import utils
import pandas as pd
df1 = utils.load_pickle("data/KRW_USD_2019_Q1_Q2.pkl")
df2 = utils.load_pickle("data/KRW_USD_2019_Q3.pkl")
df = utils.concat_two_dfs([df1, df2])
utils.standard_plot(df)
if __name__ == "__main__":
pass
gold_normals = []
for gold_idx, date in enumerate(normal_target_dates):
gold = df_gold['Gold'][gold_idx]
cpi_date = datetime.datetime(date.year, date.month, 1, 0, 0)
cpi_idx = df_cpi['Date'][df_cpi['Date'] == cpi_date].index.tolist()
if len(cpi_idx) == 0:
continue
else:
cpi_idx = cpi_idx[0]
# print(cpi_idx)
cpi = df_cpi['CPI'][cpi_idx]
print("cpi: {}, gold: {}".format(cpi, gold))
normalizer = 100 / cpi
gold_normal = gold * normalizer
gold_normals.append(gold_normal)
gold_normals_date.append(date)
data_cpi_normalized = {'Date': gold_normals_date, 'Gold_Normal': gold_normals}
df_gold_normal = pd.DataFrame(data_cpi_normalized)
utils.standard_plot(df_gold_normal, column_name="Gold_Normal")
utils.pickle_object(df_gold_normal,
"../data/gold_daily_normal_cpi_monthly.pkl")
if __name__ == "__main__":
pass
import utils
import numpy as np
fname = "volatility_data/gold_daily.pkl"
df = utils.load_pickle(fname)
utils.standard_plot(df, column_name='Volatility')
num_data = df.shape[0]
dates = []
signs = []
count_plus = 0
count_minus = 0
count_zero = 0
spread = 2
bigger_than_spread_dates = []
vols_of_bigger_than_spread_dates = []
how_biggers = []
for idx in range(num_data):
curr_date = df.loc[idx]["Date"]
curr_vol = df.loc[idx]["Volatility"]
if curr_vol > 2:
bigger_than_spread_dates.append(curr_date)
vols_of_bigger_than_spread_dates.append(curr_vol)
print(curr_date, " ", curr_vol, " ", curr_vol - spread)
how_biggers.append(curr_vol - spread)
#print(curr_vol)
import utils
fname = "data/gold_daily.pkl"
df = utils.load_pickle(fname)
utils.standard_plot(df, column_name='Gold')
num_date = df.shape[0]
half_spread = 0.01
# days_holding = 4
def count_profitable_trade_date(days_holding):
count = 0
for idx in range(num_date-days_holding):
curr_date = df.loc[idx]["Date"]
# print(curr_date)
curr_price = df.loc[idx]["Gold"]
buy_price_w_spread = curr_price * (1 + half_spread)
sell_date = df.loc[idx+days_holding]["Date"]
sell_date_price = df.loc[idx+days_holding]["Gold"]
sell_price_w_spread = sell_date_price * (1 - half_spread)
arbitrage = sell_price_w_spread - buy_price_w_spread
# print(arbitrage)
if arbitrage >= 0:
count += 1
print("Buy Date: {} Arbitrage: {}".format(curr_date, arbitrage))
print("Total Count: {}".format(count))
cpis = []
count = 0
for line in lines:
if count % 3 == 0:
date = line[0:5].split('\n')[0]
dates.append(date)
elif count % 3 == 1:
cpi = line.split('\n')[0]
cpis.append(cpi)
elif count % 3 == 2:
pass
count += 1
dates = np.array(dates, dtype='datetime64[Y]')
cpis = np.array(cpis, dtype='float64')
data = {'Date': dates,
'CPI': cpis}
df = pd.DataFrame(data)
utils.pickle_object(df, "data/{}.pkl".format(filename))
data_path = "data/cpi_yearly.pkl"
df = utils.load_pickle(data_path)
utils.standard_plot(df, column_name='CPI')
if __name__ == "__main__":
pass
import utils
fname = "data/gold_daily.pkl"
df = utils.load_pickle(fname)
utils.standard_plot(df, column_name='Gold')
num_data = df.shape[0]
volatiles = []
dates = []
for idx in range(num_data-1):
curr_day = df.loc[idx]["Date"]
curr_gold = df.loc[idx]["Gold"]
tmr_gold = df.loc[idx+1]["Gold"]
target_volatility = (tmr_gold - curr_gold) / curr_gold
target_volatility_percentage = target_volatility * 100
print("idx: {}, vol: {}".format(curr_day, target_volatility_percentage))
volatiles.append(target_volatility_percentage)
dates.append(curr_day)
df_volatile = utils.make_as_pandas_df(dates, content_list=volatiles,
content_name="Volatility")
utils.standard_plot(df_volatile, column_name="Volatility")
utils.pickle_object(df_volatile, "gold_daily.pkl")
if __name__ == "__main__":
dates = []
silvers = []
for line in f.readlines():
content = line.split(',')
idx = content[0]
print(idx)
if len(idx) == 0:
continue
date = content[1]
silver = content[2].split('\n')[0]
if len(silver) == 0:
continue
print(silver)
silver = float(silver)
dates.append(date)
silvers.append(silver)
df = utils.make_as_pandas_df(dates,
content_name='Silver',
content_list=silvers)
utils.standard_plot(df, column_name='Silver')
utils.pickle_object(df, "../data/silver_daily.pkl")
if __name__ == "__main__":
pass