def compute_bah(etf,period,cash_sum):
dca = bah.DCA(period, cash_sum)
investor = bah.Investor(etf, [1.0], dca)
sim = bah.BuyAndHoldInvestmentStrategy(investor, 2.)
sim.invest(df_adj_close[etf])
return investor
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()
def compute_one_etf(etf, inception, results):
print(etf)
result = etf[0]
if inception is not None:
inception_date = inception[inception.ticket ==
etf[0]].inc_date.values[0]
data = df_adj_close[df_adj_close.Date > inception_date][etf]
else:
data = df_adj_close[etf]
dca = bah.DCA(period, cash_to_invest)
investor = bah.Investor(etf, [1.0], dca)
sim = bah.BuyAndHoldInvestmentStrategy(investor, 2.)
sim.invest(data)
investor.compute_means()
result += ';' + str(investor.invested_history[-1])
result += ';' + str(investor.history[-1])
result += ';' + str(investor.ror_history[-1])
result += ';' + str(investor.m)
result += ';' + str(investor.std)
for rms in investor.means:
result += ';' + str(rms)
result += '\n'
print(result)
with open(results, 'a+') as fd:
fd.write(result)
def compute_bah(etf):
dca = bah.DCA(30, 300.)
investor = bah.Investor(etf, [1.0], dca)
sim = bah.BuyAndHoldInvestmentStrategy(investor, 2.)
sim.invest(df_adj_close[etf])
investor.compute_means()
return investor
def run_bah_sim(etf,period,cash_sum):
data = get_data_random_dates()
dca = bah.DCA(period, cash_sum)
investor = bah.Investor(etf, [1.0], dca)
sim = bah.BuyAndHoldInvestmentStrategy(investor, 2.)
sim.invest(data[etf])
return investor
def run_bah_sim(etf):
data = get_data_random_dates()
dca = bah.DCA(30, 300.)
investor = bah.Investor(etf, [1.0], dca)
sim = bah.BuyAndHoldInvestmentStrategy(investor, 2.)
sim.invest(data[etf])
investor.compute_means()
return investor
def compute_one_etf(etf):
inception_date = inception[inception.ticket == etf[0]].inc_date.values[0]
data = df_adj_close[df_adj_close.Date > inception_date][etf]
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(data)
investor.compute_means()
investor.compute_rank()
return data, investor
def compute_one_etf(etf, data):
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(data)
# _, ax = plt.subplots(3, 1)
#
# ax[0].plot(data)
# 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
def compute_one_etf():
global fd
print(etf)
result = etf[0] + '+' + etf[1]
df_open, df_close, df_high, df_low, df_adj_close = load_data(
etf, start_date, end_date)
dca = bah.DCA(30, 300.)
investor = bah.Investor([0.5, 0.5], dca)
sim = bah.BuyAndHoldInvestmentStrategy(investor, 2.)
sim.invest(df_adj_close, df_high, df_low)
means = []
rms_list = []
for i in range(1, 11):
rms = rolling_mean(np.array(sim.investor.ror_history), i * 365)
m = mean(rms)
if m > 0:
rms_list.append(m.round(2))
means.append(m.round(2))
else:
rms_list.append(0.)
means = np.array(means)
m = np.mean(means).round(2)
if np.isnan(m):
m = 0.
std = np.std(means).round(2)
if np.isnan(std):
std = 0.
result += ';' + str(sim.investor.invested_history[-1])
result += ';' + str(sim.investor.history[-1])
result += ';' + str(sim.investor.ror_history[-1])
result += ';' + str(m)
result += ';' + str(std)
for rms in rms_list:
result += ';' + str(rms)
result += '\n'
with open(results, 'a+') as fd:
fd.write(result)
plt.plot(df_adj_close)
plt.legend(df_adj_close)
plt.show()
reb_inv_list = []
for pc in pc_list:
print(pc)
rebalance_inv = rebalancer.simulate(df_adj_close, pc=pc)
reb_inv_list.append(rebalance_inv)
rebalancer.writeResults('reb', etf,
df_adj_close.tail(1).as_matrix()[0], rebalance_inv)
dca = bah.DCA(30, 300.)
investor = bah.Investor(etf, [1.0], dca)
sim = bah.BuyAndHoldInvestmentStrategy(investor, 2.)
sim.invest(df_adj_close, tickets=etf)
print('B&H')
print('zisk:', investor.history[-1])
print('ror:', investor.ror_history[-1])
interpet_results(reb_inv_list, investor, pc_list)
# dca = bah.DCA(30, 300.)
# investor = bah.Investor(etf[1], [1.0], dca)
# sim = bah.BuyAndHoldInvestmentStrategy(investor, 2.)
# sim.invest(data[etf[1]], [etf[1]])
#
# print('B&H')
