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社区首页 >专栏 >Optiver波动率预测大赛系列解读二:LightGBM模型及特征工程

Optiver波动率预测大赛系列解读二:LightGBM模型及特征工程

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量化投资与机器学习微信公众号
发布2021-10-22 11:21:21
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发布2021-10-22 11:21:21
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文章被收录于专栏:量化投资与机器学习

量化投资与机器学习微信公众号,是业内垂直于量化投资、对冲基金、Fintech、人工智能、大数据等领域的主流自媒体。公众号拥有来自公募、私募、券商、期货、银行、保险、高校等行业20W+关注者,连续2年被腾讯云+社区评选为“年度最佳作者”。 前言 Optiver波动率预测大赛于上个月27号截止提交,比赛终于告一段落,等待着明年1月份的最终比赛结果。Kaggle上,由财大气粗的对冲基金大佬主办的金融交易类预测大赛,总能吸引大量的人气。在过去3个月的比赛中,也诞生了很多优秀的开源代码,各路神仙应用各种模型算法,在竞争激烈的榜单你追我赶。 关于这个比赛,网络上陆陆续续也有很多参赛经验的分享。但为了充分吸收大神们的精髓,公众号还是决定从0到1解读各种不同类型的开源比赛代码,方便小伙伴们学习归纳,并应用到实际研究中去。本系列大概安排内容如下:

上一篇文章 中,我们对本次比赛要解决的问题有了一个初步的认识,简单来说就是:应用前10分钟的Book和Trade数据预测下个10分钟的已实现波动率。 特征构建 基于对金融市场的理解,对于Book数据构建以下几类特征:

  • 价差(price_spread): 买卖的价差(bid-ask spread)。价差越大,意味着流动性越低,也就意味着潜在的高波动。
  • 订单量(volume): 买量卖量之和。订单量越低,意味着流动性越低,也就意味着潜在的高波动。
  • 订单倾向(volume_imbalance): 买量卖量之差。差值越大,说明买卖力量越失衡,也就意味着潜在的高波动。

具体的特征计算逻辑参见下文代码 模型相关 在模型层面,对股票的id编码(用target均值作为编码),也作为特征之一。这样构建了一个适用于所有股票的模型,而不是单个股票对应一个模型。 目标函数RMSPE:

\sqrt{\frac{1}{n}\sum^{n}_{i=1}((y_i-\hat{y_i})/y_i)^2}

代码解读 # 导入相关工具包 import numpy as np import pandas as pd import matplotlib.pyplot as plt import os import glob # 设置数据所在文件夹路径 data_dir = '../input/optiver-realized-volatility-prediction/' 预处理函数 由于波动率是基于wap的log return计算得出,所以首先要计算wap价格,然后根据wap价格计算对数收益率及已实现波动率。 # 计算wap价格 def calc_wap(df): wap = (df['bid_price1'] * df['ask_size1'] + df['ask_price1'] * df['bid_size1'])/(df['bid_size1'] + df['ask_size1']) return wap def calc_wap2(df): wap = (df['bid_price2'] * df['ask_size2'] + df['ask_price2'] * df['bid_size2'])/(df['bid_size2'] + df['ask_size2']) return wap # 计算对数收益率 def log_return(list_stock_prices): return np.log(list_stock_prices).diff() # 计算已实现波动率 def realized_volatility(series): return np.sqrt(np.sum(series**2)) # 其他函数 def count_unique(series): return len(np.unique(series)) 订单簿数据(Book)特征计算函数 Book样例数据如下:

以下是特征处理计算的代码,关于以下代码,有几点需要注意:

