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ml_model.py
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ml_model.py
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import pandas as pd
import numpy as np
import traceback
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.svm import SVR
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn.linear_model import Ridge
from sklearn.linear_model import LinearRegression
from sklearn.feature_selection import RFE
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import AdaBoostRegressor
from sklearn.model_selection import TimeSeriesSplit, GridSearchCV,RandomizedSearchCV
from xgboost import XGBRegressor
from lightgbm import LGBMRegressor
import time
import os
import errno
from multiprocessing import cpu_count
n_cpus = cpu_count() - 1
def prepare_rolling_train(df,features_column,label_column,date_column,unique_datetime,testing_windows,first_trade_date_index, max_rolling_window_index,current_index):
if current_index <=max_rolling_window_index:
train=df[(df[date_column] >= unique_datetime[0]) \
& (df[date_column] < unique_datetime[current_index-testing_windows])]
else:
train=df[(df[date_column] >= unique_datetime[current_index-max_rolling_window_index]) \
& (df[date_column] < unique_datetime[current_index-testing_windows])]
X_train=train[features_column]
y_train=train[label_column]
return X_train,y_train
def prepare_rolling_test(df,features_column,label_column,date_column,unique_datetime,testing_windows,fist_trade_date_index, current_index):
test=df[(df[date_column] >= unique_datetime[current_index-testing_windows]) \
& (df[date_column] < unique_datetime[current_index])]
X_test=test[features_column]
y_test=test[label_column]
return X_test,y_test
def prepare_trade_data(df,features_column,label_column,date_column,tic_column,unique_datetime,testing_windows,fist_trade_date_index, current_index):
trade = df[df[date_column] == unique_datetime[current_index]]
X_trade = trade[features_column]
y_trade = trade[label_column]
trade_tic = trade[tic_column].values
return X_trade,y_trade,trade_tic
def train_linear_regression(X_train,y_train):
lr_regressor = LinearRegression()
model = lr_regressor.fit(X_train, y_train)
return model
def train_recursive_feature_elimination(X_train,y_train):
lr_regressor = LinearRegression(random_state = 42)
model = RFE(lr_regressor)
return model
def train_lasso(X_train, y_train):
# lasso_regressor = Lasso()
# model = lasso_regressor.fit(X_train, y_train)
lasso = Lasso(random_state = 42)
# scoring_method = 'r2'
# scoring_method = 'explained_variance'
# scoring_method = 'neg_mean_absolute_error'
scoring_method = 'neg_mean_squared_error'
#scoring_method = 'neg_mean_squared_log_error'
parameters = {'alpha': [1e-15, 1e-10, 1e-8, 1e-4, 1e-3, 1e-2, 1, 5, 10, 20]}
# my_cv_lasso = TimeSeriesSplit(n_splits=3).split(X_train_advanced)
lasso_regressor = GridSearchCV(lasso, parameters, scoring=scoring_method, cv=3)
lasso_regressor.fit(X_train, y_train)
model = lasso_regressor.best_estimator_
return model
def train_ridge(X_train, y_train):
# lasso_regressor = Lasso()
# model = lasso_regressor.fit(X_train, y_train)
ridge = Ridge(random_state = 42)
# scoring_method = 'r2'
# scoring_method = 'explained_variance'
# scoring_method = 'neg_mean_absolute_error'
scoring_method = 'neg_mean_squared_error'
#scoring_method = 'neg_mean_squared_log_error'
parameters = {'alpha': [1e-15, 1e-10, 1e-8, 1e-4, 1e-3, 1e-2, 1, 5, 10, 20]}
# my_cv_lasso = TimeSeriesSplit(n_splits=3).split(X_train_advanced)
ridge_regressor = GridSearchCV(ridge, parameters, scoring=scoring_method, cv=3)
ridge_regressor.fit(X_train, y_train)
model = ridge_regressor.best_estimator_
return model
