inflow:高效异步处理文本流的JavaScript库

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根据提供的文件信息,我们可以展开以下关于“inflow”包在JavaScript中逐行处理文本流的知识点: ### 标题解读 - **inflow**: 这是Node.js环境下一个用于逐行处理文本流的模块。 - **逐行有效地处理文本流**: 表示该模块专为文本数据的逐行读取设计,目的是高效处理文本数据。 ### 描述解读 - **流入支持**: 描述了该模块支持的各种特性。 - **节点支持的所有字符集**: 意味着该模块能够处理多种字符编码的文本数据,例如UTF-8、GBK等。 - **在后台快速高效地处理和缓冲**: 指的是“inflow”可以在不阻塞主线程的情况下,对文本流进行快速的处理和缓冲管理,从而提高程序的性能。 - **阻塞,非阻塞和异步访问**: 描述了模块提供的不同数据访问方式。阻塞访问会导致等待数据,而非阻塞方式允许程序在数据未准备好时继续执行其他任务,异步访问通常指不直接等待操作完成,而是通过回调、事件或Promise等机制来处理结果。 ### 安装与用法 - **安装**: 使用npm命令`npm i --save @dev-essentials/inflow`来安装“inflow”模块。其中`--save`标志会将模块添加到package.json文件的dependencies部分。 - **用法**: 提供了两种异步访问模式的示例代码,展示了如何使用“inflow”模块来从标准输入(stdin)逐行读取文本数据。 - **异步访问(异步/等待)**: 使用了`async/await`语法,这种方式可以让异步代码的书写和理解更接近同步代码的风格。示例中的`stream.nextAsync()`方法是异步获取下一行文本,当一行文本到来时,`await`会暂停代码执行直到读取完成。 - **异步访问(无异步/等待)**: 这里没有使用`async/await`,而是直接使用Promise或者回调函数来处理异步操作。尽管代码示例没有给出完整的回调逻辑,但通常`stream.next()`会返回一个Promise对象,可以通过`.then()`和`.catch()`来处理。 ### 压缩包子文件的文件名称列表 - **inflow-master**: 这可能是一个压缩包,包含了“inflow”模块的源代码、文档和其他相关文件。文件名中的“master”表明这可能是一个版本标识,通常表示稳定或主版本。 ### JavaScript知识点总结 - **Node.js流处理**: JavaScript在Node.js环境中,通过流(Streams)来处理读写操作,特别是处理大文件或网络通信中的数据流。流是抽象的数据接口,它允许以高效的方式按块处理数据。 - **异步编程**: JavaScript是基于事件循环和异步非阻塞I/O模型的,这使得它非常适合处理高并发任务。异步编程技术包括回调函数、Promises、async/await语法,这些技术使得能够在不阻塞主线程的情况下进行复杂的数据处理。 - **npm模块管理**: Node.js包管理器npm是一个强大的工具,用于安装、分享和管理JavaScript代码。通过`npm i --save`命令,开发者可以方便地将模块添加到项目依赖中,并自动更新package.json文件。 - **字符编码**: 在处理文本流时,了解和正确使用字符编码至关重要,因为它确保了数据在各种环境和平台间的正确读取和显示。 - **异步循环**: 在异步编程中,正确地进行循环操作(如逐行读取数据),需要考虑到循环的异步特性和避免常见的陷阱(比如迭代器提前关闭或者数据竞争问题)。 通过以上知识点,我们可以看出“inflow”是一个非常适合需要处理大量文本数据的JavaScript开发者使用的模块,它简化了异步逐行读取的复杂性,并提供了高效的处理机制。

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import numpy as np import tensorflow as tf from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, Conv1D, Dense, Dropout, Flatten, TimeDistributed from sklearn.preprocessing import StandardScaler from sklearn.metrics import mean_absolute_error # 生成虚拟交通流量数据 def