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fucck.py
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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import yfinance as yf
import pandas_datareader.data as pdr
import datetime as dt
from whatsapp import str_file, write_in_file
from yahoofinancials import YahooFinancials
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, LSTM
# from Classfier_AI import get_historical_data
STUPID_IDEA = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '0', '.']
test_start = dt.datetime(2010, 1, 1).strftime('%Y-%m-%d')
test_end = (dt.datetime.now() - dt.timedelta(days=0)).strftime('%Y-%m-%d')
MY_X_AXIS = {'open': [], 'close': [], 'high': [], 'low': [], 'volume': [], 'adjclose': [], 'everything': []}
POSSIBLE = {'open': [], 'close': [], 'high': [], 'low': [], 'volume': [], 'adjclose': []}
EPOCHS = 25
BATCH_SIZE = 32
UNITS = 50
PREDICTION_DAYS = 21
PREDICTION_DAY = 1
What = 'close'
yf.pdr_override()
def my_pr(ticker, file='Prediction.txt'):
data = str_file(file).split('\n')
checked_data = []
for i in data:
if i.split(' - ')[1] == ticker:
return analysis_prediction_data
def analysis_prediction_data(data):
my_data = data.split(' - ')
ticker = my_data[1]
pp = my_data[5]
date = my_data[7]
return check_data(ticker, date, pp)
def check_data(ticker, date, price):
data = get_historical_data(ticker, start=date, end=date)
real_price = data['close'][-1]
prediction_and_real_ratio = min(real_price / float(price), float(price) / real_price)
return "\n".join(
["real price - ", str(real_price), "predict price - ", str(price), str(prediction_and_real_ratio)[0:5]])
def get_historical_data(ticker, start=test_start, end=test_end):
ticker = ticker.strip("'")
data = YahooFinancials(ticker)
print(ticker)
data = data.get_historical_price_data(start, end, 'daily')
t_data = pd.DataFrame(data[ticker]['prices'])
t_data = t_data.drop('date', axis=1).set_index('formatted_date')
t_data.head()
return t_data
def get_my_x_axis():
t = {}
for i in MY_X_AXIS:
t[i] = []
return t
def get_possible():
t = {}
for i in POSSIBLE:
t[i] = []
return t
def getting_stocks():
file = open('../Trading/Data/stocks.txt', 'r')
data = file.read()
file.close()
print(data)
data = data.strip(']').strip('[').split(', ')
for i in range(len(data) - 1):
data[i] = data[i][1:-1:]
return data
def predict_tomorrow(ticker, units, prediction_days, prediction_day,
epoch_par=EPOCHS, batch_size_par=BATCH_SIZE, end_day=test_end):
try:
units = int(units)
except Exception as e:
print("Units are wrong")
units = UNITS
try:
prediction_days = int(prediction_days)
except Exception as e:
print("Prediction Days are wrong")
prediction_days = PREDICTION_DAYS
try:
prediction_day = int(prediction_day)
except Exception as e:
print("Prediction Day is wrong")
prediction_day = PREDICTION_DAY
data = get_historical_data(ticker, end=end_day)
all_in_one = get_my_x_axis()
for i in range(len(data[What])):
x = 0
for j in all_in_one.keys():
if j in POSSIBLE.keys():
all_in_one[j].append(data[j][i])
x += (data[j][i])
all_in_one['everything'].append(x)
data = pd.DataFrame(data=all_in_one)
print(data)
# data.append("all", ignore_index=True)
# print(data["all"])
# print(123, data['everything'].values.reshape(-1, 1))
scalar = MinMaxScaler(feature_range=(0, 1))
scaled_data_open = scalar.fit_transform(data['open'].values.reshape(-1, 1))
scaled_data_close = scalar.fit_transform(data['close'].values.reshape(-1, 1))
scaled_data_low = scalar.fit_transform(data['low'].values.reshape(-1, 1))
scaled_data_high = scalar.fit_transform(data['high'].values.reshape(-1, 1))
scaled_data_volume = scalar.fit_transform(data['volume'].values.reshape(-1, 1))
scaled_data_adjclose = scalar.fit_transform(data['adjclose'].values.reshape(-1, 1))
scaled_data = []
for i in range(len(scaled_data_close)):
scaled_data.append([scaled_data_open[i][-1], scaled_data_close[i][-1],
scaled_data_high[i][-1], scaled_data_low[i][-1], scaled_data_adjclose[i][-1],scaled_data_volume[i][-1]
])
# scaled_data = scalar.fit_transform(data['everything'].values.reshape(-1, 1))
scaled_data = scalar.fit_transform(scaled_data)
scaled_data_y = scalar.fit_transform(data[What].values.reshape(-1, 1))
x_train = []
y_train = []
# print(scaled_data)
for x in range(prediction_days, len(scaled_data)):
# print('new', scaled_data[x - prediction_days:x, 0])
x_train.append(scaled_data[x - prediction_days:x, 0])
y_train.append(scaled_data_y[x, 0])
x_train, y_train = np.array(x_train), np.array(y_train)
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