def train_random_forest(X_train, y_train):
random_grid = {
#'max_depth': [10, 20, 40, 80, 100, None],
'max_features': ['sqrt'],
'min_samples_leaf': [0.05,0.1,0.2],
'min_samples_split': np.linspace(0.1, 1, 10, endpoint=True),
'n_estimators': [75,100,200]}
# scoring_method = 'r2'
# scoring_method = 'explained_variance'
# scoring_method = 'neg_mean_absolute_error'
scoring_method = 'neg_mean_squared_error'
#scoring_method = 'neg_mean_squared_log_error'
n_models = 1
for key, val in random_grid.items():
n_models *= len(val)
n_jobs_per_model = min(max(1, n_cpus//n_models), n_cpus)
# my_cv_rf = TimeSeriesSplit(n_splits=5).split(X_train_rf)
rf = RandomForestRegressor(random_state=42, n_jobs= n_jobs_per_model)
#RandomizedSearchCV
#randomforest_regressor = RandomizedSearchCV(estimator=rf,
# param_distributions=random_grid,
# n_iter = 100,
# cv=3,
# n_jobs=-1,
# scoring=scoring_method,
# verbose=0)
#GridSearchCV
randomforest_regressor = GridSearchCV(estimator=rf,
param_grid=random_grid,
cv=3,
n_jobs=n_cpus // n_jobs_per_model,
scoring=scoring_method,
verbose=0)
randomforest_regressor.fit(X_train, y_train)
#print(randomforest_regressor.best_params_ )
model = randomforest_regressor.best_estimator_
'''
randomforest_regressor = RandomForestRegressor(random_state = 42,n_estimators = 400, max_features='auto')
#randomforest_regressor = RandomForestRegressor(random_state = 42,n_estimators = 300)
model = randomforest_regressor.fit(X_train, y_train)
'''
return model
def train_svm(X_train, y_train):
svr = SVR(kernel = 'rbf')
param_grid_svm = {'C':[0.001, 0.1, 1],'gamma': [1e-7,0.1]}
#param_grid_svm = {'kernel': ('linear', 'rbf','poly'), 'C':[0.001, 0.01, 0.1, 1, 10],'gamma': [1e-7, 1e-4,0.001,0.1],'epsilon':[0.1,0.2,0.5,0.3]}
# scoring_method = 'r2'
# scoring_method = 'explained_variance'
# scoring_method = 'neg_mean_absolute_error'
scoring_method = 'neg_mean_squared_error'
#scoring_method = 'neg_mean_squared_log_error'
svm_regressor = GridSearchCV(estimator=svr, param_grid =param_grid_svm, cv=3, n_jobs=-1, scoring=scoring_method, verbose=0)
svm_regressor.fit(X_train, y_train)
model = svm_regressor.best_estimator_
#estimator = svm_regressor.best_estimator_
#selector = RFE(estimator, 5, step=1)
#model = selector.fit(X, y)
return model
def train_lightgbm(X_train, y_train):
# model = gbm.fit(X_train, y_train)
param_grid_gbm = {'learning_rate': [0.1, 0.01, 0.001], 'n_estimators': [100, 250, 500,1000]}
n_models = 1
for key, val in param_grid_gbm.items():
n_models *= len(val)
n_jobs_per_model = min(max(1, n_cpus//n_models), n_cpus)
lightgbm = LGBMRegressor(random_state = 42, n_jobs=n_jobs_per_model)
# scoring_method = 'r2'
# scoring_method = 'explained_variance'
# scoring_method = 'neg_mean_absolute_error'
scoring_method = 'neg_mean_squared_error'
#scoring_method = 'neg_mean_squared_log_error'
gbm_regressor = GridSearchCV(estimator=lightgbm, param_grid=param_grid_gbm,
cv=3, n_jobs=n_cpus // n_jobs_per_model, scoring=scoring_method, verbose=0)
gbm_regressor.fit(X_train, y_train)
model = gbm_regressor.best_estimator_
'''
gbm_regressor = GradientBoostingRegressor()
model = gbm_regressor.fit(X_train, y_train)
'''
return model
def train_xgb(X_train, y_train):
xgb = XGBRegressor(random_state = 42, n_jobs=10)
param_grid_gbm = {'learning_rate': [0.1, 0.01, 0.001], 'n_estimators': [100, 250, 500,1000]}
n_models = 1
for key, val in param_grid_gbm.items():
n_models *= len(val)
n_jobs_per_model = min(max(1, n_cpus//n_models), n_cpus)
xgb = XGBRegressor(random_state = 42, n_jobs=n_jobs_per_model)
# scoring_method = 'r2'
# scoring_method = 'explained_variance'
# scoring_method = 'neg_mean_absolute_error'
scoring_method = 'neg_mean_squared_error'
#scoring_method = 'neg_mean_squared_log_error'