generate_virtual_taxi_data(num_nodes=50, time_steps=288, features=2): np.random.seed(42) # 确保可复现性 base_flow = np.linspace(100, 500, num_nodes) # 基础流量(辆/小时) inflow = np.random.normal(loc=base_flow*0.8, scale=base_flow*0.1, size=(time_steps, num_nodes)) outflow = np.random.normal(loc=base_flow*0.7, scale=base_flow*0.1, size=(time_steps, num_nodes)) # 添加早晚高峰特征 peak_mask = np.zeros((time_steps, num_nodes), dtype=bool) peak_mask[np.logical_or(time_steps*0.25<np.arange(time_steps), time_steps*0.75>np.arange(time_steps))] = True inflow[peak_mask] *= 1.5 outflow[peak_mask] *= 1.3 # 构建完整数据集 traffic_data = np.stack([inflow, outflow], axis=-1) return traffic_data # 创建序列数据 def create_sequences(data, seq_length): X, y = [], [] for i in range(len(data) - seq_length): X.append(data[i:i+seq_length]) y.append(data[i+seq_length]) return np.array(X), np.array(y) # 构建多任务模型 def build_multi_task_model(input_shape): inputs = Input(shape=input_shape) x = tf.keras.layers.TimeDistributed(Conv1D(filters=64, kernel_size=3, activation='relu', padding='same'))(inputs) x = tf.keras.layers.TimeDistributed(Flatten())(x) x = tf.keras.layers.LSTM(128, return_sequences=True)(x) # 分支预测流入和流出 inflow_output = Dense(1, name='inflow')(x) outflow_output = Dense(1, name='outflow')(x) model = Model(inputs, [inflow_output, outflow_output]) model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), loss={'inflow': 'mse', 'outflow': 'mse'}, metrics={'inflow': 'mae', 'outflow': 'mae'}) return model # 主函数 def main(): num_nodes = 50 time_steps = 288 features = 2 seq_length = 12 # 生成虚拟数据 traffic_data = generate_virtual_taxi_data(num_nodes, time_steps, features) print(f"Virtual Traffic Data Shape: {traffic_data.shape}") # 应输出 (288,50,2) # 数据预处理 scaler = StandardScaler() traffic_data_scaled = scaler.fit_transform(traffic_data.reshape(-1, features)).reshape(traffic_data.shape) # 创建序列数据 X, y = create_sequences(traffic_data_scaled, seq_length) # 划分训练集/测试集 split = int(0.8 * X.shape[0]) X_train, X_test = X[:split], X[split:] y_train, y_test = y[:split], y[split:] # 模型训练 model = build_multi_task_model(input_shape=(seq_length, num_nodes, features)) history = model.fit(X_train, {'inflow': y_train[..., 0].reshape(-1, 1), 'outflow': y_train[..., 1].reshape(-1, 1)}, epochs=50, batch_size=32, validation_split=0.2) # 结果评估 y_pred_inflow, y_pred_outflow = model.predict(X_test) y_pred_inflow = scaler.inverse_transform(y_pred_inflow).flatten() y_pred_outflow = scaler.inverse_transform(y_pred_outflow).flatten() y_test_inflow = scaler.inverse_transform(y_test[..., 0]).flatten() y_test_outflow = scaler.inverse_transform(y_test[..., 1]).flatten() mae_inflow = mean_absolute_error(y_test_inflow, y_pred_inflow) mae_outflow = mean_absolute_error(y_test_outflow, y_pred_outflow) print(f"MAE Inflow: {mae_inflow:.2f}辆/小时") print(f"MAE Outflow: {mae_outflow:.2f}辆/小时") if __name__ == '__main__': main()帮我修改这段代码

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``` {智能估值体系V14.1优化版} 大盘过滤:=INDEXC>MA(INDEXC,60) AND INDEXC>MA(INDEXC,120); {动态PE优化} DYNPETTM:=IF(FINANCE(1)>3E8 AND FINANCE(4)>1.5E8, CLOSE/((FINANCE(1)/MAX(FINANCE(4),1.2E8)+1E-6)*0.81)* (1+0.18*INDUSTRYF(1013)),1000); {PB率优化} PB_RATE:=IF(FINANCE(34)>0.88 AND CLOSE>5.5, CLOSE/((FINANCE(34)*0.85+REF(FINANCE(34),1)*0.15)*0.97+1E-6),1000); {PEG计算优化} PEG_VAL:=DYNPETTM/MAX(FINANCE(30)/REF(MAX(FINANCE(30),0.01),4),1.35); {分形波动率V21} VAR_PERIOD:=IF(STD(CLOSE,89)/CLOSE<0.018,377, IF(STD(CLOSE,89)/CLOSE<0.04,233,89)); SLOW_LEN:=IF(STD(CLOSE,89)/CLOSE>0.2,INT(VAR_PERIOD*1.618),CEILING(VAR_PERIOD*2.118)); {行业轮动V14.1} HY_RET:=EMA((INDEXC/REF(INDEXC,5)-1)*100,5)*1.45; TRANS_MAT:=EMA((SUM((IND_RATIO>REF(IND_RATIO,5))*(REF(IND_RATIO,5)>REF(IND_RATIO,21)),21)+ SUM((IND_RATIO>REF(IND_RATIO,5))*(REF(IND_RATIO,5)>REF(IND_RATIO,34)),34))/2/ (SUM(REF(IND_RATIO,5)>REF(IND_RATIO,21),55)+1E-4),5)*1.18; {行业筛选V4.1} SECTOR_FLT:=SECTOR_STR>REF(SECTOR_STR,34)*1.25 AND CTOP_SECT AND CROSS(EMA(SECTOR_STR,5),EMA(SECTOR_STR,13)) AND SLOPE(SECTOR_STR,3)>SLOPE(SECTOR_STR,8)*1.42; {三维共振V7.1} DIF:=EMA(CLOSE,7)-EMA(CLOSE,18); DEA:=EMA(DIF,3); MACD_COND:=DIF>DEA AND DEA>REF(DEA,3)*1.05; {资金流向V6.1} BIGBUY:=SUM(IF(VOL/FINANCE(7)>=0.01 AND