def generate_answers():
try:
raw_data = pd.read_csv('DOHMH_New_York_City_Restaurant_Inspection_Results.csv',low_memory=False)
except IOError:
sys.exit()
print "The program has got the raw data. Now it is cleaning the data... Please wait."
raw_data_cleaning = data_clean.clean(raw_data)
cleaned_data = raw_data_cleaning.clean_data()
# question 4: compute the performance of all restaurants and print the sum for different Boroughs.
print "Computing the performance of all restaurants..."
grade_analysis_functions.print_restaurant_grades_all(cleaned_data)
print "Camputing the performance of restaurants in different boroughs..."
grade_analysis_functions.print_restaurant_grades_by_borough(cleaned_data)
# question 5: generate the graphs
print "Generating the graphs..."
df_whole_city = grade_analysis_functions.get_grade_count_values(cleaned_data)
graph_plot.generate_graph(df_whole_city,'nyc')
for boroname in cleaned_data['BORO'].unique():
graph_plot.generate_graph(grade_analysis_functions.get_grade_count_values(cleaned_data[cleaned_data['BORO'] == boroname]),boroname.lower())
print "The graphs have been generated. Please check the current directory. Thanks!"
def main():
# Load the training dataset
training = pd.read_csv(
'tcd ml 2019-20 income prediction training (with labels).csv')
# Clean training data
training = dc.clean(training)
# Format training data
training = dp.shape(training)
model_columns = range(1, len(training.columns) - 2)
X = training.iloc[:, model_columns]
y = training.iloc[:, -1]
scaler = StandardScaler()
X = scaler.fit_transform(X, y)
regression = LinearRegression()
regression.fit(X, y)
first_model = (mean_squared_error(y_true=y, y_pred=regression.predict(X)))
print(first_model)
# lm=ElasticNet(normalize=True, max_iter=5000)
# lm=SGDRegressor(max_iter=5000, penalty='elasticnet', tol=1e-3)
lm = MLPRegressor(
hidden_layer_sizes=(5, ),
# alpha=0.001,
activation='relu',
solver='adam',
learning_rate='constant',
max_iter=10000,
# n_iter_no_change=10000,
learning_rate_init=0.01)
# lm=LinearSVR(epsilon=0.0, C=0.5, tol=1e-4, max_iter=5000)
search = GridSearchCV(
estimator=lm,
param_grid={
# 'C':np.linspace(0.5,1,5)
'alpha': np.logspace(-10, 1, 10)
# ,'l1_ratio':np.linspace(.3,.7,5)
},
scoring='neg_mean_squared_error',
n_jobs=1,
refit=True,
cv=10)
search.fit(X, y)
# first_model=(mean_squared_error(y_true=y,y_pred=lm.predict(X)))
# print(first_model)
print(search.best_params_)
print(abs(search.best_score_))
def build_lda_for_data(filename, word_set):
word_set_dic = dict(zip(word_set, range(0, len(word_set))))
dataset = []
with open(filename, 'rb') as f:
for line in f:
one_data = np.zeros(len(word_set), dtype=int)
words = jieba.cut(data_clean.clean(line), cut_all=False)
for word in words:
idx = word_set_dic.get(word, -1)
if idx < 0:
continue
else:
one_data[idx] += 1
dataset.append(one_data)
return np.array(dataset), word_set_dic
def build_word_set(filename, stop_words_filename=None):
if stop_words_filename is None:
stop_words = load_stop_words()
else:
stop_words = load_stop_words(stop_words_filename)
word_set = {}
with open(filename, 'rb') as f:
for line in f:
