Dependencies

• python 3.5

• pandas 0.22.0

• numpy 1.14.3

• pickle

• sklearn 0.19.1

• databox

Example

Preprocessing data

First create a databox object with original factors and market info. More can be found in project databox

from databox import databox
db=databox()\
    .load_indestry(ind)\
    .load_indexWeight(ind_weight)\
    .load_suspend(sus)\
    .load_adjPrice(price)\
    .set_lag(freq='d',day_lag=1)
for fac_name,fac_df in factors_dictionary.items():
    db.add_factor(fac_name,fac_df)
db.align_data()\
  .factor_ind_neutral()\
  .factor_size_neutral()

Then custmize your data for model training

sp=sample_pipeline()\
    .set_fw_return_n(1)\
    .set_sample_n(1)\
    .factor_rank()\
    .factor_zscore()\
    .fw_return_ind_neutral()\
    .fw_return_rank()\
    .fw_return_I(thresh=2000)

Note all returns are multiplied by 100 for better modeling.

Now create sample as

X_train,y_train=sp.train_set(db)
X_test,y_test=sp.test_set(db)
X_test_all=sp.test_X(db)

Modeling

Classification Method

from sklearn.ensemble import RandomForestClassifier
clf=RandomForestClassifier()
tn,tt,ml=clf_model(clf,X_train,y_train,X_test,y_test)

Where y can be 0/1 or float and result tn (train) and tt (test) would be different depending on this. If clf is a tree based model, ml would be feature importance. If clf is a linear model, ml would be coeffient.

We can also creat a model by combining several models.

from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
clf1=RandomForestClassifier()
clf2=LogisticRegression()
clf3=SVC()
from multi_factor_model import combine_clf_models
CLF=combine_clf_models()\
    .add_clf('rf',clf1)\
    .add_clf('lr',clf2)\
    .add_clf('svc',clf3,weight=2)#default weight is 1
tn,tt,ml=clf_model(CLF,X_train,y_train,X_test,y_test)

Regression Method Same as Classification method with reg_model as the replacement of clf_model and combine_reg_models as that of combine_clf_models

Combined Factor

import pandas as pd
value=CLF.predict_proba(X_test_all)
factor=pd.Series(value[:,1],index=X_test_all.index)