COUNT(VOL/FINANCE(7)>=0.008,3)=3, AMOUNT*0.85,0),3); FUNDFLOW:=(BIGBUY-BIGSELL)/FINANCE(7)*100*1.31; {情绪启动V4.1} 情绪启动:=CROSS(MARKET_SENT,1.42) AND COUNT(MARKET_SENT>1.18,3)>=2 AND CLOSE>EMA(CLOSE,233)*1.22; {终极信号V8.1} 盘后选股:=大盘过滤 AND DYNPETTM<8.5 AND PB_RATE<1.58 AND PEG_VAL<0.45 AND EVERY(CLOSE>EMA(CLOSE,55),10) AND FINANCE(30)/REF(MAX(FINANCE(30),0.01),4)>1.72 AND EVERY(VOL>MA(VOL,55)*1.42,5) AND MACD_COND AND CLOSE/EMA(CLOSE,55)>1.32 AND VOL/EMA(VOL,55)>1.62; {盘中预警V3.1} 盘中预警:CROSS(CLOSE,BOLL_UPPER) AND VOL>MA(VOL,34)*3.8 AND FUNDFLOW>REF(FUNDFLOW,1)*1.28 AND 情绪启动 AND CLOSE>HHV(HIGH,21) AND 大盘过滤; {资金流验证V13.1} LHB_DATA:=FINANCE(244)/CAPITAL*100; CAPITAL_INFLOW:=SUM(AMO,5)/SUM(AMO,21)>1.02 AND EVERY(V>REF(V,1)*1.28,5) AND (MAIN_FUND-REF(MAIN_FUND,3))/CAPITAL>0.22; {信号衰减模型V3.1} DECAY_WEIGHT:=EXP(-0.12*(BARSLAST(盘后选股))); SIGNAL_EMA:EMA(盘后选股*DECAY_WEIGHT,5); DRAWICON(盘后选股 AND 盘中预警, LOW, 1);```在代码中添加5日均量线>120日均量线作为启动确认条件;添加{资金流验证V12} MAIN_FUND:=DYNAINFO(117)/10000; LHB_DATA:=IF(FINVALUE(244)>0,FINVALUE(244)/MAIN_FUND,0); //使用FINVALUE替代 CAPITAL_INFLOW:=SUM(AMO,13)/SUM(AMO,55)>0.88 AND SUM(IF(C>O*1.02 AND C>MA(C,21),V,0),13)/SUM(V,13)>0.78 AND (AMO-REF(AMO,13))/REF(AMO,13)>0.38 AND EVERY(V>REF(V,1)*1.15,8) AND REF(MAIN_FUND,8)/CAPITAL > MAIN_FUND/CAPITAL*1.32 AND HKHOLD>REF(HKHOLD,5) AND (MAIN_FUND-REF(MAIN_FUND,8))/CAPITAL>0.15 AND LHB_DATA>0.08; {多周期共振V14} MONTH_TREND:=C>EMA(C,55)*1.15 AND EVERY(MA(C,21)>MA(C,55),13) AND SLOPE(MA(C,55),21)>0.08 AND FINANCE(4)/FINANCE(1)<0.22; WEEK_MACD:=2.8*(EMA(C,34)-EMA(C,89)-EMA(EMA(C,34)-EMA(C,89),34)); DAY_BREAK:=C>HHV(H,21) AND V>MA(V,89)*4.5 AND (C-LLV(L,21))/LLV(L,21)>0.18 AND C>MA(C,233)*1.12 AND V/CAPITAL>REF(V/CAPITAL,13)+0.045;进一步提高信号质量。

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``` {智能估值体系V14} 大盘过滤:=INDEXC>MA(INDEXC,60) AND INDEXC>MA(INDEXC,120); {新增大盘趋势过滤器} DYNPETTM:=IF(FINANCE(1)>300000000 AND FINANCE(4)>150000000, CLOSE/((FINANCE(1)/MAX(FINANCE(4),120000000)+0.000001)*0.82)* {优化系数至0.82} (1+0.18*INDUSTRYF(1013)),1000); {替换为通达信行业函数} PB_RATE:=IF(FINANCE(34)>0.88 AND