words = jieba.cut(data_clean.clean(line), cut_all=False)
for word in words:
if stop_words.get(word, 0):
continue
else:
word_set[word] = word_set.get(word, 1)
return word_set
import data_merge as dm
import factor_test as ft
cash_flow = dbi.get_stocks_data('equity_selected_cashflow_sheet_q',
['net_incr_cash_cash_equ'], '2004-01-01',
'2018-03-01')
cash_flow['ncf_ttm'] = cash_flow.groupby('stkcd')[[
'net_incr_cash_cash_equ'
]].apply(lambda x: x.rolling(4, min_periods=4).sum())
store = pd.HDFStore('test_data.h5')
fdmt = store['fundamental_info']
retn_1m = store['retn_1m']
retn_1m_zz500 = store['retn_1m_zz500']
store.close()
data = dm.factor_merge(fdmt, cash_flow)
data = data.loc[:, ['stkcd', 'trd_dt', 'wind_indcd', 'cap', 'ncf_ttm']]
data['NCFP_TTM_raw'] = data['ncf_ttm'] / (10000 * data['cap'])
ncf_raw = data['NCFP_TTM_raw'].groupby(level=1).describe()
data.drop(pd.to_datetime(['2005-01-31', '2005-02-28', '2005-03-31']),
level=1,
inplace=True)
data = dc.clean(data, 'NCFP_TTM')
data = data.set_index(['trd_dt', 'stkcd'])
data.index.names = ['trade_date', 'stock_ID']
signal_input = data[['NCFP_TTM_neu']]
test_data = ft.data_join(retn_1m, signal_input)
btic_des, btic_m = ft.btic(test_data, 'NCFP_TTM')
layer_des = ft.layer_result(test_data, retn_1m_zz500, 'NCFP_TTM')
'2005-01-01', '2018-03-01')
fdmt = dc.st_list(fdmt) # 将含有ST标记和上市不满一年的标记为1
data = pd.merge(fdmt, equity, on=['stkcd', 'trd_dt'], how='left')
data = data.groupby('stkcd').ffill().dropna() # 按股票向前填充
data = data.groupby('stkcd').resample('M', on='trd_dt').last() # 取每月最后的数据
data = data[(data.type_st == 0) & (data.year_1 == 0)] # 去除ST和上市不满一年的数据
bpd = data.loc[:, [
'stkcd', 'trd_dt', 'wind_indcd', 'cap', 'tot_shrhldr_eqy_excl_min_int'
]]
bpd['BP_raw'] = bpd.tot_shrhldr_eqy_excl_min_int / (10000 * bpd.cap)
b_raw = bpd.BP_raw.groupby(by='trd_dt').describe()
# 前三个月样本量较小，删除前三个月的数据
bpd.drop(pd.to_datetime(['2005-01-31', '2005-02-28', '2005-03-31']),
level=1,
inplace=True)
bpd = dc.clean(bpd, 'BP', by='trd_dt') # 去极值、标准化、行业中性化
bpd2 = bpd.set_index(['trd_dt', 'stkcd'])
signal_input = bpd2[['BP_neu']]
price_input = pd.DataFrame(close_price_post.stack(),
columns=['close_price_post'])
signal_input.index.names = price_input.index.names
test_data = price_input.join(signal_input, how='left') # 将因子值和后复权价格合并
test_data = test_data.groupby(level=1).ffill().dropna() # 根据股票向前填充
signal_analysis(test_data['BP_neu'].unstack(),
test_data['close_price_post'].unstack())
store = pd.HDFStore('test.h5') # 将测试数据保存到 test.h5
store['BP_neu'] = signal_input
store['close_price_post'] = price_input
store['test_data'] = test_data
store.close()
def main():
# Load the training dataset
training = pd.read_csv('tcd ml 2019-20 income prediction training (with labels).csv')
# Clean training data
training = dc.clean(training)
# Format training data
training = dp.shape(training)
# training.to_csv('training_observe.csv', index=False)
# # Split training into its different parts
# X_train, X_test, y_train, y_test = train_test_split(training.drop(columns = ['Instance', 'Income in EUR'], axis = 1),
# training['Income in EUR'],
# test_size=0.2,
# stratify=y)
model_columns = range(1, len(training.columns) - 2)
