea[-1]: # MACD死叉
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return result
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if dif[-1] <= 0: # DIF在零轴下方
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return result
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else:
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# 计算月线MACD
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m_dif, m_dea, m_macd = calculate_macd(np.array(monthly_close))
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# 月线MACD条件（放宽）：
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# 1. MACD金叉（DIF上穿DEA）或
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# 2. MACD柱由绿变红 或
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# 3. DIF > 0 且 DIF > DEA（多头）
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macd_golden = m_dif[-1] > m_dea[-1] and m_dif[-2] <= m_dea[-2]
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macd_turn_red = m_macd[-1] > 0 and m_macd[-2] <= 0
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macd_bullish = m_dif[-1] > 0 and m_dif[-1] > m_dea[-1]
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if not (macd_golden or macd_turn_red or macd_bullish):
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return result
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result['score'] += 30 # 趋势分
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# ========== 第二重滤网：日线震荡指标 ==========
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rsi = calculate_rsi(close, g.rsi_period)
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k, d, j = calculate_kdj(high, low, close)
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# RSI条件（放宽）：
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# 1. RSI < 50（相对低位）或
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# 2. RSI从低位回升
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rsi_current = rsi[-1] if len(rsi) > 0 else 50
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rsi_prev = rsi[-2] if len(rsi) > 1 else 50
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rsi_signal = (rsi_current < g.rsi_oversold) or \
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(rsi_prev < g.rsi_low and rsi_current > rsi_prev) or \
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(rsi_current < 60 and rsi_current > rsi_prev)
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# KDJ条件（放宽）：
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# 1. K < D 后金叉 或
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# 2. J < 40 或
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# 3. K/D都在低位回升
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kdj_golden = k[-1] > d[-1] and k[-2] <= d[-2]
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kdj_oversold = j[-1] < g.kdj_oversold or k[-1] < g.kdj_oversold
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kdj_rising = k[-1] > k[-2] and d[-1] > d[-2] and k[-1] < 60
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kdj_signal = kdj_golden or kdj_oversold or kdj_rising
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if not (rsi_signal or kdj_signal):
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return result
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result['score'] += 30
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# ========== 第三重滤网：入场确认（放宽）==========
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current_price = close[-1]
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ma5 = np.mean(close[-g.ma_short:])
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ma20 = np.mean(close[-g.ma_mid:])
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# 入场条件（满足任一）：
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# 1. 价格站上5日均线
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# 2. 价格接近20日均线（±3%）
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# 3. 价格突破近5日高点
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recent_high = np.max(high[-6:-1])
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price_above_ma5 = current_price > ma5
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price_near_ma20 = abs(current_price - ma20) / ma20 < 0.03
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price_breakout = current_price > recent_high * 0.99
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if not (price_above_ma5 or price_near_ma20 or price_breakout):
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return result
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result['score'] += 40
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# 额外加分
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if rsi_current < 35:
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result['score'] += 15
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if kdj_golden:
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result['score'] += 10
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if current_price > ma5 > ma20:
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result['score'] += 10
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result['pass'] = True
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return result
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except:
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return result
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## 计算MACD
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def calculate_macd(close, fast=12, slow=26, signal=9):
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ema_fast = calculate_ema(close, fast)
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ema_slow = calculate_ema(close, slow)
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dif = ema_fast - ema_slow
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dea = calculate_ema(dif, signal)
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macd = (dif - dea) * 2
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return dif, dea, macd
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## 计算EMA
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def calculate_ema(data, period):
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ema = np.zeros(len(data))
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ema[0] = data[0]
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multiplier = 2 / (period + 1)
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for i in range(1, len(data)):
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ema[i] = (data[i] - ema[i-1]) * multiplier + ema[i-1]
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return ema
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## 计算RSI
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def calculate_rsi(close, period=14):
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delta = np.diff(close)
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gain = np.where(delta > 0, delta, 0)
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loss = np.where(delta < 0, -delta, 0)
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avg_gain = np.zeros(len(delta))
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avg_loss = np.zeros(len(delta))
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avg_gain[period-1] = np.mean(gain[:period])
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avg_loss[period-1] = np.mean(loss[:period])
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for i in range(period, len(delta)):
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avg_gain[i] = (avg_gain[i-1] * (period-1) + gain[i]) / period
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avg_loss[i] = (avg_loss[i-1] * (period-1) + loss[i]) / period
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rs = avg_gain / (avg_loss + 1e-10)
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rsi = 100 - 100 / (1 + rs)
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return rsi
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## 计算KDJ
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def calculate_kdj(high, low, close, n=9, m1=3, m2=3):
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length = len(close)
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rsv = np.zeros(length)
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k = np.zeros(length)
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d = np.zeros(length)
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j = np.zeros(length)
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for i in range(n-1, length):
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hn = np.max(high[i-n+1:i+1])
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ln = np.min(low[i-n+1:i+1])
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rsv[i] = (close[i] - ln) / (hn - ln + 1e-10) * 100
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k[n-1] = 50
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d[n-1] = 50
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for i in range(n, length):
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k[i] = (m1-1)/m1 * k[i-1] + 1/m1 * rsv[i]
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d[i] = (m2-1)/m2 * d[i-1] + 1/m2 * k[i]
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j[i] = 3 * k[i] - 2 * d[i]
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return k, d, j
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## 检查持仓
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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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g.hold_days[stock] = g.hold_days.get(stock, 0) + 1
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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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# === 止损 ===
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if profit_ratio < -g.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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# === 趋势反转卖出 ===
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if check_trend_reversal(stock):
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log.info("【趋势反转】%s 周线趋势转弱" % 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 ===
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if highest >= g.take_profit:
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drawdown = highest - profit_ratio
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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%.1f%% 回撤%.1f%%" %
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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 ===
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if profit_ratio >= g.take_profit * 2:
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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("【止盈】%s +%.1f%%" % (stock, profit_ratio*100))
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order(stock, -sell_amount)
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## 检查趋势反转（使用MACD）
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def check_trend_reversal(stock):
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try:
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df = attribute_history(stock, 60, '1d', ['close'], skip_paused=True)
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if len(df) < 50:
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return False
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close = df['close'].values
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dif, dea, macd = calculate_macd(close)
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# MACD死叉且DIF < 0
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if dif[-1] < dea[-1] and dif[-1] < 0:
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return True
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# MACD连续3天下降且为负
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if macd[-1] < 0 and macd[-2] < 0 and macd[-3] < 0:
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if macd[-1] < macd[-2] < macd[-3]:
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return True
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return False
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except:
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return False
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## 清理数据
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def clean_stock_data(stock):
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for d in [g.highest_profit, 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
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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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if position_count >= g.stocknum:
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return
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available_cash = context.portfolio.available_cash * g.max_position_ratio
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buy_count = min(g.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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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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# 再次确认三重滤网信号
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result = check_triple_screen(stock)
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if result['pass']:
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order_value(stock, cash_per_stock)
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log.info("【买入】%s 评分:%.0f" % (stock, result['score']))
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g.highest_profit[stock] = 0
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g.hold_days[stock] = 0
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bought += 1
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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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if current_data[stock].paused:
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continue
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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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if stock.startswith('688') or stock.startswith('8') or stock.startswith('4'):
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continue
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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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