CLOSE>5.5, CLOSE/((FINANCE(34)*0.88+REF(FINANCE(34),1)*0.12)*0.95+0.000001),1000); {优化权重参数} {修正PEG计算V5} PEG_VAL:=DYNPETTM/MAX(FINANCE(30)/REF(MAX(FINANCE(30),0.01),4),1.28); {调整分母系数至1.28} {分形波动率V21} VAR_PERIOD:=IF(VOLATILITY(89)<0.018,377, {替换为通达信波动率函数} IF(VOLATILITY(89)<0.04,233,89)); SLOW_LEN:=IF(VOLATILITY(89)>0.2,INT(VAR_PERIOD*1.618),CEILING(VAR_PERIOD*2.118)); {行业轮动V14} HY_RET:=EMA((INDEXC/REF(INDEXC,5)-1)*100,5)*1.42; {增强行业动量系数} TRANS_MAT:=EMA((SUM((IND_RATIO>REF(IND_RATIO,5))*(REF(IND_RATIO,5)>REF(IND_RATIO,21)),21)+ SUM((IND_RATIO>REF(IND_RATIO,5))*(REF(IND_RATIO,5)>REF(IND_RATIO,34)),34))/2/ (SUM(REF(IND_RATIO,5)>REF(IND_RATIO,21),55)+0.0001),5)*1.15; {增加过渡矩阵权重} {行业筛选V4} SECTOR_FLT:=SECTOR_STR>REF(SECTOR_STR,34)*1.22 {提升强度阈值} AND CTOP_SECT AND CROSS(EMA(SECTOR_STR,5),EMA(SECTOR_STR,13)) {缩短EMA周期} AND SLOPE(SECTOR_STR,3)>SLOPE(SECTOR_STR,8)*1.35; {增强斜率对比} {三维共振V7} DIF:=EMA(CLOSE,8)-EMA(CLOSE,21); {缩短周期提高灵敏度} DEA:=EMA(DIF,5); MACD_COND:=DIF>DEA AND DEA>REF(DEA,3); {新增趋势确认条件} {资金流向V6} BIGBUY:=SUM(IF(VOL/FINANCE(7)>=0.01 AND COUNT(VOL/FINANCE(7)>=0.008,3)=3, AMOUNT*0.82,0),3); {提高主力资金系数} FUNDFLOW:=(BIGBUY-BIGSELL)/FINANCE(7)*100*1.25; {增加资金流权重} {情绪启动V4} 情绪启动:=CROSS(MARKET_SENT,1.35) AND COUNT(MARKET_SENT>1.15,3)>=2 {提高触发阈值} AND CLOSE>EMA(CLOSE,233)*1.18; {新增趋势确认} {终极信号V8} 盘后选股:=大盘过滤 AND DYNPETTM<8.8 AND PB_RATE<1.65 {加入大盘过滤} AND PEG_VAL<0.48 {降低PEG阈值} AND EVERY(CLOSE>EMA(CLOSE,55),8) {延长趋势确认周期} AND FINANCE(30)/REF(MAX(FINANCE(30),0.01),4)>1.68 AND EVERY(VOL>MA(VOL,55)*1.35,5) {增加放量天数} AND MACD_COND AND CLOSE/EMA(CLOSE,55)>1.28 AND VOL/EMA(VOL,55)>1.55; {盘中预警V3} 盘中预警:CROSS(CLOSE,BOLL_UPPER) AND VOL>MA(VOL,34)*3.5 {新增布林带突破} AND FUNDFLOW>REF(FUNDFLOW,1)*1.25 AND 情绪启动 AND CLOSE>HHV(HIGH,21) AND 大盘过滤; {资金流验证V13} LHB_DATA:=FINANCE(244)/CAPITAL*100; {修正龙虎榜函数} CAPITAL_INFLOW:=SUM(AMO,8)/SUM(AMO,34)>0.95 {缩短统计周期} AND EVERY(V>REF(V,1)*1.25,5) AND (MAIN_FUND-REF(MAIN_FUND,5))/CAPITAL>0.18; {信号衰减模型V3} DECAY_WEIGHT:=EXP(-0.08*(BARSLAST(盘后选股))); //优化衰减系数 SIGNAL_EMA:EMA(盘后选股*DECAY_WEIGHT,8); DRAWICON(盘后选股 AND 盘中预警, LOW, 1);```你的身份是高级编程技术专家,精通各类编程语言,能对编程过程中的各类问题进行分析和解答。我的问题是【使用Python构建回测系统测试选股代码的有效性?用2018-2024年全A股周期回测验证此代码选股逻辑的准确性和胜率,评估月胜率达到多少?评估有效信号准确率达到多少?】,同时此代码还有什么可提升的空间,提出可行性的优化建议和方案,如何选到选股胜率达到月胜率提高至75%以上,有效信号准确率95%以上,选到资金持续流入,股票市场情绪启动,盘中异动启动主升浪的股票,及日线盘中预警选股和盘后选股,并帮我调整参数并找到最佳选股参数计算关系和信号触发条件。请帮我检查并改正错误点补全正确代码,,并替换为通达信支持的函数,生成调整优化后通达信完整代码。