# model_columns = [1,3,4,6,7,8]
# Create linear regression object
# lm = linear_model.LinearRegression()
# lm = LinearSVR(tol=1e-4, max_iter=1000)
# lm = linear_model.SGDRegressor(max_iter=10000, tol=1e-6)
# lm = ElasticNet(alpha=0.3, l1_ratio=0.4, tol=1e-4, max_iter=10000)
lm=MLPRegressor(hidden_layer_sizes=(5,),
alpha=0.6,
activation='relu',
solver='adam',
learning_rate='constant',
max_iter=10000,
# n_iter_no_change=10000,
learning_rate_init=0.01)
# Use cross validation
kFold = KFold(10, True, 1)
# # Use principle component analysis
# pca = PCA(n_components = 4)
# Enumerate KFold splits
for training_train, training_test in kFold.split(training):
# Complete split
X_train = training.iloc[training_train, model_columns]
X_test = training.iloc[training_test, model_columns]
y_train = training.iloc[training_train, -1]
y_test = training.iloc[training_test, -1]
# # Transform with PCA
# X_train = pca.fit_transform(X_train)
# X_test = pca.transform(X_test)
#
# explained_variance = pca.explained_variance_ratio_
# for i in explained_variance:
# print(format(i*100, 'f'))
# Train the model using the training sets
lm.fit(X_train, y_train)
# Make predictions using the testing set
y_predict = lm.predict(X_test)
# y_test = pd.DataFrame(y_test, columns= {'Income in EUR'})
# y_predict = pd.DataFrame(y_predict, columns= {'Income in EUR'})
#
# y_test = dp.scaleOutput(y_test, 'Income in EUR', 'post')
# y_predict = dp.scaleOutput(y_predict, 'Income in EUR', 'post')
# Make values more realistic
# y_predict = realizePrediction(y_predict, y_train)
print(lm.score(X_test, y_test))
# The coefficients
# print('Coefficients: \n', lm.coef_)
# The mean squared error
print("Mean squared error: %.2f"
% mean_squared_error(y_test, y_predict))
# Explained variance score: 1 is perfect prediction
print('Variance score: %.2f' % r2_score(y_test, y_predict))
print(' ')
# Load the prediction dataset
test = pd.read_csv('tcd ml 2019-20 income prediction test (without labels).csv')
test = dc.clean(test, drop=False)
#Format test data
test = dp.shape(test)
# Make predictions using the test set
y_predict = lm.predict(test.iloc[:,model_columns])
print(y_predict)
test['Income'] = y_predict
# realizePrediction(y_predict, training['Income in EUR'])
# plot(training, test)
# Write prediction to CSV
pred_out = pd.read_csv('tcd ml 2019-20 income prediction submission file.csv')
pred_out['Income'] = y_predict
# pred_out = dp.scaleOutput(pred_out, 'Income', 'post')
pred_out.to_csv('tcd ml 2019-20 income prediction submission file.csv', index=False)
print(pd.DataFrame(pred_out['Income'], columns={'Income'}).describe())
import sys
sys.path.append(r'E:\FT_Users\XQZhu\stocks_backtest\self_lib') # 自定义的函数
import pandas as pd
import database_api as dbi
import data_clean as dc
import data_merge as dm
import factor_test as ft
divid = dbi.get_stocks_data('equity_cash_dividend', ['cash_div'], '2004-01-01',
'2018-03-01')
store = pd.HDFStore('test_data.h5')
fdmt = store['fundamental_info']
retn_1m = store['retn_1m']
retn_1m_zz500 = store['retn_1m_zz500']
