print("Hello, World!")import oandapyV20 import oandapyV20.endpoints.instruments as instruments import oandapyV20.endpoints.orders as orders from oandapyV20.contrib.requests import MarketOrderRequest import pandas as pd import numpy as np import time # OANDA API credentials accountID = "YOUR_ACCOUNT_ID" access_token = "YOUR_ACCESS_TOKEN" # Connect to OANDA API api = oandapyV20.API(access_token=access_token) # Function to retrieve historical candlestick data def get_historical_data(instrument, granularity, count): params = { "count": count, "granularity": granularity } r = instruments.InstrumentsCandles(instrument=instrument, params=params) data = api.request(r) prices = [{"time": x['time'], "open": float(x['mid']['o']), "high": float(x['mid']['h']), "low": float(x['mid']['l']), "close": float(x['mid']['c'])} for x in data['candles']] df = pd.DataFrame(prices) df['time'] = pd.to_datetime(df['time']) df.set_index('time', inplace=True) return df # Simple Moving Average (SMA) function def sma(data, period): return data.rolling(window=period).mean() # Function to check if the current price is above SMA def is_price_above_sma(data, period): if data['close'] > data['sma'][period]: return True else: return False # Function to execute a market order def execute_market_order(instrument, units, order_type): if order_type == 'buy': side = 'BUY' elif order_type == 'sell': side = 'SELL' mktOrder = MarketOrderRequest(instrument=instrument, units=units, side=side) r = orders.OrderCreate(accountID, data=mktOrder.data) api.request(r) # Main function for trading def trade_forex(instrument, granularity, sma_period, trade_units): while True: try: # Retrieve historical data historical_data = get_historical_data(instrument, granularity, 20) # Calculate SMA historical_data['sma'] = sma(historical_data['close'], sma_period) # Get the latest candlestick latest_data = historical_data.iloc[-1] # Check if the current price is above SMA if is_price_above_sma(latest_data, sma_period): # Execute a buy order execute_market_order(instrument, trade_units, 'buy') print("Buy order executed at market price.") else: # Execute a sell order execute_market_order(instrument, trade_units, 'sell') print("Sell order executed at market price.") # Wait for the next candlestick time.sleep(60) # Wait for 1 minute except Exception as e: print("Error occurred:", str(e)) if __name__ == "__main__": instrument = "EUR_USD" # Change to your desired instrument granularity = "M1" # 1-minute candlestick sma_period = 20 # Simple Moving Average period trade_units = 1000 # Number of units to trade trade_forex(instrument, granularity, sma_period, trade_units)
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 |