  • "log_return"/"log_return2":计算的是每个time_id内相邻两个快照的收益率,没两个快照之间的时间间隔可能不一致,如示例数据中的秒数间隔数为1、4、1、1。
  • "wap_balance"/"price_spread"/"volume_imbalance"等等(代码21-27行所示):计算的是这个time_id内这个特征在这个时间段内的平均值。如果自己尝试时,也可以增加其他统计维度的值,如最大最小,标准差等。
  • 除了每个time_id整个10min时间窗口构建的特征,原作者还构建了每个time_id后300s的特征,见以下代码37-45行。

def preprocessor_book(file_path): df = pd.read_parquet(file_path) #calculate return etc df['wap'] = calc_wap(df) df['log_return'] = df.groupby('time_id')['wap'].apply(log_return) df['wap2'] = calc_wap2(df) df['log_return2'] = df.groupby('time_id')['wap2'].apply(log_return) df['wap_balance'] = abs(df['wap'] - df['wap2']) df['price_spread'] = (df['ask_price1'] - df['bid_price1']) / ((df['ask_price1'] + df['bid_price1'])/2) df['bid_spread'] = df['bid_price1'] - df['bid_price2'] df['ask_spread'] = df['ask_price1'] - df['ask_price2'] df['total_volume'] = (df['ask_size1'] + df['ask_size2']) + (df['bid_size1'] + df['bid_size2']) df['volume_imbalance'] = abs((df['ask_size1'] + df['ask_size2']) - (df['bid_size1'] + df['bid_size2'])) #dict for aggregate create_feature_dict = { 'log_return':[realized_volatility], 'log_return2':[realized_volatility], 'wap_balance':[np.mean], 'price_spread':[np.mean], 'bid_spread':[np.mean], 'ask_spread':[np.mean], 'volume_imbalance':[np.mean], 'total_volume':[np.mean], 'wap':[np.mean], } #####groupby / all seconds df_feature = pd.DataFrame(df.groupby(['time_id']).agg(create_feature_dict)).reset_index() df_feature.columns = ['_'.join(col) for col in df_feature.columns] #time_id is changed to time_id_ ######groupby / last XX seconds last_seconds = [300] for second in last_seconds: second = 600 - second df_feature_sec = pd.DataFrame(df.query(f'seconds_in_bucket >= {second}').groupby(['time_id']).agg(create_feature_dict)).reset_index() df_feature_sec.columns = ['_'.join(col) for col in df_feature_sec.columns] #time_id is changed to time_id_ df_feature_sec = df_feature_sec.add_suffix('_' + str(second)) df_feature = pd.merge(df_feature,df_feature_sec,how='left',left_on='time_id_',right_on=f'time_id__{second}') df_feature = df_feature.drop([f'time_id__{second}'],axis=1) #create row_id stock_id = file_path.split('=')[1] df_feature['row_id'] = df_feature['time_id_'].apply(lambda x:f'{stock_id}-{x}') df_feature = df_feature.drop(['time_id_'],axis=1) return df_feature 以股票0计算相关特征作为示例,一个有18个特征: %%time file_path = data_dir + "book_train.parquet/stock_id=0" preprocessor_book(file_path)

成交数据(Trade)特征计算函数 Trade样例数据如下:

Trade数据字段比较少,特征也比较少,作者主要构建了以下特征(见代码7-10行):

  • 某个time_id内的总成交量
  • 某个time_id内的成交笔数
  • 某个time_id内平均订单量
  • 计算成交价计算的已实现波动率
  • 同样也计算了每个time_id后300秒的以上4个特征