xgb_regressor = GridSearchCV(estimator=xgb, param_grid=param_grid_gbm,
cv=3, n_jobs=n_cpus // n_jobs_per_model, scoring=scoring_method, verbose=0)
xgb_regressor.fit(X_train, y_train)
model = xgb_regressor.best_estimator_
'''
gbm_regressor = GradientBoostingRegressor()
model = gbm_regressor.fit(X_train, y_train)
'''
return model
def train_ada(X_train, y_train):
ada = AdaBoostRegressor()
# model = ada.fit(X_train, y_train)
param_grid_ada = {'n_estimators': [20, 100],
'learning_rate': [0.01, 0.05, 1]}
# scoring_method = 'r2'
# scoring_method = 'explained_variance'
# scoring_method = 'neg_mean_absolute_error'
# scoring_method = 'neg_mean_squared_error'
#scoring_method = 'neg_mean_squared_log_error'
ada_regressor = GridSearchCV(estimator=ada, param_distributions=param_grid_ada,
cv=3, n_jobs=-1, scoring=scoring_method, verbose=0)
ada_regressor.fit(X_train, y_train)
model = ada_regressor.best_estimator_
'''
ada_regressor = AdaBoostRegressor()
model = ada_regressor.fit(X_train, y_train)
'''
return model
def evaluate_model(model, X_test, y_test):
from sklearn.metrics import mean_squared_error
#from sklearn.metrics import mean_squared_log_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import explained_variance_score
from sklearn.metrics import r2_score
y_predict = model.predict(X_test)
mae = mean_absolute_error(y_test, y_predict)
mse = mean_squared_error(y_test, y_predict)
#msle = mean_squared_log_error(y_test, y_predict)
explained_variance = explained_variance_score(y_test, y_predict)
r2 = r2_score(y_test, y_predict)
return mse
def append_return_table(df_predict, unique_datetime, y_trade_return, trade_tic, current_index):
tmp_table = pd.DataFrame(columns=trade_tic)
tmp_table = tmp_table.append(pd.Series(y_trade_return, index=trade_tic), ignore_index=True)
df_predict.loc[unique_datetime[current_index]][tmp_table.columns] = tmp_table.loc[0]
def run_4model(df,features_column, label_column,date_column,tic_column,
unique_ticker, unique_datetime, trade_date,
first_trade_date_index=20,
testing_windows=4,
max_rolling_window_index=44):
## initialize all the result tables
## need date as index and unique tic name as columns
df_predict_rf = pd.DataFrame(columns=unique_ticker, index=trade_date)
df_predict_gbm = pd.DataFrame(columns=unique_ticker, index=trade_date)
df_predict_xgb = pd.DataFrame(columns=unique_ticker, index=trade_date)
df_predict_best = pd.DataFrame(columns=unique_ticker, index=trade_date)
df_best_model_name = pd.DataFrame(columns=['model_name'], index=trade_date)
evaluation_record = {}
# first trade date is 1995-06-01
# fist_trade_date_index = 20
# testing_windows = 6
import re
df = df.rename(columns = lambda x:re.sub('[^A-Za-z0-9_]+', '', x))
for i in range(first_trade_date_index, len(unique_datetime)):
try:
# prepare training data
X_train, y_train = prepare_rolling_train(df,
features_column,
label_column,
date_column,
unique_datetime,
testing_windows,
first_trade_date_index,
max_rolling_window_index,
current_index=i
)
# prepare testing data
X_test, y_test = prepare_rolling_test(df,
features_column,
label_column,
date_column,
unique_datetime,
testing_windows,
first_trade_date_index,
current_index=i)
# prepare trade data
X_trade, y_trade, trade_tic = prepare_trade_data(df,
features_column,
label_column,
date_column,
tic_column,
unique_datetime,
testing_windows,
first_trade_date_index,
current_index=i)
# Training
# lr_model = train_linear_regression(X_train, y_train)
t = time.perf_counter()
xgb_model = train_xgb(X_train, y_train)
print(f"xgb:{time.perf_counter() - t}s")
t = time.perf_counter()
gbm_model = train_lightgbm(X_train, y_train)
print(f"gbm:{time.perf_counter() - t}s")
t =time.perf_counter()
