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:
在 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:
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:
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:
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):
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:
下一篇,在本方案的基础上又有了质的提升。