Python Forex Trading Bot

I have had some time to continue on my Python Forex Trading Bot now that we're all self isolating. This is purely for educational purposes because when I run this sucker, it loses money. Not so much anymore but it's not profitable. The reason why I say 'educational purposes' is that coding is not my first choice of career and I teach myself as I go along. Coding's been very profitable in other parts of my life and I use it to get $hit done.

I now understand the concept of Classes, which is great because it makes pieces of code very 'pluggable.' Originally I thought I could write a set of functions in the MomentumTrader class that would serve as my Stop and Trailing Stop orders. I did do that only to find out that I was creating those orders AFTER the trade and when the Bot would try to close out or add to the position (as it does because it's a mean reversion strategy) it would sometimes crash. This led me to find a set classes in the API called onFill. This eliminated the need for me to create the order first and THEN add in a stop or trailing stop. I was able to do it once the trade was filled. The moral of the story, you should really understand your API classes.

Overall the API extended by Feite is quite robust and powerful, but it's still very hard to make any money with this thing. Although I've been whining about getting active again, the reality is that the long term wins.

I'll continue to test this over the course of the next few weeks using an Oanda Practice Account but I think I'm going to write a new class that best mimics my current Forex trading style instead. I use the Daily chart, trade pairs where I make $ from the carry trade, and do a long term trend play. That's the beauty of Forex, you can see some great long trends if you zoom out.

My discretionary trading system does have some flaws. I usually get the entry wrong and have to place a second trade to 'scale in.' It's something I don't like doing because it means more risk. I also need to work on proper risk management as well. Right now I don't use stops and I routinely take on 200 pip swings. This has worked out for me because 99% of the time I trade the EURUSD pair, which has been in a long downward trend. I usually make a short entry, then the price turns against me and goes higher, then I place another short entry where the price stabilizes. I think I've been very lucky until now and my trading metrics and expectancy are positive. Still, I feel like I leave a lot to chance and I'd like to size my position accordingly, make better entries, and use better risk management.

Current Python Forex Trading Bot

So here's the latest incarnation of the Bot. I spent some time clean it up and adding in a trailingstop onfill function.

#Install Py Package from:

import json
import oandapyV20 as opy
import oandapyV20.endpoints.instruments as instruments
from oandapyV20.contrib.factories import InstrumentsCandlesFactory

import pandas as pd
from import json_normalize

from oandapyV20.exceptions import V20Error, StreamTerminated
from oandapyV20.endpoints.transactions import TransactionsStream
from oandapyV20.endpoints.pricing import PricingStream
from oandapyV20.contrib.requests import TrailingStopLossOrderRequest

import datetime
from dateutil import parser

import numpy as np
def exampleAuth():
    accountID, token = None, None
    with open("./oanda_account/account.txt") as I:
        accountID =
    with open("./oanda_account/token.txt") as I:
        token =
    return accountID, token
instrument = "EUR_USD"

#Set time functions to offset chart
today =
two_years_ago = today - datetime.timedelta(days=720)

current_time =

twentyfour_hours_ago = current_time - datetime.timedelta(hours=12)
print (current_time)
print (twentyfour_hours_ago)
#Create time parameter for Oanada call
ct = current_time.strftime("%Y-%m-%dT%H:%M:%SZ")
tf = twentyfour_hours_ago.strftime("%Y-%m-%dT%H:%M:%SZ")
#Connect to tokens
accountID, access_token = exampleAuth()
client = opy.API(access_token=access_token)
params={"from": tf,
        "to": ct,
r = instruments.InstrumentsCandles(instrument=instrument,params=params)
#Do not use client from above
data = client.request(r)
results= [{"time":x['time'],"closeAsk":float(x['ask']['c'])} for x in data['candles']]
df = pd.DataFrame(results).set_index('time')

df.index = pd.DatetimeIndex(df.index)
from oandapyV20.endpoints.pricing import PricingStream
import oandapyV20.endpoints.orders as orders
from oandapyV20.contrib.requests import MarketOrderRequest, TrailingStopLossDetails, TakeProfitDetails
from oandapyV20.exceptions import V20Error, StreamTerminated
import oandapyV20.endpoints.trades as trades

class MomentumTrader(PricingStream): 
    def __init__(self, momentum, *args, **kwargs): 
        PricingStream.__init__(self, *args, **kwargs)
        self.ticks = 0 
        self.position = 0
        self.df = pd.DataFrame()
        self.momentum = momentum
        self.units = 1000
        self.connected = False
        self.client = opy.API(access_token=access_token)

    def create_order(self, units):
        #You can write a custom distance value here, so distance = some calculation

        trailingStopLossOnFill = TrailingStopLossDetails(distance=0.0005)

        order = orders.OrderCreate(accountID=accountID, 
        response = self.client.request(order)
        print('\t', response)

    def on_success(self, data):
        self.ticks += 1
        # print(self.ticks, end=', ')

        # appends the new tick data to the DataFrame object
        self.df = self.df.append(pd.DataFrame([{'time': data['time'],'closeoutAsk':data['closeoutAsk']}],

        #transforms the time information to a DatetimeIndex object
        self.df.index = pd.DatetimeIndex(self.df["time"])

        # Convert items back to numeric (Why, OANDA, why are you returning strings?)
        self.df['closeoutAsk'] = pd.to_numeric(self.df["closeoutAsk"],errors='ignore')

        # resamples the data set to a new, homogeneous interval, set this from '5s' to '1m'
        dfr = self.df.resample('60s').last().bfill()

        # calculates the log returns
        dfr['returns'] = np.log(dfr['closeoutAsk'] / dfr['closeoutAsk'].shift(1))

        # derives the positioning according to the momentum strategy
        dfr['position'] = np.sign(dfr['returns'].rolling(self.momentum).mean())


        if dfr['position'].iloc[-1] == 1:
            print("go long")
            if self.position == 0:

            elif self.position == -1:
                self.create_order(self.units * 2)
            self.position = 1

        elif dfr['position'].iloc[-1] == -1:
            print("go short")
            if self.position == 0:

            elif self.position == 1:
                self.create_order(-self.units * 2)

            self.position = -1

        if self.ticks == 25000:
            print("close out the position")
            if self.position == 1:
            elif self.position == -1:

    def disconnect(self):

    def rates(self, account_id, instruments, **params):
        self.connected = True
        params = params or {}
        ignore_heartbeat = None
        if "ignore_heartbeat" in params:
            ignore_heartbeat = params['ignore_heartbeat']
        while self.connected:
            response = self.client.request(self)
            for tick in response:
                if not self.connected:
                if not (ignore_heartbeat and tick["type"]=="HEARTBEAT"):
# Set momentum to be the number of previous 5 second intervals to calculate against

mt = MomentumTrader(momentum=60,accountID=accountID,params={'instruments': instrument})
print (mt)
mt.rates(account_id=accountID, instruments=instrument, ignore_heartbeat=True)

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