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Ai全自动编写策略：SAR布林带多阶段策略(通达信&python双源码)

松鼠Quant

2025-11-05

量化策略开发，高质量社群，交易思路分享等相关内容

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『正文』

Ai编写策略是量化领域的新常态，松鼠Quant在早些时候推出了开源项目《gitee官方推荐，松鼠QuantAi+回测框架详细版教程(图文)》让Ai自动编写策略，自动回测，给出总结和迭代意见。

现在我们将通达信指标：《买在起涨点》，让Ai自动完成python编写，然后自动回测总结。

通达信源码：

{指标介绍：黄金点三个时买入，蓝点出时毫不犹豫出半仓，加之和布林轨结合用，站稳中轨持半仓，直到第二波蓝点出就全止盈。出第一蓝点时同时破中轨肯定是大阴线就全止盈出局。}
VAR1:=1090630;喜来财:DRAWNULL,NODRAW;珍珠点:IF(DATE<=VAR1,SAR(4,2,20),MA(CLOSE,30)),NODRAW,COLORWHITE;彩虹带:MA(CLOSE,30),COLORWHITE,LINETHICK2;SMX:MA(CLOSE,30),COLORRED;A:=珍珠点,NODRAW;B:=(MA(CLOSE,30)-REF(MA(CLOSE,30),1))*100/REF(MA(CLOSE,30),1);{以下是在k线图上画sar指标线}IF(CLOSE>=A,A,DRAWNULL),POINTDOT,COLORRED,LINETHICK4;IF(CLOSE<A,A,DRAWNULL),POINTDOT,COLORGREEN,LINETHICK4;IF(MA(B,3)<0.1,MA(CLOSE,30),DRAWNULL),COLORGRAY,LINETHICK2;{划彩色k线}STICKLINE(C>=A AND C>=O,C,O,2.8,1),COLORRED;STICKLINE(C>=A AND C<O,C-0.003*C,O,2.8,0),COLORRED;STICKLINE(C>=A ,H,C,0,0),COLORRED;STICKLINE(C>=A ,O,L-0.003*C,0,0),COLORRED;STICKLINE((C<A AND C>=O),C,O,2.8,1),COLORGREEN;STICKLINE((C<A AND C<O),C-0.003*C,O,2.8,0),COLORGREEN;STICKLINE(C<A ,H,C,0,0),COLORGREEN;STICKLINE(C<A ,O,L-0.003*C,0,0),COLORGREEN;JJ:=(CLOSE+HIGH+LOW)/3;A8:=EMA(JJ,10);B8:=REF(A8,1);持股区域:STICKLINE(A8>B8,A8,B8,2,0),COLORYELLOW;持币区域:STICKLINE(A8<B8,A8,B8,2,0),COLORBLUE;

接下来让Ai帮我们转化到松鼠Quant回测框架内，我们不需要写一行代码！！！

github地址(国外)：

https://github.com/songshuquant/ssquant-ai

gitee地址(国内):

