start_date=train_start_date,
end_date=train_end_date,
tickers=train_tickers,
device=device)
print("Resample ...")
buy_samples, sell_samples, none_samples = \
DataPreparator.over_sample(
raw_buy_samples,
raw_sell_samples,
raw_none_samples,
seed)
os.makedirs(f'data/networks/{run_id}', exist_ok=True)
Logger.log(run_id, f"Id: {run_id}")
if args.train_buyer > 0:
buyer_features = np.array(buy_samples, dtype=np.float32)
print("Train buyer samples detector ...")
buyer_result = gym.train_auto_encoder('buyer',
trader.buyer,
buyer_optimizer,
buyer_features,
buyer_result,
max_epochs=500,
max_steps=10)
print("Reload trader with best training result after training ...")
buyer_optimizer, buyer_result = manager.load(
'buyer', trader.buyer, buyer_optimizer, trader.reset_buyer,
lambda: manager.create_buyer_optimizer(trader), buyer_result)
def trade(self,
run_id,
profit_taking_threshold=None,
buy_and_hold=False,
intra_day=False):
result = ''
print(
f"Trade limited all stocks from {self.start_date} to {self.end_date} ..."
)
message, limit_all_investments, limit_all_gain_loss = \
self._trade(
quotes=self.all_quotes,
all_tickers=self.all_tickers,
profit_taking_threshold=profit_taking_threshold,
buy_and_hold=buy_and_hold,
report_each_trade=True,
tickers=self.stock_exchange.tickers,
max_positions=self.max_limit_positions)
result += f'\nTrade Portfolio (max {self.max_limit_positions} stocks): {message}'
print(
f"Trade all stocks from {self.start_date} to {self.end_date} ...")
message, all_investments, all_gain_loss = \
self._trade(
quotes=self.all_quotes,
all_tickers=self.all_tickers,
profit_taking_threshold=profit_taking_threshold,
buy_and_hold=buy_and_hold,
report_each_trade=True,
tickers=self.stock_exchange.tickers,
max_positions=self.max_positions)
result += f'\nTrade All ({self.max_positions} stocks): {message}'
print(
f"Buy and hold all stocks from {self.start_date} to {self.end_date} ..."
)
message = \
self._buy_and_hold(
self.all_quotes,
self.all_tickers,
False,
self.stock_exchange.tickers)
result += f'\nBuy % Hold All ({len(self.stock_exchange.tickers)} stocks): {message}'
print(result)
Logger.log(run_id, result)
index_ticker = 'URTH'
index_title = self.stock_exchange.etf_tickers[index_ticker]
if intra_day:
compare_index = self.stock_exchange.load_intra_day(
index_ticker, self.start_date, self.end_date, True)
else:
compare_index = self.stock_exchange.load(index_ticker,
self.start_date,
self.end_date, True)
all_title = f'All stocks ({self.max_positions} positions)'
limit_all_title = f'All stocks (max. {self.max_limit_positions} positions at once)'
gain_loss_all_title = f'Return all stocks ({self.max_positions} positions)'
gain_loss_limit_all_title = f'Return all stocks (max. {self.max_limit_positions} positions at once)'
length = (len(compare_index)
if len(compare_index) < len(all_investments) else
len(all_investments))
resulting_frame = pd.DataFrame(
data={
'index':
range(length),
'date':
np.array(compare_index['date'].values[-length:]),
index_title:
np.array(compare_index['adj_close'].values[-length:]),
all_title:
np.array(all_investments[-length:]),
limit_all_title:
np.array(limit_all_investments[-length:]),
gain_loss_all_title:
np.array(all_gain_loss[-length:]) + self.start_capital,
gain_loss_limit_all_title:
np.array(limit_all_gain_loss[-length:]) + self.start_capital
})
all_columns = [
index_title, all_title, limit_all_title, gain_loss_all_title,
gain_loss_limit_all_title
]
for column in all_columns:
change_column = f'Change {column}'
resulting_frame[change_column] = resulting_frame[
column].pct_change(1).fillna(0.0) * 100.0
resulting_frame[column] = \
resulting_frame.apply(
lambda row: resulting_frame[change_column].values[0:int(row['index'] + 1)].sum(),
axis=1)
resulting_frame.set_index(resulting_frame['date'], inplace=True)
fig, axis = plt.subplots(nrows=2)
investment_columns = [all_title, limit_all_title]
resulting_frame[index_title].plot.area(ax=axis[0], stacked=False)
resulting_frame[investment_columns].plot(
ax=axis[0],
figsize=(20, 10),
linewidth=2,
title=f'Investment vs {index_title}')
gain_loss_columns = [gain_loss_all_title, gain_loss_limit_all_title]
resulting_frame[index_title].plot.area(ax=axis[1], stacked=False)
resulting_frame[gain_loss_columns].plot(
ax=axis[1],
figsize=(20, 10),
linewidth=2,
title=f'Portfolio Changes vs {index_title}')
x_min, x_max = self._get_x_min_max(resulting_frame)
axis[0].set_xlim(x_min, x_max)
axis[1].set_xlim(x_min, x_max)
y_min, y_max = self._get_y_min_max(resulting_frame, investment_columns,
index_title)
axis[0].set_ylim(y_min, y_max)
y_min, y_max = self._get_y_min_max(resulting_frame, gain_loss_columns,
index_title)
axis[1].set_ylim(y_min, y_max)
results = resulting_frame[gain_loss_columns].copy()
results.to_csv(f'data/{run_id}.trading.gain_loss.csv')
self._colorize_plot(fig, axis)
plt.savefig(f'data/{run_id}.chart.png')
plt.show()
plt.close()
