def get_prices_with_features(ticker, interval, start, end, limit):
if ticker is None:
return 'Missing parameter: ticker'
if interval is None:
return 'Missing parameter: interval'
if start is not None:
error = is_datetime_string(start)
if error is not None:
return error
else:
return 'Missing parameter: ' + _PARAM_START
if end is not None:
error = is_datetime_string(end)
if error is not None:
return error
if limit is not None:
error = is_integer_string(limit)
if error is not None:
return error
prices = pg.get_prices_with_features(ticker, interval, start, end, limit)
return prices.to_html()
import numpy as np
import pandas as pd
import dash
import dash_core_components as dcc
import dash_html_components as html
from sklearn.linear_model import LinearRegression
import utils.AlphaVantageUtils as av
import utils.PostgresUtils as pg
import utils.ModelUtils as mdl
name = pg.get_symbol_name(av._TIC_MICROSOFT)
df_prices = pg.get_prices_with_features(av._TIC_MICROSOFT, av._INT_DAILY, None,
None, None)
df_prices.drop(columns=['open', 'high', 'low', 'volume'], inplace=True)
print(df_prices.info())
df_train, df_test = mdl.train_test_split(df_prices, 1000)
predictions = []
train = df_train.drop(pg._COL_DATETIME, axis=1)
test = df_test.drop(pg._COL_DATETIME, axis=1)
print(train.shape)
print(test.shape)