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``` {——智能估值体系V7——} DYNPETTM:=IF(FINANCE(33)>2.5E8 AND FINANCE(1)>1.2E8, CLOSE/MAX(FINANCE(33)/FINANCE(1),0.82), 999); PB_RATE:=IF(FINANCE(34)>0.68 AND CLOSE>4.2, CLOSE/(FINANCE(34)*0.78 + FINANCE(34,1)*0.22), 999); PEG_VAL:=DYNPETTM/MAX((FINANCE(54)/FINANCE(54,1)*100-100),0.68); {——分形波动率V7——} VOL_REGIME:=DMA(STD(C,89)/MA(C,89)*SQRT(144),34); VAR_PERIOD:=IF(VOL_REGIME<0.012,233, IF(VOL_REGIME<0.028,144, IF(VOL_REGIME<0.055,55,34))); FAST_LEN:=BARSLAST(CROSS(VOL_REGIME,0.025))+21; SLOW_LEN:=CEILING(VAR_PERIOD*(1.618-IF(VOL_REGIME>0.15,0.22,0.06))); {——七维情绪引擎V8——} MARKET_SENTI:=EMA(ADVANCE/DECLINE,13)*0.42 + EMA(AMOUNT/REF(AMOUNT,5),13)*0.32 + EMA((HSL-REF(HSL,8))/REF(HSL,8),21)*0.18 + EMA(L2_AMO(3,2)/FINVALUE(0),8)*0.08 + 0.18*(IF(BKVISITOR(1)>REF(BKVISITOR(8),8),1,0)); HOT_INDEX:=SMA(SUM(IF(C>DYNAINFO(58)*1.028,V/FINVALUE(7)*100,0),21)/ MA(V,21)*2.28,13,1)*IF(CAPITAL>8E9,1.22,1); {——资金流验证V7——} CAPITAL_INFLOW:=SUM(L2_AMO(3,2),13)/SUM(L2_AMO(3,2),55)>0.92 AND SUM(IF(C>O*1.028 AND C>MA(C,21),V,0),13)/SUM(V,13)>0.82 AND (AMOUNT-REF(AMOUNT,13))/REF(AMOUNT,13)>0.42 AND EVERY(L2_BIGORDER(2,0)>0.72,8) AND CHIPSDIFF(8,21)>0.32 AND HKHOLDDIFF(5)>0.08; {——月线战略层V7——} MONTH_MA:=EMA(C,55); MONTH_TREND:=C>MONTH_MA*1.18 AND EVERY(MA(C,21)>MA(C,55),13) AND SLOPE(MA(C,55),21)>0.12 AND FINVALUE(4)/FINVALUE(1)<0.22 AND QUARTERLY.MA5>QUARTERLY.MA20; {——周线战役层V7——} WEEK_DIF:=EMA(C,34) - EMA(C,89); WEEK_DEA:=EMA(WEEK_DIF,34); WEEK_MACD:=2.888*(WEEK_DIF - WEEK_DEA); WEEK_VOL:=EMA(V,21)>EMA(V,89)*4.2 AND V>REF(MA(V,21),1)*2.28 AND VOL>REF(HHV(V,21),1)*1.92; {——日线战术层V7——} DAY_BREAK:=C>HHV(H,21) AND V>MA(V,89)*6.8 AND (C-LLV(L,21))/LLV(L,21)>0.28 AND C>MA(C,233)*1.18 AND (HSL-REF(HSL,13))>5.2; {——分时预警模块V5——} MIN30_BREAK:=#MIN30.C>#MIN30.HHV(H,89) AND #MIN30.V>#MIN30.MA(V,89)*7.2 AND #MIN30.C>EMA(#MIN30.C,233)*1.12 AND #MIN30.C/REF(#MIN30.C,1)>1.068 AND #MIN30.V>EMA(#MIN30.V,89)*3.18 AND CROSS(#MIN30.MACD.DIF,#MIN30.MACD.DEA); {——信号合成模块V8——} FINAL_SIGNAL:=MONTH_TREND*0.32 + WEEK_MACD>REF(WEEK_MACD,5)*1.88*0.28 + DAY_BREAK*0.22 + MIN30_BREAK*0.18 + (MARKET_SENTI>4.8)*0.15 + (HOT_INDEX>3.8)*0.12 + (PEG_VAL<0.48)*0.18 + (DYNPETTM<8.2)*0.15 + CAPITAL_INFLOW*0.28 >= 0.92 AND BARSLAST(FINAL_SIGNAL)>89; {——智能止损模块V5——} STOP_LOSS:=C<EMA(L,21)*0.92 OR (V>MA(V,89)*8.2 AND C<OPEN*0.968) OR VOL_REGIME>0.15 AND C<EMA(C,55)*0.88;```你的身份是高级编程技术专家,精通各类编程语言,能对编程过程中的各类问题进行分析和解答。我的问题是【我编辑通达信选股代码,你如何深度理解此代码能否选到资金持续流入,股票市场情绪启动,盘中异动启动主升浪的股票,及日线盘中预警选股和盘后选股。用2018-2024年全A股周期回测验证此代码选股逻辑的准确性和胜率,评估月胜率达到多少?评估有效信号准确率达到多少?,同时此代码还有什么可提升的空间,提出可行性的优化建议和方案,使选股代码月胜率提高至80%以上,有效信号准确率95%以上,优化选股逻辑和参数计算关系和信号触发条件。修正后要求选股胜率达到月胜率提高至80%以上,有效信号准确率95%以上,选到资金持续流入,股票市场情绪启动,盘中异动启动主升浪的股票,及日线盘中预警选股和盘后选股。请帮我检查并改正错误点补全正确代码,原有选股逻辑完整保留。替换为通达信支持的函数,生成优化后完整代码。

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``` {智能估值体系V13} DYNPETTM:=IF(FINANCE(1)>300000000 AND FINANCE(4)>150000000, CLOSE/((FINANCE(1)/MAX(FINANCE(4),120000000)+0.000001)*0.85)* (1+0.15*INDUSTRY_COMPARE('PE')),1000); PB_RATE:=IF(FINANCE(34)>0.85 AND CLOSE>5.5, CLOSE/((FINANCE(34)*0.85+REF(FINANCE(34),1)*0.15)*0.92+0.000001),1000); {修正PEG计算V4} PEG_VAL:=DYNPETTM/MAX(FINANCE(30)/REF(MAX(FINANCE(30),0.01),4),1.35); {分形波动率V20} VOL_REG:=DMA(STD(CLOSE,IF(MARKET_STATUS('BULL'),144,233))/MA(CLOSE,233)*SQRT(233),34)*1.12; VAR_PERIOD:=IF(VOL_REG<0.018,377,IF(VOL_REG<0.04,233,89)); FAST_LEN:=BARSLAST(CROSS(VOL_REG,0.035))+18; SLOW_LEN:=IF(VOL_REG>0.2,INT(VAR_PERIOD*1.618),CEILING(VAR_PERIOD*2.118)); {行业轮动V13} HY_RET:=EMA((INDEXC/REF(INDEXC,5)-1)*100,5)*1.35; IND_RATIO:=EMA(INDEXC/MAX(INDEXO,0.01),11); TRANS_MAT:=EMA((SUM((IND_RATIO>REF(IND_RATIO,5))*(REF(IND_RATIO,5)>REF(IND_RATIO,21)),21)+ SUM((IND_RATIO>REF(IND_RATIO,5))*(REF(IND_RATIO,5)>REF(IND_RATIO,34)),34))/2/ (SUM(REF(IND_RATIO,5)>REF(IND_RATIO,21),55)+0.0001),5); SECTOR_STR:=TRANS_MAT*EMA(HY_RET,8)*0.72+REF(TRANS_MAT,5)*EMA(HY_RET,13)*0.28; {行业筛选V3} CTOP_SECT:=COUNT(ABS(SECTOR_STR)>=ABS(REF(SECTOR_STR,1)),250)<=2; SECTOR_FLT:=SECTOR_STR>REF(SECTOR_STR,34)*1.18 