def load_data():
df_adj_close = load_all_data_from_file(prefix + 'etf_data_adj_close.csv',
start_date, end_date)
ticket_list = load_ticket_list(df_adj_close)
print('cleaning data...')
df_adj_close = df_adj_close[ticket_list]
print('Backfill of data for NaNs...')
df_adj_close = df_adj_close.fillna(method='bfill')
print('Cleaning anomalies from data...')
for ticket in ticket_list:
pct = df_adj_close[ticket].pct_change()
for i in range(len(df_adj_close[ticket])):
if i > 0 and i < len(df_adj_close[ticket]) - 1:
bound_lower = pct.iloc[i]
bound_upper = pct.iloc[i + 1]
# print('%d|%f|%f'%(i,bound_lower,bound_upper))
if np.abs(bound_lower) > 0.1 and np.abs(bound_upper) > 0.1:
# print('changing price from %f to %f' %(df_adj_close[ticket].iloc[i], df_adj_close[ticket].iloc[i+1]))
df_adj_close[ticket].iloc[i] = df_adj_close[ticket].iloc[i
+
1]
ticket_list = remove_anomalies_tickets(df_adj_close, ticket_list)
df_adj_close = df_adj_close[ticket_list]
return ticket_list, df_adj_close
def compute_one_etf(etf):
start_date, end_date = compute_dates(etf)
print(start_date)
df_adj_close = load_all_data_from_file('etf_data_adj_close.csv', start_date, end_date)
dca = bah.DCA(30, 300.)
investor = bah.Investor(etf, np.full(len(etf), 1.0 / len(etf)), dca)
sim = bah.BuyAndHoldInvestmentStrategy(investor, 2.)
sim.invest(df_adj_close[etf])
investor.compute_means()
_, ax = plt.subplots(3, 1)
for rms in investor.rms_list:
ax[2].plot(rms)
print('invested:' + str(investor.invested_history[-1]))
print('value gained:' + str(investor.history[-1]))
print('ror:' + str(investor.ror_history[-1]))
print('mean:' + str(investor.m))
print('std:' + str(investor.std))
for rms in investor.means:
print(str(rms))
#ax[0].plot(np.log(df_adj_close[etf]))
ax[0].plot(sim.investor.invested_history)
ax[0].plot(sim.investor.history)
# ax[0].plot(sim.investor.history)
ax[1].plot(sim.investor.ror_history)
ax[0].legend(['nav', 'invested', 'value'])
ax[1].legend(['RoR'])
plt.show()
# ax[1].plot(sim.investor.invested_history)
# ax[1].plot(sim.investor.history)
# ax[2].plot(sim.investor.ror_history)
# ax[0].legend(['nav'])
# ax[1].legend(['invested', 'value'])
# ax[2].legend(['Returns'])
#
# plt.show()
return investor
print('starting to load data')
prefix = 'xetra_'
start_date = '1993-01-01'
end_date = '2017-12-31'
df_adj_close = load_all_data_from_file(prefix + 'etf_data_adj_close.csv', start_date, end_date)
etf = ['DBPG.DE']
data = df_adj_close[etf].values
print(len(data))
ror = []
for _ in range(1):
df_price = get_data_random_dates(df_adj_close)
investor = compute_one_etf(etf, df_price[etf])
if investor.invested == 0:
continue
print('invested:' + str(investor.invested_history[-1]))
import sys
sys.path.insert(0, '../../../etf_data')
from etf_data_loader import load_all_data_from_file
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dir_data = 'data_ltc_eur/'
dir_models = 'models_ltc_eur/'
ticket = 'LTC_EUR'
start_date = '2017-01-01'
end_date = '2018-06-01'
df_adj_close = load_all_data_from_file('btc_data_open.csv', start_date,
end_date)
data = pd.DataFrame(df_adj_close['Open'])
# data = data.ewm(alpha=0.1).mean()
np.warnings.filterwarnings('ignore')
plt.plot(data.pct_change().cumsum().as_matrix())
legends = ['benchmark']
agent = CryptoTraderAgent('btc', model='models/decision_tree_13.pkl')
agent.invest(data, window=30)
plt.plot(agent.ror_history)
legends.append('ror decision_tree_11.pkl')
chaos_counts = [