https://gitee.com/ssquant/ssquant-ai

如何使用，手把手教程：《gitee官方推荐，松鼠QuantAi+回测框架详细版教程(图文)》

提出需求：

自动修复BUG：

回测完成：

Ai给出改进意见：

Python源码：

import pandas as pdimport numpy as npfrom ssquant.backtest.multi_source_backtest import MultiSourceBacktesterfrom ssquant.api.strategy_api import StrategyAPI# 使用全局变量来存储策略状态，避免API对象属性问题gold_points = 0blue_point_occurred = Falsefirst_blue_exit = Falsedef initialize(api: StrategyAPI): """策略初始化函数""" global gold_points, blue_point_occurred, first_blue_exit api.log("SAR布林带多阶段策略初始化") # 重置全局状态变量 gold_points = 0 # 黄金点计数 blue_point_occurred = False # 是否出现过蓝点 first_blue_exit = False # 是否执行过第一次蓝点减仓def strategy_function(api: StrategyAPI): """ SAR布林带多阶段策略策略逻辑： 1. 黄金点三个时买入开仓 2. 蓝点出现时毫不犹豫出半仓 3. 结合布林轨：站稳中轨持半仓，直到第二波蓝点出就全止盈 4. 出第一蓝点时同时破中轨（大阴线）就全止盈出局 """ global gold_points, blue_point_occurred, first_blue_exit # 数据验证 - 增强版 current_idx = api.get_idx() if current_idx is None: return # 需要足够的数据计算指标 if current_idx < 30: return close = api.get_close() high = api.get_high() # 修复：使用正确的API方法 low = api.get_low() # 修复：使用正确的API方法 open_price = api.get_open() # 增强数据验证 if (close is None or len(close) == 0 or high is None or len(high) == 0 or low is None or len(low) == 0 or open_price is None or len(open_price) == 0): api.log("警告: 数据为空或数据源异常") return # 检查索引是否在有效范围内 if current_idx >= len(close): api.log(f"警告: 索引 {current_idx} 超出数据范围 {len(close)}") return # 获取策略参数 sar_step = api.get_param('sar_step', 0.02) sar_max = api.get_param('sar_max', 0.2) boll_period = api.get_param('boll_period', 20) boll_std = api.get_param('boll_std', 2) # 计算SAR指标 - 修复版 def calculate_sar(high_series, low_series, acceleration=0.02, maximum=0.2): """计算抛物线转向指标SAR""" length = len(high_series) if length == 0: return pd.Series([], dtype=float) sar = np.zeros(length) ep = np.zeros(length) af = np.zeros(length) trend = np.zeros(length) # 初始化 sar[0] = low_series.iloc[0] ep[0] = high_series.iloc[0] af[0] = acceleration trend[0] = 1 for i in range(1, length): # 保存前一个值 prev_sar = sar[i-1] prev_ep = ep[i-1] prev_af = af[i-1] prev_trend = trend[i-1] # 反转情况 if prev_trend == 1: # 上升趋势 sar[i] = prev_sar + prev_af * (prev_ep - prev_sar) if high_series.iloc[i] > prev_ep: ep[i] = high_series.iloc[i] af[i] = min(prev_af + acceleration, maximum) else: ep[i] = prev_ep af[i] = prev_af if low_series.iloc[i] < sar[i]: trend[i] = -1 sar[i] = prev_ep ep[i] = low_series.iloc[i] af[i] = acceleration else: trend[i] = 1 else: # 下降趋势 sar[i] = prev_sar + prev_af * (prev_ep - prev_sar) if low_series.iloc[i] < prev_ep: ep[i] = low_series.iloc[i] af[i] = min(prev_af + acceleration, maximum) else: ep[i] = prev_ep af[i] = prev_af if high_series.iloc[i] > sar[i]: trend[i] = 1 sar[i] = prev_ep ep[i] = high_series.iloc[i] af[i] = acceleration else: trend[i] = -1 return pd.Series(sar, index=high_series.index) # 计算布林带 - 修复版 def calculate_bollinger_bands(close_series, period=20, std_dev=2): """计算布林带指标""" if len(close_series) < period: return pd.Series([], dtype=float), pd.Series([], dtype=float), pd.Series([], dtype=float) middle_band = close_series.rolling(window=period, min_periods=1).mean() std = close_series.rolling(window=period, min_periods=1).std() upper_band = middle_band + (std * std_dev) lower_band = middle_band - (std * std_dev) return upper_band, middle_band, lower_band try: # 计算指标 sar_values = calculate_sar(high, low, sar_step, sar_max) boll_upper, boll_middle, boll_lower = calculate_bollinger_bands(close, boll_period, boll_std) # 检查指标计算结果 if (len(sar_values) == 0 or len(boll_middle) == 0 or current_idx >= len(sar_values) or current_idx >= len(boll_middle)): return