AND CTOP_SECT AND CROSS(EMA(SECTOR_STR,8),EMA(SECTOR_STR,21)) AND SLOPE(SECTOR_STR,3)>SLOPE(SECTOR_STR,13)*1.2 AND FINANCE(42)/100000000>2; {十维情绪V24} MARKET_SENT:=EMA(ADVANCE/DECLINE,21)*0.68+ EMA(AMOUNT/REF(AMOUNT,5),21)*0.42+ EMA((VOL/FINANCE(7)-REF(VOL/FINANCE(7),8))/REF(VOL/FINANCE(7),8),34)*0.35+ EMA(REF(FINANCE(20),3)/FINANCE(7),13)*0.3; {三维共振V6} DIF:=EMA(CLOSE,10)-EMA(CLOSE,24); DEA:=EMA(DIF,7); {资金流向V5} BIGBUY:=SUM(IF(VOL/FINANCE(7)>=0.01 AND COUNT(VOL/FINANCE(7)>=0.008,3)=3, AMOUNT*0.75,0),3); BIGSELL:=SUM(IF(VOL/FINANCE(7)>0.01 AND COUNT(VOL/FINANCE(7)>0.008,3)=3, AMOUNT*0.25,0),3); FUNDFLOW:=(BIGBUY-BIGSELL)/FINANCE(7)*100; 资金流信号:=EMA(FUNDFLOW,5)>0.75 AND MA(BIGBUY,3)/MA(BIGSELL,3)>1.35 AND EVERY(FUNDFLOW>REF(FUNDFLOW,1),3); {情绪启动V3} 情绪启动:=CROSS(MARKET_SENT,1.25) AND COUNT(MARKET_SENT>1.05,3)>=2; {技术形态V4} 技术形态:=CLOSE>MAX(EMA(CLOSE,55),EMA(CLOSE,233)) AND DIF>EMA(DEA,3); {多周期趋势V3} MONTH_TREND:=CLOSE>EMA(CLOSE,34)*1.22 AND SLOPE(MA(CLOSE,55),13)>0.15; DAY_BREAK:=CLOSE>HHV(HIGH,13) AND VOL>MA(VOL,55)*2.8 AND VOL>REF(VOL,1)*1.8; {终极信号V7} 盘后选股:=DYNPETTM<10.5 AND PB_RATE<1.8 AND PEG_VAL<0.58 AND SECTOR_FLT AND EVERY(CLOSE>EMA(CLOSE,34),5) AND FINANCE(30)/REF(MAX(FINANCE(30),0.01),4)>1.55 AND EVERY(VOL>MA(VOL,34)*1.25,3) AND 资金流信号 AND 情绪启动 AND 技术形态 AND CLOSE/EMA(CLOSE,34)>1.22 AND VOL/EMA(VOL,34)>1.35; {资金流验证V12} MAIN_FUND:=DYNAINFO(117)/10000; LHB_DATA:=IF(FINVALUE(244)>0,FINVALUE(244)/MAIN_FUND,0); //使用FINVALUE替代 CAPITAL_INFLOW:=SUM(AMO,13)/SUM(AMO,55)>0.88 AND SUM(IF(C>O*1.02 AND C>MA(C,21),V,0),13)/SUM(V,13)>0.78 AND (AMO-REF(AMO,13))/REF(AMO,13)>0.38 AND EVERY(V>REF(V,1)*1.15,8) AND REF(MAIN_FUND,8)/CAPITAL > MAIN_FUND/CAPITAL*1.32 AND HKHOLD>REF(HKHOLD,5) AND (MAIN_FUND-REF(MAIN_FUND,8))/CAPITAL>0.15 AND LHB_DATA>0.08; DRAWICON(盘后选股 AND DAY_BREAK AND MONTH_TREND, LOW, 1);```你的身份是高级编程技术专家,精通各类编程语言,能对编程过程中的各类问题进行分析和解答。我的问题是【我编辑通达信选股代码,你如何深度理解此代码能用2018-2024年全A股周期回测验证此代码选股逻辑的准确性和胜率,评估月胜率达到多少?评估有效信号准确率达到多少?