rf_model = train_random_forest(X_train, y_train)
print(f"rf:{time.perf_counter() - t}s")
# ridge_model = train_ridge(X_train, y_train)
# Validation
rf_eval = evaluate_model(rf_model, X_test, y_test)
xgb_eval = evaluate_model(xgb_model, X_test, y_test)
gbm_eval = evaluate_model(gbm_model, X_test ,y_test)
# Trading
y_trade_rf = rf_model.predict(X_trade)
y_trade_xgb = xgb_model.predict(X_trade)
y_trade_gbm = gbm_model.predict(X_trade)
# Decide the best model
eval_data = [
[rf_eval, y_trade_rf] ,
[xgb_eval, y_trade_xgb],
[gbm_eval, y_trade_gbm]
]
eval_table = pd.DataFrame(eval_data, columns=['model_eval', 'model_predict_return'],
index=['rf', 'xgb', 'gbm'])
evaluation_record[unique_datetime[i]]=eval_table
# lowest error score model
y_trade_best = eval_table.model_predict_return.values[eval_table.model_eval == eval_table.model_eval.min()][0]
best_model_name = eval_table.index.values[eval_table.model_eval == eval_table.model_eval.min()][0]
# Highest Explained Variance
# y_trade_best = eval_table.model_predict_return.values[eval_table.model_eval==eval_table.model_eval.max()][0]
# best_model_name = eval_table.index.values[eval_table.model_eval==eval_table.model_eval.max()][0]
df_best_model_name.loc[unique_datetime[i]] = best_model_name
# Prepare Predicted Return table
append_return_table(df_predict_rf, unique_datetime, y_trade_rf, trade_tic, current_index=i)
append_return_table(df_predict_xgb, unique_datetime, y_trade_xgb, trade_tic, current_index=i)
append_return_table(df_predict_gbm, unique_datetime, y_trade_gbm, trade_tic, current_index=i)
append_return_table(df_predict_best, unique_datetime, y_trade_best, trade_tic, current_index=i)
print('Trade Date: ', unique_datetime[i])
except Exception:
traceback.print_exc()
df_evaluation = get_model_evaluation_table(evaluation_record,trade_date)
return (
df_predict_rf,
df_predict_gbm,
df_predict_xgb,
df_predict_best,
df_best_model_name,
evaluation_record,
df_evaluation)
def get_model_evaluation_table(evaluation_record,trade_date):
evaluation_list = []
for d in trade_date:
try:
evaluation_list.append(evaluation_record[d]['model_eval'].values)
except:
print('error')
df_evaluation = pd.DataFrame(evaluation_list,columns = ['rf', 'xgb', 'gbm'])
df_evaluation.index = trade_date
return df_evaluation
def save_model_result(sector_result,sector_name):
df_predict_rf = sector_result[0].astype(np.float64)
df_predict_gbm = sector_result[1].astype(np.float64)
df_predict_xgb = sector_result[2].astype(np.float64)
df_predict_best = sector_result[3].astype(np.float64)
df_best_model_name = sector_result[4]
df_evaluation_score = sector_result[5]
df_model_score = sector_result[6]
filename = 'results/'+sector_name+'/'
if not os.path.exists(os.path.dirname(filename)):
try:
os.makedirs(os.path.dirname(filename))
except OSError as exc: # Guard against race condition
if exc.errno != errno.EEXIST:
raise
df_predict_rf.to_csv('results/'+sector_name+'/df_predict_rf.csv')
df_predict_gbm.to_csv('results/'+sector_name+'/df_predict_gbm.csv')
df_predict_xgb.to_csv('results/'+sector_name+'/df_predict_xgb.csv')
df_predict_best.to_csv('results/'+sector_name+'/df_predict_best.csv')
df_best_model_name.to_csv('results/'+sector_name+'/df_best_model_name.csv')
#df_evaluation_score.to_csv('results/'+sector_name+'/df_evaluation_score.csv')
df_model_score.to_csv('results/'+sector_name+'/df_model_score.csv')
def calculate_sector_daily_return(daily_price, unique_ticker,trade_date):
daily_price_pivot = pd.pivot_table(daily_price, values='adj_price', index=['datadate'],
columns=['tic'], aggfunc=np.mean)
daily_price_pivot=daily_price_pivot[unique_ticker]
daily_return=daily_price_pivot.pct_change()
daily_return = daily_return[daily_return.index>=trade_date[0]]
