# Import the CCXT library
import ccxt

# Define the exchange and the market
exchange = ccxt.binance() # You can use any other supported exchange
market = "BTC/USDT" # You can use any other supported market

# Define the API key and secret
api_key = "YOUR_API_KEY" # Replace with your own API key
api_secret = "YOUR_API_SECRET" # Replace with your own API secret

# Set the authentication parameters
exchange.apiKey = api_key
exchange.secret = api_secret

# Define the trading parameters
amount = 0.01 # The amount of BTC to buy or sell
fast_period = 10 # The fast SMA period
slow_period = 20 # The slow SMA period
interval = "1h" # The candlestick interval

# Define the variables for storing the SMA values
fast_sma = 0
slow_sma = 0
prev_fast_sma = 0
prev_slow_sma = 0

# Define the variable for storing the order id
order_id = None

# Define a function to calculate the SMA
def calculate_sma(data, period):
    # Sum up the closing prices of the last period candles
    sum = 0
    for i in range(period):
        sum += data[-i-1][4]
    # Divide by the period to get the average
    return sum / period

# Define a function to check the SMA crossover
def check_sma_crossover():
    global fast_sma, slow_sma, prev_fast_sma, prev_slow_sma
    # Get the latest candlestick data
    data = exchange.fetch_ohlcv(market, interval)
    # Calculate the current SMA values
    fast_sma = calculate_sma(data, fast_period)
    slow_sma = calculate_sma(data, slow_period)
    # Calculate the previous SMA values
    prev_fast_sma = calculate_sma(data[:-1], fast_period)
    prev_slow_sma = calculate_sma(data[:-1], slow_period)
    # Check if the fast SMA crossed above the slow SMA
    if fast_sma > slow_sma and prev_fast_sma <= prev_slow_sma:
        return "buy"
    # Check if the fast SMA crossed below the slow SMA
    elif fast_sma < slow_sma and prev_fast_sma >= prev_slow_sma:
        return "sell"
    # Otherwise, return None
    else:
        return None

# Define a function to place an order
def place_order(side):
    global order_id
    # Check if there is an existing order
    if order_id is not None:
        # Cancel the existing order
        exchange.cancel_order(order_id, market)
        # Reset the order id
        order_id = None
    # Create a new order
    order = exchange.create_order(market, "market", side, amount)
    # Print the order details
    print(order)
    # Store the order id
    order_id = order["id"]

# Define the main loop
def main():
    # Run the loop indefinitely
    while True:
        # Check the SMA crossover
        signal = check_sma_crossover()
        # If there is a buy signal
        if signal == "buy":
            # Place a buy order
            place_order("buy")
        # If there is a sell signal
        elif signal == "sell":
            # Place a sell order
            place_order("sell")
        # Wait for one minute
        time.sleep(60)

# Run the main function
if __name__ == "__main__":
    main()
 

Python Online Compiler

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.

Taking inputs (stdin)

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
name = sys.stdin.readline()
print("Hello "+ name)

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