SajidLhessani

#Neccessary librairies
from binance import Client
import pandas as pd
import numpy as np
import talib as ta
import time

#Insert your secret key
key = 'AsEGPnZK7hpF7DFUb4Bdsmd5nTf1q6qQ1ULhqlaafdPWl0T1l6bbTm6QyuTrOJeP'
secret = '640p8wsE9cOUmJ7qH2RkleRzOn8HCdlXE2r4uZctLgP25Cp4lT9j3cCrAsrslKUx'

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#Copy and paste your API keys within these variables
key = 'H0gXdPT6Zu12L7siZQBOoI3JJZaN2i9leT7eM457IFHHG980aeds76'
secret = '2KwevuHUq1IHW1jsC4WBtABn4hzB2i9leT7eM457IFHHG980aeds76'

# Store the connection command line within a variable called "client"
client = Client(api_key=key, api_secret = secret)

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# Use drop method to drop the columns
X = df.drop(['Close', 'Signal', 'High',
             'Low', 'Volume', 'Ret'], axis=1)

# Create a variable which contains all the 'Signal' values
y = df['Signal']

# Test variables for 'c' and 'g'
c = [10, 100, 1000, 10000]
g = [1e-2, 1e-1, 1e0]

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# Save the predicted values for the train data
df.iloc[:split, df.columns.get_loc('Pred_Signal')] = pd.Series(
    cls.predict(ss1.transform(X.iloc[:split])).tolist())

# Save the predicted values for the test data
df.iloc[split:, df.columns.get_loc('Pred_Signal')] = y_predict

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# Pass the test data to the predict function and store the values into 'y_predict'
y_predict = cls.predict(ss1.transform(X.iloc[split:]))

# Initiate a column by name, 'Pred_Signal' and assign 0 to it
df['Pred_Signal'] = 0

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# Use drop method to drop the columns
X = df.drop(['Close', 'Signal', 'High',
             'Low', 'Volume', 'Ret'], axis=1)

# Create a variable which contains all the 'Signal' values
y = df['Signal']

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# Call the 'fit' method of rcv and pass the train data to it
rcv.fit(X.iloc[:split], y.iloc[:split])

# Call the 'best_params_' method to obtain the best parameters of C
best_C = rcv.best_params_['svc__C']

# Call the 'best_params_' method to obtain the best parameters of kernel
best_kernel = rcv.best_params_['svc__kernel']

# Call the 'best_params_' method to obtain the best parameters of gamma

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# Instantiate the StandardScaler
ss1 = StandardScaler()

# Pass the scaled train data to the SVC classifier
cls.fit(ss1.fit_transform(X.iloc[:split]), y.iloc[:split])

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# Test variables for 'c' and 'g'
#Setting the different values to test within C, Gamma and Kernel
c = [10, 100, 1000, 10000]
g = [1e-2, 1e-1, 1e0]

# Intialise the parameters
parameters = {'svc__C': c,
              'svc__gamma': g,
              'svc__kernel': ['rbf']
              }

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#Import packages
import pandas as pd
import numpy as np

import yfinance as yf

#Scrap the data
#data = pd.read_html('https://indiancompanies.in/listed-companies-in-nse-with-symbol/')[0]
data = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')[0]["Symbol"]
data