 # Import necessary libraries

import time
from datetime import datetime
import pandas as pd
import numpy as np

import ccxt # Install ccxt package for cryptocurrency exchange data
from talib import *

# Define function to fetch data from exchange API

def fetch_ohlcv(symbol, timeframe, since):
    exchange = ccxt.binance()
    ohlcv = exchange.fetch_ohlcv(symbol, timeframe, since)
    header = ['Timestamp', 'Open', 'High', 'Low', 'Close', 'Volume']
    df = pd.DataFrame(ohlcv, columns=header).set_index('Timestamp')
    return df

# Define function to calculate technical indicator values

def calculate_indicators(ohlcv):
    close = ohlcv['Close']
    rsi = RSI(close, timeperiod=14)
    macd, macdsignal, macdhist = MACD(close, fastperiod=10, slowperiod=26, signalperiod=9)
    return rsi, macd, macdsignal, macdhist

# Define function to place order on exchange

def place_order(exchange, symbol, order_type, amount, price=None):
    if order_type == 'buy':
        order = exchange.create_market_buy_order(symbol, amount)
    elif order_type == 'sell':
        order = exchange.create_market_sell_order(symbol, amount)
    order_id = order.get('id')
    
    # Wait for order to be filled
    while True:
        order_status = exchange.fetch_order_status(order_id)
        if order_status == 'closed':
            break
        time.sleep(5)
    return order_id

# Define main function to automate trading

def trade():
    exchange = ccxt.binance() # Specify exchange
    symbol = 'BTC/USDT' # Specify trading symbol
    amount = 0.001 # Specify order amount
    
    # Specify technical indicators and thresholds
    rsi_thresh = 30
    macd_thresh = -10
    
    # Initialize variables
    position = None
    current_time = datetime.utcnow()
    
    while True:
        # Fetch latest candle data
        ohlcv = fetch_ohlcv(symbol, '1m', current_time.timestamp())
        
        # Calculate technical indicators
        rsi, macd, macdsignal, macdhist = calculate_indicators(ohlcv)
        
        # Determine trading signal
        if rsi.iloc[-1] < rsi_thresh and macdhist.iloc[-1] < macd_thresh:
            signal = 'buy'
        elif rsi.iloc[-1] > 70:
            signal = 'sell'
        else:
            signal = 'hold'
        
        # Place order if signal changes
        if signal == 'buy' and position != 'long':
            order_id = place_order(exchange, symbol, 'buy', amount)
            position = 'long'
        elif signal == 'sell' and position == 'long':
            order_id = place_order(exchange, symbol, 'sell', amount)
            position = None
        
        current_time = datetime.utcnow()
        time.sleep(5)

# Execute main function
if __name__ == '__main__':
    trade()

created 1 year ago

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About Python

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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.

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else:
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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.

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mylist=("Iphone","Pixel","Samsung")
for i in mylist:
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while condition  
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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.

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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:

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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

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