store.close()
data = dm.factor_merge(fdmt, divid)
data = data.loc[:, ['stkcd', 'trd_dt', 'wind_indcd', 'cap', 'cash_div']]
data['DP_raw'] = data['cash_div'] / data['cap']
dp_raw = data['DP_raw'].groupby(level=1).describe()
data.drop(pd.to_datetime(['2005-01-31', '2005-02-28', '2005-03-31']),
level=1,
inplace=True)
data = dc.clean(data, 'DP')
data = data.set_index(['trd_dt', 'stkcd'])
data.index.names = ['trade_date', 'stock_ID']
signal_input = data[['DP_neu']]
test_data = ft.data_join(retn_1m, signal_input)
btic_des, btic_m = ft.btic(test_data, 'DP')
layer_des = ft.layer_result(test_data, retn_1m_zz500, 'NCFP_TTM', quantile=5)
def main():
# Load the training dataset
training = pd.read_csv(
'tcd ml 2019-20 income prediction training (with labels).csv')
# Clean training data
training = dc.clean(training)
# Format training data
training = dp.shape(training)
# Split training into its different parts
X_train, X_test, y_train, y_test = train_test_split(
training.drop(columns=['Instance', 'Income in EUR'], axis=1),
training['Income in EUR'],
test_size=0.1,
shuffle=True)
categorical_features_indices = np.where(X_train.dtypes != np.float)[0]
model = CatBoostRegressor(iterations=5000,
depth=6,
learning_rate=0.15,
loss_function='RMSE',
random_seed=37,
od_type='Iter',
metric_period=50,
od_wait=200,
use_best_model=True,
task_type='GPU')
model.fit(X_train,
y_train,
cat_features=categorical_features_indices,
eval_set=(X_test, y_test),
plot=True)
print(model.score(X_test, y_test))
fea_imp = pd.DataFrame({
'imp': model.feature_importances_,
'col': X_train.columns
})
fea_imp = fea_imp.sort_values(['imp', 'col'], ascending=[True,
False]).iloc[-30:]
fea_imp.plot(kind='barh', x='col', y='imp', figsize=(10, 7), legend=None)
plt.title('CatBoost - Feature Importance')
plt.ylabel('Features')
plt.xlabel('Importance')
# Load the prediction dataset
test = pd.read_csv(
'tcd ml 2019-20 income prediction test (without labels).csv')
test = dc.clean(test, drop=False)
#Format test data
test = dp.shape(test)
# Make predictions using the test set
y_predict = model.predict(test.iloc[:, 1:-1])
print(y_predict)
test['Income'] = y_predict
# Write prediction to CSV
pred_out = pd.read_csv(
'tcd ml 2019-20 income prediction submission file.csv')
pred_out['Income'] = y_predict
pred_out.to_csv('tcd ml 2019-20 income prediction submission file.csv',
index=False)
print(pd.DataFrame(pred_out['Income'], columns={'Income'}).describe())
def load_raw_data(filename):
raw_dataset = []
with open(filename, 'rb') as f:
for line in f:
raw_dataset.append(data_clean.clean(line))
return raw_dataset
import data_clean data_clean.clean()
# Look at uniqueness of columns
#for column_name in training.columns[1:11]:
# uniqueAnalysis(column_name, training)
# After looking at uniqueness, we know this:
# 1. 'Country', 'Size of City', 'Wears Glasses', and 'Body Height [cm]' are complete columns (without NaNs), but set defaults anyway
# 2. Though not complete, 'Gender' NaNs can be put into the 'unknown' category
# 3. Though not complete, 'Hair Color' NaNs can be put into the 'Unknown' category
# 4. The rest of the columns with NaNs will just have to use some best guess for replacing NaNs
# 5. Since 'Country' is complete, might be worth adding a regions column for checking whether there's correlation
# 6. Should look at distribution of 'Body Height [cm]
plt.style.use('ggplot')
