def __init__(self):
self.insdustry_quote = base.load_industryquote_xlsx()
self.nav = base.load_nav_csv()
self.records = base.load_records_csv()
self.current_date = time
self.trading_day_df = base.load_trading_day_csv()
self.quote = base.load_quote_csv()
def test_Update_IR_rank():
records = base.load_records_csv()
industry_quote = base.load_industryquote_xlsx()
nav = base.load_nav_csv()
quote = base.load_quote_csv()
IR_rank = base.load_irweek_csv()
date = '2017-01-06'
df_records = records[records.Updated <= date]
df_ind_quote = industry_quote[industry_quote.TradingDay <= date]
df_nav = nav[nav.NavDate <= date]
df_quote = quote[quote.TradingDay <= date]
Update_IR_rank(date, df_records, df_ind_quote, df_nav, df_quote, IR_rank)
def process_data():
df = base.load_quote_csv()
df = with_nday_avg(df, 2)
df = with_nday_avg(df, 3)
df = with_nday_avg(df, 4)
df = with_nday_avg(df, 5)
df = with_nday_avg(df, 6)
df = with_nday_avg(df, 7)
df = with_nday_ratio_difference(df, 2)
df = with_nday_ratio_difference(df, 3)
df = with_nday_ratio_difference(df, 4)
df = with_nday_ratio_difference(df, 5)
df = with_nday_ratio_difference(df, 6)
df = with_nday_ratio_difference(df, 7)
df = with_nday_reward_ratio(df, 15)
df = label_by_reward_ratio(df, 15)
df = df.drop(column[3:] + ex_column, axis=1)
df = df.dropna(axis=0, how='any')
df.to_csv("../stock_avg.csv", index=False)
import pandas as pd
import numpy as np
import datetime
import time
from bitarray import bitarray
import base
quote = base.load_quote_csv()
industry = base.load_industry_csv()
quote.dropna(inplace=True)
def func(x):
if x < 0:
return 0
elif x >= 0:
return 1
def signs_window(quote, code, size=10):
vals = quote[quote.SecuCode == code].Close
signs = pd.DataFrame(vals.diff()[1:].apply(func))
names = ['signs_1']
df = signs
for i in range(1, size):
df = df.merge(signs.shift(-i), left_index=True, right_index=True)
names.append('signs_' + str(i + 1))
df.columns = names
return np.array(df.dropna().applymap(lambda x: int(x)))