def preprocessor_trade(file_path): df = pd.read_parquet(file_path) df['log_return'] = df.groupby('time_id')['price'].apply(log_return) aggregate_dictionary = { 'log_return':[realized_volatility], 'seconds_in_bucket':[count_unique], 'size':[np.sum], 'order_count':[np.mean], } df_feature = df.groupby('time_id').agg(aggregate_dictionary) df_feature = df_feature.reset_index() df_feature.columns = ['_'.join(col) for col in df_feature.columns] ######groupby / last XX seconds last_seconds = [300] for second in last_seconds: second = 600 - second df_feature_sec = df.query(f'seconds_in_bucket >= {second}').groupby('time_id').agg(aggregate_dictionary) df_feature_sec = df_feature_sec.reset_index() df_feature_sec.columns = ['_'.join(col) for col in df_feature_sec.columns] df_feature_sec = df_feature_sec.add_suffix('_' + str(second)) df_feature = pd.merge(df_feature,df_feature_sec,how='left',left_on='time_id_',right_on=f'time_id__{second}') df_feature = df_feature.drop([f'time_id__{second}'],axis=1) df_feature = df_feature.add_prefix('trade_') stock_id = file_path.split('=')[1] df_feature['row_id'] = df_feature['trade_time_id_'].apply(lambda x:f'{stock_id}-{x}') df_feature = df_feature.drop(['trade_time_id_'],axis=1) return df_feature 以股票0计算相关特征作为示例,算上股票代码本身,一个有8个特征: file_path = data_dir + "trade_train.parquet/stock_id=0" preprocessor_trade(file_path)

计算所有股票的特征 作者使用并行计算,并且把book和trade的特征合并在一个dataframe,函数的输入是stock_id的列表,代码运行时会根据stock_id去文件里读取相关股票的数据。 def preprocessor(list_stock_ids, is_train = True): from joblib import Parallel, delayed # parallel computing to save time df = pd.DataFrame() def for_joblib(stock_id): if is_train: file_path_book = data_dir + "book_train.parquet/stock_id=" + str(stock_id) file_path_trade = data_dir + "trade_train.parquet/stock_id=" + str(stock_id) else: file_path_book = data_dir + "book_test.parquet/stock_id=" + str(stock_id) file_path_trade = data_dir + "trade_test.parquet/stock_id=" + str(stock_id) df_tmp = pd.merge(preprocessor_book(file_path_book),preprocessor_trade(file_path_trade),on='row_id',how='left') return pd.concat([df,df_tmp]) df = Parallel(n_jobs=-1, verbose=1)( delayed(for_joblib)(stock_id) for stock_id in list_stock_ids ) df = pd.concat(df,ignore_index = True) return df 构建训练/测试数据集 首先,根据train.csv内的stock_id计算特征,得到df_train,再通过row_id合并df_train与train,把对应的训练数据过滤出来。同样的操作也应该在测试集上。