training = dc.clean(training)
#training = dp.scaleOutput(training, 'Income in EUR')
training = dp.shape(training)
#print('Year of Record ' + str(len(training.index[training['Year of Record'].isna()])))
#print('Gender ' + str(len(training.index[training['Gender'].isna()])))
#print('Age ' + str(len(training.index[training['Age'].isna()])))
#print('Country ' + str(len(training.index[training['Country'].isna()])))
#print('Size of City ' + str(len(training.index[training['Size of City'].isna()])))
#print('Profession ' + str(len(training.index[training['Profession'].isna()])))
#print('University Degree ' + str(len(training.index[training['University Degree'].isna()])))
#print('Wears Glasses ' + str(len(training.index[training['Wears Glasses'].isna()])))
#print('Hair Color ' + str(len(training.index[training['Hair Color'].isna()])))
#print('Body Height [cm] ' + str(len(training.index[training['Body Height [cm]'].isna()])))
#print('Income in EUR ' + str(len(training.index[training['Income in EUR'].isna()])))
exec(open(r'E:\FT_Users\XQZhu\stocks_backtest\prerun.py').read())
import sys
sys.path.append(r'E:\FT_Users\XQZhu\stocks_backtest\self_lib') # 自定义的函数
import pandas as pd
import database_api as dbi
import data_merge as dm
import data_clean as dc
net_profit = dbi.get_stocks_data('equity_selected_income_sheet_q',
['net_profit_excl_min_int_inc'],
'2004-01-01', '2018-03-01')
net_profit['net_profit_emi_ttm'] = net_profit.groupby('stkcd')[['net_profit_excl_min_int_inc']].apply(
lambda x: x.rolling(4, min_periods=4).sum())
store = pd.HDFStore('test_data.h5')
fdmt = store['fundamental_info']
store.close()
data = dm.factor_merge(fdmt, net_profit)
data = data.loc[:, ['stkcd', 'trd_dt', 'wind_indcd', 'cap', 'net_profit_emi_ttm']]
data['EP_TTM_raw'] = data['net_profit_emi_ttm'] / (10000 * data['cap'])
e_raw = data['EP_TTM_raw'].groupby(level=1).describe()
data.drop(pd.to_datetime(['2005-01-31', '2005-02-28', '2005-03-31']), level=1, inplace=True)
data = dc.clean(data, 'EP_TTM')
signal_input, test_data = dm.test_merge(data, 'EP_TTM_neu', close_price_post)
dm.test_result(test_data, 'EP_TTM_neu', 'close_price_post')
store = pd.HDFStore('test_data.h5')
store['EP_TTM_neu'] = signal_input
store.close()
'EPS_C_1m',
'EPS_C_3m',
'Est_Sales',
'Sales_C_1m',
'Sales_C_3m',
'Fore_Earning',
'Earning_C_1m_ratio',
'Earning_C_3m_ration',
'Retn_30',
'Retn_90',
'Retn_180',
'Inst_Num_30',
'Inst_Num_90',
'Inst_Num_180',
'Rating',
'Rating_C_1m',
'Rating_C_3m',
]
for factor in Consensus_names:
data = fdmt_m.join(consensus.loc[:, factor])
data = data[(data.type_st == 0) & (data.year_1 == 0)].dropna()
data = dc.clean(data, factor) # 对于Rating相关的三个指标，不用去极值
data = data.set_index(['trade_date', 'stock_ID'])
Consensus = Consensus.join(data[[factor + '_neu']], how='outer')
store = pd.HDFStore('test_data.h5')
store['Consensus'] = Consensus
