# 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()
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.
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)
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.
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
Indentation is very important in Python, make sure the indentation is followed correctly
For loop is used to iterate over arrays(list, tuple, set, dictionary) or strings.
mylist=("Iphone","Pixel","Samsung")
for i in mylist:
print(i)
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
There are four types of collections in Python.
List is a collection which is ordered and can be changed. Lists are specified in square brackets.
mylist=["iPhone","Pixel","Samsung"]
print(mylist)
Tuple is a collection which is ordered and can not be changed. Tuples are specified in round brackets.
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)
Set is a collection which is unordered and unindexed. Sets are specified in curly brackets.
myset = {"iPhone","Pixel","Samsung"}
print(myset)
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.
mydict = {
"brand" :"iPhone",
"model": "iPhone 11"
}
print(mydict)
Following are the libraries supported by OneCompiler's Python compiler
Name | Description |
---|---|
NumPy | NumPy python library helps users to work on arrays with ease |
SciPy | SciPy is a scientific computation library which depends on NumPy for convenient and fast N-dimensional array manipulation |
SKLearn/Scikit-learn | Scikit-learn or Scikit-learn is the most useful library for machine learning in Python |
Pandas | Pandas is the most efficient Python library for data manipulation and analysis |
DOcplex | DOcplex is IBM Decision Optimization CPLEX Modeling for Python, is a library composed of Mathematical Programming Modeling and Constraint Programming Modeling |