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Why choose to use values[1:] here? shouldn't it be values[:-1]?
use values[1:] then no meaning to get data 1 day before split_date by (market_info['Date']+ datetime.timedelta(days=1))>= split_date, [1:] will skip the first day, start apply random walk on actual_data(t), not actual_date(t-1).
The text was updated successfully, but these errors were encountered:
Apologies for the delay, I'm still getting through my Christmas backlog...
It wasn't immediately obvious to me why I went [1:], but it's actually because the dataframe is ordered by date in descending order. That's the default ordering on coinmarketcap. That means market_info[0] is actually the latest date, so we don't need to try to predict the day after it.
I think that resolves your issue. If not, please let me know!
In bolack in[13],
ax1.plot(market_info[market_info['Date']>= split_date]['Date'].astype(datetime.datetime), market_info[(market_info['Date']+ datetime.timedelta(days=1))>= split_date]['bt_Close'].values[1:] * (1+bt_random_steps), label='Predicted')
Why choose to use
values[1:]
here? shouldn't it bevalues[:-1]
?use
values[1:]
then no meaning to get data 1 day before split_date by(market_info['Date']+ datetime.timedelta(days=1))>= split_date
,[1:]
will skip the first day, start apply random walk on actual_data(t), not actual_date(t-1).The text was updated successfully, but these errors were encountered: