bug修复

admin

2025-05-06 8242a0c7a7fe85feaa4e652ddb3a1385173a5288

 strategy/market_sentiment_analysis.py

@@ -411,7 +411,11 @@

# 计算市场分布形态因子 函数
# ====================== 输入数据 ======================
data = {'-1': '2676', '-10': '4', '-2': '769', '-3': '181', '-4': '59', '-5': '43', '-6': '18', '-7': '11', '-8': '5', '-9': '4', '0': '219', '1': '714', '10': '6', '2': '214', '3': '69', '4': '45', '5': '23', '6': '9', '7': '9', '8': '7', '9': '6', 'DT': 17, 'SJDT': '8', 'SJZT': '17', 'STDT': '9', 'STZT': '4', 'SZJS': 1123, 'XDJS': 3787, 'ZSZDFB': '480,1639,112,123,351,26,76,202,15,17,32,1,15,30,5,78,204,18,', 'ZT': 21, 'sign': '市场人气一般', 'szln': 2591290, 'qscln': 6629612, 's_zrcs': 2604433, 'q_zrcs': 6660830, 's_zrtj': 38031347, 'q_zrtj': 91465597}
data = {'-1': '2676', '-10': '4', '-2': '769', '-3': '181', '-4': '59', '-5': '43', '-6': '18', '-7': '11', '-8': '5',
        '-9': '4', '0': '219', '1': '714', '10': '6', '2': '214', '3': '69', '4': '45', '5': '23', '6': '9', '7': '9',
        '8': '7', '9': '6', 'DT': 17, 'SJDT': '8', 'SJZT': '17', 'STDT': '9', 'STZT': '4', 'SZJS': 1123, 'XDJS': 3787,
        'ZSZDFB': '480,1639,112,123,351,26,76,202,15,17,32,1,15,30,5,78,204,18,', 'ZT': 21, 'sign': '市场人气一般',
        'szln': 2591290, 'qscln': 6629612, 's_zrcs': 2604433, 'q_zrcs': 6660830, 's_zrtj': 38031347, 'q_zrtj': 91465597}


# ====================== 总手涨跌分布数据预处理 ======================
@@ -437,8 +441,10 @@

    # 2. 定义区间划分（假设从-9%到+9%，1%间隔）
    bins = [
           "[+9%, +8%)", "[+8%, +7%)", "[+7%, +6%)", "[+6%, +5%)", "[+5%, +4%)", "[+4%, +3%)", "[+3%, +2%)", "[+2%, +1%)", "[+1%, 0%)", "[0%, -1%)", "[-1%, -2%)", "[-2%, -3%)", "[-3%, -4%)", "[-4%, -5%)", "[-5%, -6%)", "[-6%, -7%)", "[-7%, -8%)", "[ -8%, -9%)"
        ]
        "[+9%, +8%)", "[+8%, +7%)", "[+7%, +6%)", "[+6%, +5%)", "[+5%, +4%)", "[+4%, +3%)", "[+3%, +2%)", "[+2%, +1%)",
        "[+1%, 0%)", "[0%, -1%)", "[-1%, -2%)", "[-2%, -3%)", "[-3%, -4%)", "[-4%, -5%)", "[-5%, -6%)", "[-6%, -7%)",
        "[-7%, -8%)", "[ -8%, -9%)"
    ]

    # 3. 验证数据长度
    if len(counts) != len(bins):
@@ -455,6 +461,7 @@
    }

    return result


# ====================== 核心因子计算 ======================
def calculate_factors(data):
@@ -637,15 +644,20 @@
        signals.append("高情绪热度：涨停股多且人气向好")

    # 信号5：涨跌分布综合
    if factors['top_three']['top1']['value']+factors['top_three']['top2']['value']+factors['top_three']['top3']['value'] > 50:
        value = factors['top_three']['top1']['value'] + factors['top_three']['top2']['value'] + factors['top_three']['top3']['value']
    if factors['top_three']['top1']['value'] + factors['top_three']['top2']['value'] + factors['top_three']['top3'][
        'value'] > 50:
        value = factors['top_three']['top1']['value'] + factors['top_three']['top2']['value'] + \
                factors['top_three']['top3']['value']
        signals.append(f"涨跌分布：长尾分布{value}")
        if factors['top_three']['top1']['value']/factors['top_three']['top2']['value'] > 1.25:
            signals.append(f"涨跌分布：强势聚集{factors['top_three']['top1']['key']}:{factors['top_three']['top1']['value']}%")
        if factors['top_three']['top1']['value'] / factors['top_three']['top2']['value'] > 1.25:
            signals.append(
                f"涨跌分布：强势聚集{factors['top_three']['top1']['key']}:{factors['top_three']['top1']['value']}%")
        else:
            signals.append(f"涨跌分布：中度聚集{factors['top_three']['top1']['key']}:{factors['top_three']['top1']['value']}%")
            signals.append(
                f"涨跌分布：中度聚集{factors['top_three']['top1']['key']}:{factors['top_three']['top1']['value']}%")
    else:
        value = factors['top_three']['top1']['value'] + factors['top_three']['top2']['value'] + factors['top_three']['top3']['value']
        value = factors['top_three']['top1']['value'] + factors['top_three']['top2']['value'] + \
                factors['top_three']['top3']['value']
        signals.append(f"涨跌分布：均匀分布{value}")

