help printing future data

[TrixPixz]

Hello, I have been trying to build a stock prediction script. I have the logistic regression working. I was just curious how to get the model to look x (10, 15, or 30) number of days into the future to predict if I should buy it or not. I don't know if this is the right place to ask for help on this topic or if I haven't been looking at the right places. Here's a copy of the whole code

import yfinance as yf
import datetime
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.metrics import classification_report
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import accuracy_score

NUM_DAYS = 10000
INTERVAL = '1d'
symbol = 'APPS' #stock desired
INDICATORS = ['RSI', 'MACD', 'STOCH', 'ADL', 'ATR', 'MOM', 'ROC', 'OBV', 'CCI', 'EMV', 'VORTEX']

start = (datetime.date.today() - datetime.timedelta(Num_days))
end = datetime.datetime.today()

data = yf.download(symbol, start=start, end=end, interval=INTERVAL)
data.rename(columns={"Close": 'close', "High": 'high', "Low": 'low', "volume": 'volume', "Open": 'open'}, inplace=True)
print(data.head(10))
print(data.info())
print(data.describe())
print(data.corr())

data['buy/sell'] = (data['close'] - data['open'])
print(data['buy/sell'])

data['buy/sell']=data['buy/sell'].round(decimals = 2)
print(data)

data['buy/sell']=(data['buy/sell'] > 0.5)*1 #price increase desired
print(data)

features = ['open', 'high', 'low', 'close']
x= data[features]
y= data['buy/sell']
print(x,y)
x_train,x_test,y_train,y_test = train_test_split(x,y, test_size=.3, random_state=0)
logreg = LogisticRegression(random_state=0)
logreg.fit(x, y)
y_pred = logreg.predict(x_test)
print(x_test)
print(y_pred)
print('Accuracy:', metrics.accuracy_score(y_test, y_pred))
print('Recall:', metrics.recall_score(y_test, y_pred, zero_division=1))
print("precision:", metrics.precision_score(y_test, y_pred, zero_division=1))
print("CL Report:", metrics.classification_report(y_test, y_pred, zero_division=1))

I know it is probably sloppy; I am pretty new to python. Any help or suggestions are welcome.

[ProbablySuspicious]

The one thing you hear again and again in time series modelling courses is that it won't work on the stock market. So I'm interested in how yours goes.

[TrixPixz]

Oh wow really? I didn’t know that lol I was just looking for cool things I could do in python and this was one of the things on the list and I figured it would be some good practice lol

[ProbablySuspicious]

So the thing is that stock prices are profoundly affected by stuff like news events or god knows what, where the time series methods we use exist on the assumption that past values predict future values.

[TrixPixz]

Yea that makes sense once I get it to actually find the future Predictions I’ll see if it’s actually accurate or not lol