#IMPORT IQ OPTIONS API
from iqoptionapi.api import IQOptionAPI
from iqoptionapi.stable_api import IQ_Option

#IMPORT NUMPY AND TALIB
import numpy as np 
import talib

#--IMPORT THREADING AND TIME (ESSENTIAL)
import threading
import time as t
#--END OF IMPORTS

#USER ACCOUNT CREDENTIALS AND LOG IN 
my_user = "[email protected]"    #YOUR IQOPTION USERNAME
my_pass = "QWer12@*"         #YOUR IQOTION PASSWORD
#CONNECT ==>:
Iq=IQ_Option(my_user,my_pass)
iqch1,iqch2 = Iq.connect()
if iqch1==True:
    print("\nLogin successful.")
else:
    print("Log In Failed.")
#DONE

#CHOOSE BALANCE TYPE
balance_type= "PRACTICE"
if balance_type == 'REAL':
    Iq.change_balance(balance_type)
print("AI Started, Please Wait...")

#SET UP TRADE PARAMETERS 
Money               =   10                      #Amount for Option
goal                =   "EURUSD-OTC"            #Target Instrument
size                =   60                      #Timeframe In Seconds=[1,5,10,15,30,60,120,300,600,900,1800,3600,7200,14400,28800,43200,86400,604800,2592000,"all"]
period              =   14                      #Number of Bars to look back
expirations_mode    =   1                       #Option Expiration Time in Minutes

#GET OHLC DATA FROM IQOPTION
Iq.start_candles_stream(goal,size,period)
cc=Iq.get_realtime_candles(goal,size)

#STORE OPEN AND CLOSE VALUES
my_open = []
my_close =[]

#WHEN TO PLACE BET BEFORE EXPIRY TIME (TIME IN SECONDS)
place_at  = 0
def get_purchase_time():
    remaning_time=Iq.get_remaning(expirations_mode)   
    purchase_time=remaning_time
    return purchase_time

def get_expiration_time():
    exp=Iq.get_server_timestamp()
    time_to_buy=(exp % size)
    return int(time_to_buy)

#THREAD TO RUN TIMER SIMULTANEOUSLY
def expiration_thread():
    global place_at
    while True:
        x=get_expiration_time()
        t.sleep(1)
        if x == place_at:
            place_option(Money,goal,expirations_mode)
threading.Thread(target=expiration_thread).start()

#SET VALUES TO PLACE BET(S)
def set_values():

    global open_val
    global close_val
    global ma_close_val


    for k in list(cc.keys()):
        open=(cc[k]['open'])
        close=(cc[k]['open'])

        my_open.append(open)
        open_size=len(my_open)
        open_val=my_open[open_size-2]

        my_close.append(close)
        close_size=len(my_close)
        close_val=my_close[close_size-1]

        my_ma_close=np.array(my_close)
        ma_close_values = talib.SMA(my_ma_close,timeperiod=14)
        my_ma_close_size=len(ma_close_values)
        ma_close_val = ma_close_values[my_ma_close_size-1]

#BET PLACEMENT CONDITIONS AND BET PLACEMENT
def place_option(Money,goal,expirations_mode):  

    set_values()

    #CALL OPTION
    if close_val>ma_close_val:
        check,id=Iq.buy(Money,goal,"call",expirations_mode)
        if check:
            print("'CALL' Option  Placed Successfully.")
        else:
            print("'CALL' Option failed.")
    #PUT OPTION
    elif close_val<ma_close_val:
        check,id=Iq.buy(Money,goal,"put",expirations_mode)
        if check:
            print("'PUT' Option  Placed Successfully.")
        else:
            print("'PUT' Option failed.")
#--END 

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Write, Run & Share Python code online using OneCompiler's Python online compiler for free. It's one of the robust, feature-rich online compilers for python language, supporting both the versions which are Python 3 and Python 2.7. Getting started with the OneCompiler's Python editor is easy and fast. The editor shows sample boilerplate code when you choose language as Python or Python2 and start coding.

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OneCompiler's python online editor supports stdin and users can give inputs to programs using the STDIN textbox under the I/O tab. Following is a sample python program which takes name as input and print your name with hello.

import sys
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About Python

Python is a very popular general-purpose programming language which was created by Guido van Rossum, and released in 1991. It is very popular for web development and you can build almost anything like mobile apps, web apps, tools, data analytics, machine learning etc. It is designed to be simple and easy like english language. It's is highly productive and efficient making it a very popular language.

Tutorial & Syntax help

Loops

1. If-Else:

When ever you want to perform a set of operations based on a condition IF-ELSE is used.

if conditional-expression
    #code
elif conditional-expression
    #code
else:
    #code

Note:

Indentation is very important in Python, make sure the indentation is followed correctly

2. For:

For loop is used to iterate over arrays(list, tuple, set, dictionary) or strings.

Example:

mylist=("Iphone","Pixel","Samsung")
for i in mylist:
    print(i)

3. While:

While is also used to iterate a set of statements based on a condition. Usually while is preferred when number of iterations are not known in advance.

while condition  
    #code 

Collections

There are four types of collections in Python.

1. List:

List is a collection which is ordered and can be changed. Lists are specified in square brackets.

Example:

mylist=["iPhone","Pixel","Samsung"]
print(mylist)

2. Tuple:

Tuple is a collection which is ordered and can not be changed. Tuples are specified in round brackets.

Example:

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)

Below throws an error if you assign another value to tuple again.

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)
myTuple[1]="onePlus"
print(myTuple)

3. Set:

Set is a collection which is unordered and unindexed. Sets are specified in curly brackets.

Example:

myset = {"iPhone","Pixel","Samsung"}
print(myset)

4. Dictionary:

Dictionary is a collection of key value pairs which is unordered, can be changed, and indexed. They are written in curly brackets with key - value pairs.

Example:

mydict = {
    "brand" :"iPhone",
    "model": "iPhone 11"
}
print(mydict)

Supported Libraries

Following are the libraries supported by OneCompiler's Python compiler

NameDescription
NumPyNumPy python library helps users to work on arrays with ease
SciPySciPy is a scientific computation library which depends on NumPy for convenient and fast N-dimensional array manipulation
SKLearn/Scikit-learnScikit-learn or Scikit-learn is the most useful library for machine learning in Python
PandasPandas is the most efficient Python library for data manipulation and analysis
DOcplexDOcplex is IBM Decision Optimization CPLEX Modeling for Python, is a library composed of Mathematical Programming Modeling and Constraint Programming Modeling