    return signals if signals else ["无显著信号：维持当前策略"]
@@ -752,7 +764,9 @@
                    # data_cache.today_planned_order_amount = ((data_cache.real_time_market_strong + data_cache.index_trend_expectation_score) * 0.01) * (
                    #                                                 usefulMoney * 0.9 * low_emotion_mood_ratio / Unfinished_opening_plan_number)
                    # 除以3应该是一个常量，如果以Unfinished_opening_plan_number，会出现float division by zero 错误
                    data_cache.today_planned_order_amount = usefulMoney * 0.95 * ideal_trading_market_score / 3
                    if not data_cache.today_planned_order_amount or data_cache.today_planned_order_amount <= 0:
                        data_cache.today_planned_order_amount = usefulMoney * 0.95 * ideal_trading_market_score / 3
                        logger.info(f"第一次计划下单金额==={data_cache.today_planned_order_amount}元")
                    # 开仓计划运行时间段
                    if data_cache.OPENING_TIME < now_time < data_cache.NOON_MARKET_TIME:
                        # # 如果一日三仓的计划还未完成 且 首次开仓金额为0
@@ -768,7 +782,8 @@
                            #         logger.info(f"采用开仓策略计算方式=》今日计划下单金额:{data_cache.today_planned_order_amount}")
                            if addition_position_number > 0:
                                # 下过一次单过后再计算动态下单金额
                                data_cache.today_first_planned_order_amount = float(data_cache.today_planned_order_amount)
                                data_cache.today_first_planned_order_amount = float(
                                    data_cache.today_planned_order_amount)
                                logger.info(
                                    f"采用开仓策略计算方式--》今日(首个)新增持仓产生，将实时计算金额赋值给首笔持仓金额=》今日计划下单金额:{data_cache.today_planned_order_amount}")

@@ -803,13 +818,17 @@
    print(f"总股票数: {factors['total_stocks']}")
    print(f"涨跌比(BDR): {factors['rise_vs_fall']['rise_vs_fall_ratio']:.2f}")
    print(f"极端波动比例: {factors['sentiment']['extreme_ratio']:.2%}")
    print(f"资金净流入(元): {round(factors['capital_flow']['net']/10000, 2)}万")
    print(f"资金净流入(元): {round(factors['capital_flow']['net'] / 10000, 2)}万")
    print(f"涨停股占比: {factors['sentiment']['zt_ratio']:.2%}")
    print(f"聚集前三名情况字典=={factors['top_three']}")
    print(f"上涨幅段最大：{factors['rise_vs_fall']['rise_max_key']}:{factors['rise_vs_fall']['percentages'].get(factors['rise_vs_fall']['rise_max_key'])}%")
    print(f"下跌幅段最大：{factors['rise_vs_fall']['fall_max_key']}:{factors['rise_vs_fall']['percentages'].get(factors['rise_vs_fall']['fall_max_key'])}%")
    print(f"聚集区域：{factors['rise_vs_fall']['gather_area']}，聚集区域的比例值：{factors['rise_vs_fall']['percentages'].get(factors['rise_vs_fall']['gather_area'])}%")
    print(f"零散区域：{factors['rise_vs_fall']['scattered_area']}，聚集区域的比例值：{factors['rise_vs_fall']['percentages'].get(factors['rise_vs_fall']['scattered_area'])}%")
    print(
        f"上涨幅段最大：{factors['rise_vs_fall']['rise_max_key']}:{factors['rise_vs_fall']['percentages'].get(factors['rise_vs_fall']['rise_max_key'])}%")
    print(
        f"下跌幅段最大：{factors['rise_vs_fall']['fall_max_key']}:{factors['rise_vs_fall']['percentages'].get(factors['rise_vs_fall']['fall_max_key'])}%")
    print(
        f"聚集区域：{factors['rise_vs_fall']['gather_area']}，聚集区域的比例值：{factors['rise_vs_fall']['percentages'].get(factors['rise_vs_fall']['gather_area'])}%")
    print(
        f"零散区域：{factors['rise_vs_fall']['scattered_area']}，聚集区域的比例值：{factors['rise_vs_fall']['percentages'].get(factors['rise_vs_fall']['scattered_area'])}%")
    print(f"市场情绪量化: {'积极' if factors['sentiment']['sign'] else '谨慎'}")
    print(f"涨跌因子字典={factors['rise_vs_fall']}")

@@ -822,4 +841,4 @@
    for item in factors['rise_vs_fall']['parsed_data']["区间分布"]:
        print(f"{item['区间']}: {item['成交量(手)']}手")
    print(f"\n总成交量: {factors['rise_vs_fall']['parsed_data']['总成交量(手)']}手")
    print(f"说明: {factors['rise_vs_fall']['parsed_data']['说明']}")
    print(f"说明: {factors['rise_vs_fall']['parsed_data']['说明']}")