The Contrarian Case for Getting Active

#!/usr/bin/env python
# coding: utf-8

# In[1]:


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

import pandas as pd
from pandas.io.json 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


# In[2]:


def exampleAuth():
    accountID, token = None, None
    with open("./oanda_account/account.txt") as I:
        accountID = I.read().strip()
    with open("./oanda_account/token.txt") as I:
        token = I.read().strip()
    return accountID, token


# In[3]:


instrument = "AUD_USD"

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

current_time = datetime.datetime.now()
twentyfour_hours_ago = current_time - datetime.timedelta(days=2)

print (current_time)
print (twentyfour_hours_ago)


# In[4]:


#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")


# In[5]:


#Connect to tokens
accountID, access_token = exampleAuth()
client = opy.API(access_token=access_token)


# In[6]:


# The v20 api handles from times a little differently - be careful of the timezone
params={"from": tf,
        "to": ct,
        "granularity":'M1',
        "price":'A'}
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)

df.info()
df.head()


# In[7]:


df['returns'] = np.log(df['closeAsk'] / df['closeAsk'].shift(1))

cols = []

for momentum in [15, 30, 60, 120]:
    col = 'position_%s' % momentum
    df[col] = np.sign(df['returns'].rolling(momentum).mean())
    cols.append(col)


# In[8]:


get_ipython().run_line_magic('matplotlib', 'inline')
import seaborn as sns; sns.set()

strats = ['returns']

for col in cols:
    strat = 'strategy_%s' % col.split('_')[1]
    df[strat] = df[col].shift(1) * df['returns']
    strats.append(strat)


# In[9]:


df[strats].dropna().cumsum().apply(np.exp).plot()


# In[10]:


from oandapyV20.endpoints.pricing import PricingStream
import oandapyV20.endpoints.orders as orders
from oandapyV20.contrib.requests import MarketOrderRequest
from oandapyV20.exceptions import V20Error, StreamTerminated
import oandapyV20.endpoints.trades as trades
from oandapyV20.contrib.requests import TrailingStopLossOrderRequest

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 = API(access_token=config['oanda']['access_token'])
        self.client = opy.API(access_token=access_token)

    def create_order(self, units):
        #order = orders.OrderCreate(accountID=config['oanda']['account_id'], data=MarketOrderRequest(instrument="EUR_USD", units=units).data)
        order = orders.OrderCreate(accountID=accountID, data=MarketOrderRequest(instrument=instrument, units=units).data)
        response = self.client.request(order)
        print('\t', response)

        #create a stop might need it's own fuction and called under the trading stragety part
        #stp = pd.Series(response, name='stop')
        #stp_id = stp.orderFillTransaction.get('tradeOpened').get('tradeID')
        #print ('\t The tradeID is ', stp_id)
        #stp_vwap = (stp.orderFillTransaction.get('fullVWAP') - 0.00010
        #print ('\t The stop loss value is ', stp_vwap)
        #stp_order = orders.OrderCreate(accountID=accountID, data=StopLossOrderRequest(tradeID=stp_id, price=stp_vwap))
        #response_stp_order = self.client.request(stp_order)

        #create a trailing stop
        #This below is for a trailing stop that works but the distance is key, this should be set for breakouts
        #tsl = pd.Series(response, name='junk')
        #tsl_id = tsl.orderFillTransaction.get('tradeOpened').get('tradeID')
        #print('The trade ID is: ', tsl_id)
        #tsl_order = orders.OrderCreate(accountID=accountID, data=TrailingStopLossOrderRequest(tradeID=tsl_id, distance=5).data)
        #response_tsl = self.client.request(tsl_order)
        #print('\t', response_tsl)


    def on_success(self, data):
        self.ticks += 1
        print("ticks=",self.ticks)
        # 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']}],
                                 index=[data["time"]]))
        # 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()

        #print ('\t this is the dfr', dfr)

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

        #print('/t returns: ', dfr['returns'])

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

        print("position=",dfr['position'].iloc[-1])

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

    def disconnect(self):
        self.connected=False

    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:
                    break
                if not (ignore_heartbeat and tick["type"]=="HEARTBEAT"):
                    print(tick)
                    self.on_success(tick)


# In[11]:


# Set momentum to be the number of previous 60 second intervals to calculate against
# For two hours, momentum = 120. For 15 minutes, set to 15
mt = MomentumTrader(momentum=15,accountID=accountID,params={'instruments': instrument})
print (mt)


# In[12]:


mt.rates(account_id=accountID, instruments=instrument, ignore_heartbeat=True)