In the previous tutorial, we understood the candles prices format (OHLC), as well as learning to use many technical indicators using stockstats library in Python.
In this tutorial, we will learn how to use the fxcmpy wrapper in Python to perform trading operations through the use of FXCM broker on a demo account (virtual money).
For this tutorial, you will need to install:
pip install fxcmpy python-socketio
On your favorite browser, head to the FXCM Trading Station, then click on “Free Demo” as shown in the following figure, to create a free demo account:
Once you signed up for a demo account, the next step is to generate an “Access Token“ that we will use in our Python Code later on:
First import fxcmpy class from fxcmpy package:
from fxcmpy import fxcmpy
Then you should create an instance of that class while specify the access token and the type of the server (demo in our case). Then call the method
connect() to establish the connection, you can check if the connection has been established using the
is_connected() method which returns a boolean (
After that, if you wish to close the connection, consider using
Note: It is Preferable to wrap all fxcmpy operations in
try/except/ block in case the API conncetion get lost for some reason (in most cases, it is a HTTP Exception error and the socket will return
# generate this once you create your demo account # this is fake token, just for demonstration ACCESS_TOKEN = "8438834e8edaff70ca3db0088a8d6c5c37f51279" try: fxcm_con = fxcmpy(access_token=ACCESS_TOKEN, server="demo") print("Is connected:", fxcm_con.is_connected()) except Exception as e: print(e)
In the stock market, prices are usually presented under the OHLC format (Open, High, Low, Close) which is plotted as a candlesticks chart (check this tutorial for further details).
We distinguish two types of Trading Operations, Long (Buy) and Short (Sell).
Long Operations work under this formula: gain/loss = Close_Price - Open_Price
Short Operations work under this formula : gain/loss = - ( Close_Price - Open_Price )
From those two formulas we conclude that:
Choosing between long or short operations all depends on how accurate your financial strategy is, since finding good trading moments using technical indicators and this what defines good financial analysts or what we call as Quants.
To open a long (buy) / short (sell) trade in fxcmpy we use the
open_trade() operations and assign values to this attributes:
symbol: of the stock we want to trade in our case it will be "US30" (Dow Jones Market Index).
Trueif we want a long operation and
Falseif we want a short operation.
amount: Positive integer value, the bigger the amount, the bigger the Gain/Loss (the higher risk).
Every position is assigned a
trade_id that we will be using if we want to close the trade. To get all
trade_ids of opened positions we use the method
get_open_trade_ids() and close a position with the
close_trade() method by specifying the
trade_id and the
trade_id = fxcm_con.get_open_trade_ids() fxcm_con.close_trade(trade_id=trade_id,amount=1)
You can check the trading station to see your opened and closed positions.
In the previous section, we have learned how to open Buy/Sell operations for a given symbol (in this case, it's US30). However, in such scenario we are required to close those orders by using the method
Such strategy may be risky in case the market becomes too volatile (going up/down ward suddenly), it can result to a high gain, but there is also a possibility that it will lead to a high loss.
For this reason, it is recommended sometimes to manage risk using Take Profit and Stop Loss.
In fxcmpy, we can do so by specify two attributes in the
Note: PIP or Percentage In Points, is a unit that represents the smallest price move.
limit attribute is a positive integer value for the take profit amount, whereas
stop is the negative integer value for the stop loss amount.
Finally, closing the connection:
In this tutorial, you have learned:
For more details, you can check the official documentation of fxcmpy API.
If you want an in depth course which dives into more details in algorithmic trading, I would suggest you take Learn Algorithmic Trading with Python course, it is recommended by Investopedia, check it out!
Disclaimer: The content of this tutorial is purely explanatory and we are not responsible on how you may use it during live trading with a real account.
Happy Trading ♥View Full Code