return daily_return
def calculate_sector_quarterly_return(daily_price, unique_ticker,trade_date_plus1):
daily_price_pivot = pd.pivot_table(daily_price, values='adj_price', index=['datadate'],
columns=['tic'], aggfunc=np.mean)
daily_price_pivot=daily_price_pivot[unique_ticker]
quarterly_price_pivot=daily_price_pivot.ix[trade_date_plus1]
quarterly_return=quarterly_price_pivot.pct_change()
quarterly_return = quarterly_return[quarterly_return.index>trade_date_plus1[0]]
return quarterly_return
def pick_stocks_based_on_quantiles_old(df_predict_best):
quantile_0_25 = {}
quantile_25_50 = {}
quantile_50_75 = {}
quantile_75_100 = {}
for i in range(df_predict_best.shape[0]):
q_25=df_predict_best.iloc[i].quantile(0.25)
q_50=df_predict_best.iloc[i].quantile(0.5)
q_75=df_predict_best.iloc[i].quantile(0.75)
q_100=df_predict_best.iloc[i].quantile(1)
quantile_0_25[df_predict_best.index[i]] = df_predict_best.iloc[i][df_predict_best.iloc[i] <= q_25]
quantile_25_50[df_predict_best.index[i]] = df_predict_best.iloc[i][(df_predict_best.iloc[i] > q_25) & \
(df_predict_best.iloc[i] <= q_50)]
quantile_50_75[df_predict_best.index[i]] = df_predict_best.iloc[i][(df_predict_best.iloc[i] > q_50) & \
(df_predict_best.iloc[i] <= q_75)]
quantile_75_100[df_predict_best.index[i]] = df_predict_best.iloc[i][(df_predict_best.iloc[i] > q_75)]
return (quantile_0_25, quantile_25_50, quantile_50_75, quantile_75_100)
def pick_stocks_based_on_quantiles(df_predict_best):
quantile_0_30 = {}
quantile_70_100 = {}
for i in range(df_predict_best.shape[0]):
q_30=df_predict_best.iloc[i].quantile(0.3)
q_70=df_predict_best.iloc[i].quantile(0.7)
quantile_0_30[df_predict_best.index[i]] = df_predict_best.iloc[i][df_predict_best.iloc[i] <= q_30]
quantile_70_100[df_predict_best.index[i]] = df_predict_best.iloc[i][(df_predict_best.iloc[i] >= q_70)]
return (quantile_0_30, quantile_70_100)
def calculate_portfolio_return(daily_return,trade_date_plus1,long_dict,frequency_date):
df_portfolio_return = pd.DataFrame(columns=['portfolio_return'])
for i in range(len(trade_date_plus1) - 1):
# for long only
#equally weight
#long_normalize_weight = 1/long_dict[trade_date_plus1[i]].shape[0]
# map date and tic
long_tic_return_daily = \
daily_return[(daily_return.index >= trade_date_plus1[i]) &\
(daily_return.index < trade_date_plus1[i + 1])][long_dict[trade_date_plus1[i]].index]
# return * weight
long_daily_return = long_tic_return_daily
df_temp = long_daily_return.mean(axis=1)
df_temp = pd.DataFrame(df_temp, columns=['daily_return'])
df_portfolio_return = df_portfolio_return.append(df_temp)
return df_portfolio_return
def calculate_portfolio_quarterly_return(quarterly_return,trade_date_plus1,long_dict):
df_portfolio_return = pd.DataFrame(columns=['portfolio_return'])
for i in range(len(trade_date_plus1) - 1):
# for long only
#equally weight
#long_normalize_weight = 1/long_dict[trade_date_plus1[i]].shape[0]
# map date and tic
long_tic_return = quarterly_return[quarterly_return.index == trade_date_plus1[i + 1]][long_dict[trade_date_plus1[i]].index]
df_temp = long_tic_return.mean(axis=1)
df_temp = pd.DataFrame(df_temp, columns=['portfolio_return'])
df_portfolio_return = df_portfolio_return.append(df_temp)
return df_portfolio_return
def long_only_strategy_daily(df_predict_return, daily_return, trade_month_plus1, top_quantile_threshold=0.75):
long_dict = {}
for i in range(df_predict_return.shape[0]):
top_q = df_predict_return.iloc[i].quantile(top_quantile_threshold)
# low_q=df_predict_return.iloc[i].quantile(0.2)
# Select all stocks
# long_dict[df_predict_return.index[i]] = df_predict_return.iloc[i][~np.isnan(df_predict_return.iloc[i])]
# Select Top 30% Stocks
long_dict[df_predict_return.index[i]] = df_predict_return.iloc[i][df_predict_return.iloc[i] >= top_q]