model = Sequential()
model.add(LSTM(units=units, return_sequences=True, input_shape=(x_train.shape[1], 1)))
model.add(Dropout(0.2))
model.add(LSTM(units=units, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=units))
model.add(Dropout(0.2))
model.add(Dense(units=prediction_day))
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(x_train, y_train, epochs=epoch_par, batch_size=batch_size_par)
""""" Test The Data """""
test_data = get_historical_data(ticker, end=end_day)
all_in_one2 = get_my_x_axis()
for i in range(len(test_data[What])):
x = 0
for j in all_in_one.keys():
if j != 'everything':
all_in_one2[j].append(data[j][i])
x += test_data[j][i]
all_in_one2['everything'].append(x)
print(all_in_one2)
test_data = pd.DataFrame(data=all_in_one2)
actual_prices = test_data[What].values
total_data_set = pd.concat((data['open'], test_data[What]), axis=0)
model_input_open = total_data_set[len(total_data_set) - len(test_data) - prediction_days:].values
model_input_open = model_input_open.reshape(-1, 1)
total_data_set = pd.concat((data['close'], test_data[What]), axis=0)
model_input_close = total_data_set[len(total_data_set) - len(test_data) - prediction_days:].values
model_input_close = model_input_close.reshape(-1, 1)
total_data_set = pd.concat((data['low'], test_data[What]), axis=0)
model_input_low = total_data_set[len(total_data_set) - len(test_data) - prediction_days:].values
model_input_low = model_input_low.reshape(-1, 1)
total_data_set = pd.concat((data['high'], test_data[What]), axis=0)
model_input_high = total_data_set[len(total_data_set) - len(test_data) - prediction_days:].values
model_input_high = model_input_high.reshape(-1, 1)
total_data_set = pd.concat((data['adjclose'], test_data[What]), axis=0)
model_input_adjclose = total_data_set[len(total_data_set) - len(test_data) - prediction_days:].values
model_input_adjclose = model_input_adjclose.reshape(-1, 1)
total_data_set = pd.concat((data['volume'], test_data[What]), axis=0)
model_input_volume = total_data_set[len(total_data_set) - len(test_data) - prediction_days:].values
model_input_volume = model_input_volume.reshape(-1, 1)
model_input = []
for i in range(len(model_input_high)):
model_input.append([model_input_open[i][-1], model_input_close[i][-1],
model_input_high[i][-1], model_input_low[i][-1],
model_input_adjclose[i][-1], model_input_volume[i][-1]])
# model_input = scalar.fit_transform(model_input)
model_input = scalar.transform(model_input)
print(model_input[-1], scaled_data[-1])
# Make Predictions on Test Data
x_test = []
for x in range(prediction_days, len(model_input)):
x_test.append(model_input[x - prediction_days:x, 0])
x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
pre_prices = model.predict(x_test)
pre_prices = scalar.inverse_transform(pre_prices)
# Predict Next Day
print(model_input[-1], model_input[-2], model_input[-3], model_input[-4], model_input[-5],
model_input[-6], model_input[-7], model_input[-8], model_input[-9], model_input[-10])
real_data = [model_input[len(model_input) + 1 - prediction_days:len(model_input + 1), 0]]
real_data = np.array(real_data)
real_data = np.reshape(real_data, (real_data.shape[0], real_data.shape[1], 1))
# print(real_data)
prediction = model.predict(real_data)
prediction = scalar.inverse_transform(prediction)
# # Plot the Test Prediction
# plt.plot(actual_prices, color='blue')
# plt.plot(pre_prices, color='red')
# plt.title(f'{ticker} Share Price')
# plt.xlabel('Time')
# plt.ylabel(f'{ticker} Share Price')
# plt.legend()
# plt.show()
return prediction, data[What].values[-1]
def predict_stocks(ticker_list, units='140', prediction_day='1', prediction_days='80',
epochs_par=EPOCHS, batch_size=BATCH_SIZE, end_day=test_end):
long_stocks = []
short_stocks = []
float_price = 0
yesterday = 0
print("units: ", units, "PD:", prediction_day, "PDS:", prediction_days)
for my_ticker in ticker_list:
my_prediction, yesterday = predict_tomorrow(my_ticker, units, prediction_days=prediction_days,
prediction_day=prediction_day,
batch_size_par=batch_size, epoch_par=epochs_par, end_day=end_day)
print(f"Prediction - {my_prediction, yesterday}")
print(123456, my_prediction[-1][-1])
float_price = my_prediction[-1][-1]
if float_price > float(yesterday):
long_stocks.append((my_ticker, f"Predict Price - {float_price}", f"Last Price - {float(yesterday)}"))
else:
short_stocks.append((my_ticker, f"Predict Price - {float_price}", f"Last Price - {float(yesterday)}"))
write_in_file(path='Prediction2.txt', data=float_price)
return float(float_price)
predict_stocks(['NIO'])