store.close()
BTIC, IC, IC_corr, Annual, Sharpe, Rela_IR = ct.class_test(
Consensus_names, 'Consensus')
exec(open(r'E:\FT_Users\XQZhu\stocks_backtest\prerun.py').read())
import sys
sys.path.append(r'E:\FT_Users\XQZhu\stocks_backtest\self_lib') # 自定义的函数
import pandas as pd
import database_api as dbi
import data_merge as dm
import data_clean as dc
oper_rev = dbi.get_stocks_data('equity_selected_income_sheet_q', ['oper_rev'],
'2004-01-01', '2018-03-01')
oper_rev['oper_rev_ttm'] = oper_rev.groupby('stkcd')[['oper_rev']].apply(
lambda x: x.rolling(4, min_periods=4).sum())
store = pd.HDFStore('test_data.h5')
fdmt = store['fundamental_info']
store.close()
data = dm.factor_merge(fdmt, oper_rev)
data = data.loc[:, ['stkcd', 'trd_dt', 'wind_indcd', 'cap', 'oper_rev_ttm']]
data['SP_TTM_raw'] = data['oper_rev_ttm'] / (10000 * data['cap'])
s_raw = data['SP_TTM_raw'].groupby(level=1).describe()
data.drop(pd.to_datetime(['2005-01-31', '2005-02-28', '2005-03-31']), level=1, inplace=True)
data = dc.clean(data, 'SP_TTM')
signal_input, test_data = dm.test_merge(data, 'SP_TTM_neu', close_price_post)
dm.test_result(test_data, 'SP_TTM_neu', 'close_price_post')
store = pd.HDFStore('test_data.h5')
store['SP_TTM_neu'] = signal_input
store.close()
# -*- coding: utf-8 -*-
import os
import h5py
import numpy as np
from ECoG_model import preprocessing, label_shift_left, binarize
from data_clean import clean
os.chdir(os.path.dirname(__file__))
with h5py.File("ECoG_data.h5", "r+") as f1:
with h5py.File("ECoG_big_data.h5", "w") as f2:
for i in range(1, 4):
subj = "sub" + str(i)
u = f1[subj]["unmixing_matrix"]
X = f1[subj]['train_data'][:]
clist = clean(X)
X = X.dot(u)
Y = f1[subj]['cleaned_train_dg'][:]
Xt = f1[subj]['test_data'][:].dot(u)
Yt = f1[subj]['cleaned_test_dg'][:]
X, _, yb = preprocessing(X, Y[:, 0], cleaning=False)
meanX = np.mean(X, axis=0)
stdX = np.std(X, axis=0)
X -= meanX
X /= stdX
yb = yb[clist, :]
clabel = Y[-X.shape[0]:, :][clist, :]
blabel = np.empty_like(clabel, 'i')
blabel[:, 0] = yb.ravel()
for i in range(1, 5):
blabel[:, i] = \
label_shift_left(binarize(
nrows = month_start_end_row_indices[month_key][1] - skiprows + 1
train_month = pd.read_csv(data_path + "train_ver2.csv",
dtype=load_dtypes,
skiprows=range(1, skiprows + 1),
nrows=nrows)
train_month["fecha_dato"] = pd.to_datetime(train_month["fecha_dato"],
format="%Y-%m-%d")
train_month["fecha_alta"] = pd.to_datetime(train_month["fecha_alta"],
format="%Y-%m-%d")
train_month["age"] = pd.to_numeric(train_month["age"], errors="coerce")
# Data Cleaning
df = train_month
try:
clean(df)
except:
logging.info("Exception is thrown")
continue
assert df.isnull().any().sum(
) == 0, "Data still contains nan values : \n\n {}".format(
df.isnull().any())
if REDUCE_SIZE:
logging.info("- Reduce size")
if selected_clients is None:
full_stats = df.describe()
unique_ids = pd.Series(df["ncodpers"].unique())
limit_people = 200000
counter = 200
net_profit['net_profit_emi_ttm'] = net_profit.groupby('stkcd')[[
'net_profit_excl_min_int_inc'
]].apply(lambda x: x.rolling(4, min_periods=4).sum())
net_profit['growth'] = net_profit.groupby('stkcd')[[