current_close = close.iloc[current_idx] current_sar = sar_values.iloc[current_idx] prev_sar = sar_values.iloc[current_idx-1] if current_idx > 0 else current_sar current_boll_middle = boll_middle.iloc[current_idx] # 判断黄金点（价格上穿SAR且SAR转势向上） is_gold_point = False if current_idx >= 2: is_gold_point = (current_close > current_sar and current_sar > prev_sar and prev_sar <= sar_values.iloc[current_idx-2]) # 判断蓝点（价格下穿SAR） is_blue_point = (current_close < current_sar and current_close <= prev_sar) # 判断是否破中轨（大阴线情况） break_middle_band = (current_close < current_boll_middle and open_price.iloc[current_idx] > current_boll_middle) current_pos = api.get_pos() # 策略逻辑执行 if is_gold_point: gold_points += 1 api.log(f"检测到黄金点 #{gold_points}, 价格: {current_close:.2f}, SAR: {current_sar:.2f}") # 三个黄金点买入 if gold_points >= 3 and current_pos == 0: api.buy(volume=1, order_type='next_bar_open') api.log(f"三个黄金点确认，买入开仓，价格: {current_close:.2f}") gold_points = 0 # 重置计数 blue_point_occurred = False first_blue_exit = False elif is_blue_point: api.log(f"检测到蓝点，价格: {current_close:.2f}, SAR: {current_sar:.2f}") blue_point_occurred = True # 第一蓝点处理 if not first_blue_exit and current_pos > 0: if break_middle_band: # 蓝点+破中轨（大阴线）- 全止盈 api.close_all(order_type='next_bar_open') api.log(f"蓝点+破中轨大阴线，全止盈出局，价格: {current_close:.2f}") first_blue_exit = True gold_points = 0 else: # 蓝点出现 - 出半仓 exit_volume = current_pos // 2 if exit_volume > 0: api.sell(volume=exit_volume, order_type='next_bar_open') api.log(f"蓝点出现，减半仓 {exit_volume}手，价格: {current_close:.2f}") first_blue_exit = True # 第二蓝点处理 - 全止盈 elif first_blue_exit and current_pos > 0: api.close_all(order_type='next_bar_open') api.log(f"第二蓝点出现，全止盈出局，价格: {current_close:.2f}") first_blue_exit = False gold_points = 0 # 布林带中轨支撑检查（持半仓情况下） if (first_blue_exit and current_pos > 0 and current_close >= current_boll_middle and not is_blue_point): # 站稳中轨，保持半仓 api.log(f"价格站稳布林带中轨 {current_boll_middle:.2f}，保持半仓状态") except Exception as e: api.log(f"策略执行异常: {str(e)}") returnif __name__ == "__main__": # 导入API认证信息 try: from ssquant.config.auth_config import get_api_auth API_USERNAME, API_PASSWORD = get_api_auth() except ImportError: print("警告：未找到 auth_config.py 文件，请在下方填写您的认证信息：API_USERNAME和API_PASSWORD") API_USERNAME = "" API_PASSWORD = "" # 创建多数据源回测器 backtester = MultiSourceBacktester() # 设置基础配置 backtester.set_base_config({ 'username': API_USERNAME, # 使用配置文件中的用户名 'password': API_PASSWORD, # 使用配置文件中的密码 'use_cache': True, # 是否使用缓存数据 'save_data': True, # 是否保存数据 'align_data': False, # 是否对齐数据 'fill_method': 'ffill', # 填充方法 'debug': True # 启用调试模式 }) # 添加品种配置（黄金期货） backtester.add_symbol_config( symbol='au888', config={ 'start_date': '2024-01-01', # 回测开始日期 'end_date': '2025-12-31', # 回测结束日期 'initial_capital': 100000.0, # 初始资金，单位：元 'commission': 0.0003, # 手续费率 'margin_rate': 0.1, # 保证金率 'contract_multiplier': 1000, # 黄金期货合约乘数：1000克/手 'periods': [ {'kline_period': '1d', 'adjust_type': '1'}, # 日线，后复权 ] } ) # 设置策略参数 strategy_params = { 'sar_step': 0.02, # SAR加速因子初始值 'sar_max': 0.2, # SAR加速因子最大值 'boll_period': 20, # 布林带周期 'boll_std': 2 # 布林带标准差倍数 } # 运行回测 results = backtester.run( strategy=strategy_function, initialize=initialize, strategy_params=strategy_params, )

没错，整个开发过程我们没有手写一行代码，松鼠QuantAi助手会编写，回测，分析，修改。基于松鼠Quant的MCP服务器+Deepseek开启全新量化学习的路径，即使0基础也能完成策略的编写与验证！

如果你喜欢这个系列，请点赞，转发，在看三连。我们根据受欢迎程度决定是否继续更新，感谢您的支持。

github地址(国外)：

https://github.com/songshuquant/ssquant-ai

gitee地址(国内):

https://gitee.com/ssquant/ssquant-ai

如何使用，手把手教程：《gitee官方推荐，松鼠QuantAi+回测框架详细版教程(图文)》

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