# 训练集 train = pd.read_csv(data_dir + 'train.csv') train_ids = train.stock_id.unique() df_train = preprocessor(list_stock_ids= train_ids, is_train = True) train['row_id'] = train['stock_id'].astype(str) + '-' + train['time_id'].astype(str) train = train[['row_id','target']] df_train = train.merge(df_train, on = ['row_id'], how = 'left') # 测试集 test = pd.read_csv(data_dir + 'test.csv') test_ids = test.stock_id.unique() df_test = preprocessor(list_stock_ids= test_ids, is_train = False) df_test = test.merge(df_test, on = ['row_id'], how = 'left') 上文说过,本文构建了一个适用于所有股票的模型,而不是单个股票对应一个模型。对于股票编码的方法,并不是传统的one-hot,作者用股票在训练集内target的均值作为编码(见代码第5行): from sklearn.model_selection import KFold #stock_id target encoding df_train['stock_id'] = df_train['row_id'].apply(lambda x:x.split('-')[0]) df_test['stock_id'] = df_test['row_id'].apply(lambda x:x.split('-')[0]) stock_id_target_mean = df_train.groupby('stock_id')['target'].mean() df_test['stock_id_target_enc'] = df_test['stock_id'].map(stock_id_target_mean) # test_set #training tmp = np.repeat(np.nan, df_train.shape[0]) kf = KFold(n_splits = 10, shuffle=True,random_state = 19911109) for idx_1, idx_2 in kf.split(df_train): target_mean = df_train.iloc[idx_1].groupby('stock_id')['target'].mean() tmp[idx_2] = df_train['stock_id'].iloc[idx_2].map(target_mean) df_train['stock_id_target_enc'] = tmp LightGBM LightGBM模型本身是为了解决XGBoost在训练时间空间上的缺陷提出来的高效的GBDT模型,关于LightGBM模型的介绍可以参考网上丰富的资料。在这里,我们不花篇幅讲解LightGBM的原理,做一回调包侠,直接引入lightgbm模块: import lightgbm as lgbm # 数据最后准备 df_train['stock_id'] = df_train['stock_id'].astype(int) df_test['stock_id'] = df_test['stock_id'].astype(int) X = df_train.drop(['row_id','target'],axis=1) y = df_train['target'] # 定义目标函数 def rmspe(y_true, y_pred): return (np.sqrt(np.mean(np.square((y_true - y_pred) / y_true)))) def feval_RMSPE(preds, lgbm_train): labels = lgbm_train.get_label() return 'RMSPE', round(rmspe(y_true = labels, y_pred = preds),5), False # 参数设置 params = { "objective": "rmse", "metric": "rmse", "boosting_type": "gbdt", 'early_stopping_rounds': 30, 'learning_rate': 0.01, 'lambda_l1': 1, 'lambda_l2': 1, 'feature_fraction': 0.8, 'bagging_fraction': 0.8, } # 训练验证集划分 kf = KFold(n_splits=5, random_state=19901028, shuffle=True) oof = pd.DataFrame() # out-of-fold result models = [] # models scores = 0.0 # validation score gain_importance_list = [] split_importance_list = [] 在上面的代码中,把数据集分成了5个fold,每个fold训练的模型都保留下来(代码第31行),最终的scores是5个模型的平均值。 # 模型训练 for fold, (trn_idx, val_idx) in enumerate(kf.split(X, y)): print("Fold :", fold+1) # create dataset X_train, y_train = X.loc[trn_idx], y[trn_idx] X_valid, y_valid = X.loc[val_idx], y[val_idx] #RMSPE weight weights = 1/np.square(y_train) lgbm_train = lgbm.Dataset(X_train,y_train,weight = weights) weights = 1/np.square(y_valid) lgbm_valid = lgbm.Dataset(X_valid,y_valid,reference = lgbm_train,weight = weights) # model model = lgbm.train(params=params, train_set=lgbm_train, valid_sets=[lgbm_train, lgbm_valid], num_boost_round=5000, feval=feval_RMSPE, verbose_eval=100, categorical_feature = ['stock_id'] ) # validation y_pred = model.predict(X_valid, num_iteration=model.best_iteration) RMSPE = round(rmspe(y_true = y_valid, y_pred = y_pred),3) print(f'Performance of the prediction: , RMSPE: {RMSPE}') #keep scores and models scores += RMSPE / 5 models.append(model) print("*" * 100) # 最终的训练集的scores为0.2344 在测试集上预测,并进行提交 y_pred = df_test[['row_id']] X_test = df_test.drop(['time_id', 'row_id'], axis = 1) target = np.zeros(len(X_test)) #light gbm models for model in models: pred = model.predict(X_test[X_valid.columns], num_iteration=model.best_iteration) target += pred / len(models) y_pred = y_pred.assign(target = target) y_pred.to_csv('submission.csv',index = False) 总结 这个方案给我们带来的启示由以下几点:

  • 使用训练集target均值对测试集股票id进行编码;
  • kfold中每个fold训练一个模型,最终测试集是多个模型综合的结果(LightGBM本身就是一种集成学习);
  • 在构建特征时,分时间段构建,比如作者构建了全部600秒及后300秒的特征;
  • 后续改进方向上,可以构建更多不同时间跨度的特征,计算这些特征不同的统计值。

下一篇,在本方案的基础上又有了质的提升。

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原始发表:2021-10-15,如有侵权请联系 cloudcommunity@tencent.com 删除

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