'net_profit_emi_ttm'
]].apply(lambda x: x.pct_change())
store = pd.HDFStore('test_data.h5')
fdmt = store['fundamental_info']
retn_1m = store['retn_1m']
retn_1m_zz500 = store['retn_1m_zz500']
store.close()
data = dm.factor_merge(fdmt, net_profit)
data = data.loc[:, [
'stkcd', 'trd_dt', 'wind_indcd', 'cap', 'net_profit_emi_ttm', 'growth'
]]
data['PEG_TTM_raw'] = 100 * data['cap'] / data['net_profit_emi_ttm'] / data[
'growth']
p_raw = data['PEG_TTM_raw'].groupby(level=1).describe()
data.drop(pd.to_datetime(['2005-01-31', '2005-02-28', '2005-03-31']),
level=1,
inplace=True)
data = dc.clean(data, 'PEG_TTM')
data = data.set_index(['trd_dt', 'stkcd'])
data.index.names = ['trade_date', 'stock_ID']
signal_input = data[['PEG_TTM_neu']]
test_data = ft.data_join(retn_1m, signal_input)
btic_des, btic_m = ft.btic(test_data, 'PEG_TTM')
layer_des = ft.layer_result(test_data, retn_1m_zz500, 'PEG_TTM')
Vol1 = HighLow.join(retn_std, how='outer')
Vol2 = Vol1.join(v_std, how='outer')
Vol = Vol2.join(Resid_vol, how='outer')
store = pd.HDFStore('test_data.h5')
fdmt = store['fundamental_info']
store.close()
fdmt.rename(columns={
'trd_dt': 'trade_date',
'stkcd': 'stock_ID'
},
inplace=True)
fdmt_m = fdmt.groupby('stock_ID').resample('M', on='trade_date').last()
# volatility_index = data.loc[:, ['trade_date', 'stock_ID']]
Volatility = store['Volatility_index']
Volatility = Volatility.set_index(['trade_date', 'stock_ID'])
Volatility_names = Vol.columns.tolist()
for factor in Volatility_names:
data = fdmt_m.join(Vol.loc[:, factor])
data = data[(data.type_st == 0) & (data.year_1 == 0)].dropna()
data = dc.clean(data, factor)
data = data.set_index(['trade_date', 'stock_ID'])
Volatility = Volatility.join(data[[factor + '_neu']], how='outer')
store['Volatility'] = Volatility
ind = list(np.sort(data['wind_2'].unique()))[1:]
BTIC, IC, IC_corr, Annual, Sharpe, Rela_IR = ct.class_test(
Volatility_names, 'Volatility')
exec(open(r'E:\FT_Users\XQZhu\stocks_backtest\prerun.py').read())
import sys
sys.path.append(r'E:\FT_Users\XQZhu\stocks_backtest\self_lib') # 自定义的函数
import pandas as pd
import database_api as dbi
import data_merge as dm
import data_clean as dc
cash_flow = dbi.get_stocks_data('equity_selected_cashflow_sheet_q',
['net_cash_flows_oper_act'],
'2004-01-01', '2018-03-01')
cash_flow['oper_cf_ttm'] = cash_flow.groupby('stkcd')[['net_cash_flows_oper_act']].apply(
lambda x: x.rolling(4, min_periods=4).sum())
store = pd.HDFStore('test_data.h5')
fdmt = store['fundamental_info']
store.close()
data = dm.factor_merge(fdmt, cash_flow)
data = data.loc[:, ['stkcd', 'trd_dt', 'wind_indcd', 'cap', 'oper_cf_ttm']]
data['OCFP_TTM_raw'] = data['oper_cf_ttm'] / (10000 * data['cap'])
ocf_raw = data['OCFP_TTM_raw'].groupby(level=1).describe()
data.drop(pd.to_datetime(['2005-01-31', '2005-02-28', '2005-03-31']), level=1, inplace=True)
data = dc.clean(data, 'OCFP_TTM')
signal_input, test_data = dm.test_merge(data, 'OCFP_TTM_neu', close_price_post)
dm.test_result(test_data, 'OCFP_TTM_neu', 'close_price_post')
store = pd.HDFStore('test_data.h5')
store['OCFP_TTM_neu'] =signal_input
store.close()
