Hello and welcome, today we are doing some predictions with python.
Here is the python code:
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import numpy as np
from statistics import mean
import matplotlib.pyplot as plt
import csv
import sys
def new_entry():
mylist = []
print("ML system isnt made to take into consideration things like other months or years,so we only work with days.\n")
user_day = str(input("Enter the day (numeric):"))
user_price = str(input("Enter the price(numeric, not float-point value):"))
print("\nSaving old data...")
file2 = open("btc2019.csv", "r")
for k in file2:
mylist.append(k)
file2.close()
print("\nAdding new data...")
file = open("btc2019.csv", "w+")
#lets form our new entry
newentry = "\n"+user_day+","+"1"+","+"2019"+","+user_price
for r in mylist:
operationManager = str(r)
file.write(operationManager)
file.write(newentry)
file.close()
print("Done.")
def predict_btc():
day = []
price = []
with open('btc2019.csv', newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
day.append(row['day'])
price.append(row['price'])
lastestentry = day[-1]
print("\nLatest entry (day):",lastestentry,"\n")
print("What number would you like to predict(date-day)\nEnter a day (numeric):")
keys = int(input(">"))
xs = np.array(day, dtype=np.float64)
ys = np.array(price, dtype=np.float64)
def best_fit_slope_and_intercept(xs,ys):
m = ( ((mean(xs) * mean(ys)) - mean(xs*ys)) /
((mean(xs) * mean(xs)) - mean(xs*xs)))
b = mean(ys) - m*mean(xs)
return m,b
m,b = best_fit_slope_and_intercept(xs,ys)
print("m and b : ",m,b)
regression_line = [(m*x)+b for x in xs]
predict_x = keys #sami zadamo
predict_y = (m*predict_x)+b
print("Predicting x (",predict_x,") to be :",predict_y)
plt.scatter(xs,ys,color='b')
plt.scatter(predict_x, predict_y,color='r')
plt.plot(xs, regression_line,color='r')
plt.title("Prediction of the Bitcoin price - January 2019.")
plt.show()
usage= """
USAGE:
>myprogram.py <argument>
-arguments are:
-->predict
-->new
-Predict allows you to predict based on your csv
-new allows you to add new data into your csv, sometimes if you do it wrong
it messes shit up.
example:
> titaniumsky.py predict
"""
def Main():
try:
if sys.argv[1] == "predict":
print("Loading the prediction tool.")
predict_btc()
elif sys.argv[1] == "new":
print("Making a new entry")
new_entry()
else:
print("Invalid argument passed.Printing usage...\n")
print("Usage:\n",usage)
print("\n\n")
Main()
except IndexError:
print("No parameters given.")
print("Printing out help.\n",usage)
Main()
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And here is my CSV file, you can always make yours or use a different one:
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day,month,year,price
1,1,2019,3485
2,1,2019,3443
3,1,2019,3468
4,1,2019,3584
5,1,2019,3604
6,1,2019,3599
7,1,2019,3607
8,1,2019,3584
9,1,2019,3605
10,1,2019,3575
11,1,2019,3600
12,1,2019,3725
13,1,2019,3652
14,1,2019,3677
15,1,2019,3651
16,1,2019,3631
17,1,2019,3704
18,1,2019,3557
19,1,2019,3658
20,1,2019,3686
21,1,2019,3674
22,1,2019,4034
23,1,2019,4034
24,1,2019,4028
25,1,2019,4078
26,1,2019,3836
27,1,2019,3851
28,1,2019,3832
29,1,2019,3931
30,1,2019,3849
31,1,2019,3950
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Feel free to edit this dataset or the code itself, the code does not need the "New entry" function, it does not need to be ran with a sys module,
this can be done way less complicated.
Thank you so much for watching and have a nice day. :)