# short_dict[df_predict_return.index[i]] = df_predict_return.iloc[i][df_predict_return.iloc[i]<=low_q]
df_portfolio_return_daily = pd.DataFrame(columns=['daily_return'])
for i in range(len(trade_month_plus1) - 1):
# for long only
#equally weight
long_normalize_weight = 1/long_dict[trade_month_plus1[i]].shape[0]
# calculate weight based on predicted return
#long_normalize_weight = \
#long_dict[trade_month_plus1[i]] / sum(long_dict[trade_month_plus1[i]].values)
# map date and tic
long_tic_return_daily = \
daily_return[(daily_return.index >= trade_month_plus1[i]) & (daily_return.index < trade_month_plus1[i + 1])][
long_dict[trade_month_plus1[i]].index]
# return * weight
long_daily_return = long_tic_return_daily * long_normalize_weight
df_temp = long_daily_return.sum(axis=1)
df_temp = pd.DataFrame(df_temp, columns=['daily_return'])
df_portfolio_return_daily = df_portfolio_return_daily.append(df_temp)
# for short only
# short_normalize_weight=short_dict[trade_month[i]]/sum(short_dict[trade_month[i]].values)
# short_tic_return=tic_monthly_return[tic_monthly_return.index==trade_month[i]][short_dict[trade_month[i]].index]
# short_return_table=short_tic_return
# portfolio_return_dic[trade_month[i]] = long_return_table.values.sum() + short_return_table.values.sum()
return df_portfolio_return_daily
def long_only_strategy_monthly(df_predict_return, tic_monthly_return, trade_month, top_quantile_threshold=0.7):
long_dict = {}
short_dict = {}
for i in range(df_predict_return.shape[0]):
top_q = df_predict_return.iloc[i].quantile(top_quantile_threshold)
# low_q=df_predict_return.iloc[i].quantile(0.2)
# Select all stocks
# long_dict[df_predict_return.index[i]] = df_predict_return.iloc[i][~np.isnan(df_predict_return.iloc[i])]
# Select Top 30% Stocks
long_dict[df_predict_return.index[i]] = df_predict_return.iloc[i][df_predict_return.iloc[i] >= top_q]
# short_dict[df_predict_return.index[i]] = df_predict_return.iloc[i][df_predict_return.iloc[i]<=low_q]
portfolio_return_dic = {}
for i in range(len(trade_month)):
# for longX_train_rf only
# calculate weight based on predicted return
long_normalize_weight = long_dict[trade_month[i]] / sum(long_dict[trade_month[i]].values)
# map date and tic
long_tic_return = tic_monthly_return[tic_monthly_return.index == trade_month[i]][
long_dict[trade_month[i]].index]
# return * weight
long_return_table = long_tic_return * long_normalize_weight
portfolio_return_dic[trade_month[i]] = long_return_table.values.sum()
# for short only
# short_normalize_weight=short_dict[trade_month[i]]/sum(short_dict[trade_month[i]].values)
# short_tic_return=tic_monthly_return[tic_monthly_return.index==trade_month[i]][short_dict[trade_month[i]].index]
# short_return_table=short_tic_return
# portfolio_return_dic[trade_month[i]] = long_return_table.values.sum() + short_return_table.values.sum()
df_portfolio_return = pd.DataFrame.from_dict(portfolio_return_dic, orient='index')
df_portfolio_return = df_portfolio_return.reset_index()
df_portfolio_return.columns = ['trade_month', 'monthly_return']
df_portfolio_return.index = df_portfolio_return.trade_month
df_portfolio_return = df_portfolio_return['monthly_return']
return df_portfolio_return
def plot_predict_return_distribution(df_predict_best,sector_name,out_path):
import matplotlib.pyplot as plt
for i in range(df_predict_best.shape[0]):
fig=plt.figure(figsize=(8,5))
df_predict_best.iloc[i].hist()
plt.xlabel("predicted return",size=15)
plt.ylabel("frequency",size=15)
plt.title(sector_name+": trade date - "+str(df_predict_best.index[i]),size=15)
plt.savefig(out_path+str(df_predict_best.index[i])+".png")