This may come easy to some of you, but I am new and trying to learn by reading the tuts and editing code. I am trying to figure out how to import vix price and set it to not trade once vix hits a certain price. This is the original code that I am trying to add to. I might have a slight idea of it, but I don't want to screw it up and start of learning the wrong way. Any help or direction is appreciated, thanks!
Credit Peter Bakker for this original and Eric Tjossem edit.
import numpy as np
import math
from pytz import timezone
import datetime
from sqlalchemy import or_
import scipy.stats as stats
import talib as ta
def initialize(context):
set_symbol_lookup_date('2016-05-01')
log.info(get_environment('arena'))
if get_environment('arena') == 'backtest':
context.backtest_mode = True
log.info('We are backtesting.')
else:
context.backtest_mode = False
# Keeping track of the last sale that you have.
context.last_sale = None
context.stocks = ''
context.spy = symbol('SPY')
context.sh = symbol('SH')
context.assets = [context.spy]
context.history_close = None
context.history_max = None
context.history_min = None
context.lookback = 62
context.smoothing = 40 #40 is standard
context.holdtilpositive = False
context.holdtilnegative = False
context.buytomorrow = False
context.threshold = 0.0035 #5% is standard
context.hilo_index = []
context.hilo_MAindex = []
context.slope_index = []
context.spyslope_index = []
context.current_hilo = -10
context.m = len(context.stocks)
context.b_t = np.ones(context.m) / context.m
context.eps = 0.75 # change epsilon here
context.init = False
context.previous_datetime = None
#for making the history available in before trade start:
schedule_function(Algo_hilo, date_rule=date_rules.every_day(),
time_rule=time_rules.market_close(minutes=5))
# schedule_function(daily, date_rule=date_rules.week_start(2),
# time_rule=time_rules.market_open(minutes=30)
# Set a trading guard that will prevent any short positions
# from being placed. This is to insure that algorithms that
# depend on short positions are not accidently deployed.
set_long_only()
def handle_data(context, data):
# Because most Robinhood accounts are cash accounts,
# trades(and most other brokerages) settle
# on a T+3 settlement date. This snippet of code prevents
# a trade from happening when you still have unsettled cash
# by checking if total cash (settled & unsettled) matches your
# settled cash amount.
# [IMPORTANT] During backtests, `settled_cash` will always equal
# `cash`. In order to simulate a similar check, please also
# incorporate `simulate_cash_settlement` in handle data as you will
# see in this algorithm.
# For live trading only:
# if do_unsettled_funds_exist(context):
# return
# `cash_settlement_date` simulates a T+3 settlement date. This
# should be used at the beginning of any handle_data or method
# used for schedule_function that places orders. At the end of
# of that method should be `check_last_sale`. Only for
# backtesting purposes!
# `check_last_sale` is what `cash_settlement_date` needs in
# order to work properly. Only for backtesting purposes!
pass
def do_unsettled_funds_exist(context):
"""
For Robinhood users. In order to prevent you from attempting
to trade on unsettled cash (settlement dates are T+3) from
sale of proceeds. You can use this snippet of code which
checks for whether or not you currently have unsettled funds
To only be used for live trading!
"""
if context.portfolio.cash != context.account.settled_cash:
return True
def check_last_sale(context):
"""
To be used at the end of each bar. This checks if there were
any sales made and sets that to `context.last_sale`.
`context.last_sale` is then used in `cash_settlement_date` to
simulate a T+3 Cash Settlement date
To only be used for backtesting!
"""
open_orders = get_open_orders()
most_recent_trade = []
# If there are open orders check for the most recent sale
if open_orders:
for sec, order in open_orders.iteritems():
for oo in order:
if oo.amount < 0:
most_recent_trade.append(oo.created)
if len(most_recent_trade) > 0:
context.last_sale = max(most_recent_trade)
def cash_settlement_date(context):
"""
This will simulate Robinhood's T+3 cash settlement. If the
most recent sale is less than 3 trading days from the current
day, assume we have unsettled funds and exit
To only be used for backtesting!
"""
if context.last_sale and (get_datetime() - context.last_sale).days < 3:
return True
def Algo_hilo(context, data):
cash = context.portfolio.cash
leverage = context.account.leverage
context.history_close = data.history(context.assets, 'price', 202, '1d')
#Calc spy slope
x1 = list(xrange(22))
spyslope, spyintercept, psyr_value, spyp_value, spystd_err = stats.linregress(x1,context.history_close[context.spy][-22:])
context.spyslope_index.append(spyslope)
#Calc zscore of spy slope
z_spyslope= stats.zscore(context.spyslope_index, axis=0, ddof=1)[-1]
accspyslope = 0
#Calc accelleration of spy
if len(context.spyslope_index) > 5:
x2 = list(xrange(5))
accspyslope, accspyintercept, accpsyr_value, accspyp_value, accspystd_err = stats.linregress(x2,context.spyslope_index[-5:])
#Calc long and short mavg spy
mavg_short = np.mean(context.history_close[context.spy][-22:])
mavg_med = np.mean(context.history_close[context.spy][-122:])
mavg_long = np.mean(context.history_close[context.spy][-200:])
current_price = context.history_close[context.spy][-1]
#get maxes and minimums
context.history_max = context.history_close.idxmax(axis=0, skipna=True)
context.history_min = context.history_close.idxmin(axis=0, skipna=True)
minctr = 0
maxctr = 0
stockctr = len(context.history_close.columns)
#find number of hi's and Lows
for stock in context.history_max.index:
datemax = context.history_max[stock]
if str(type(datemax))=="<class 'pandas.tslib.Timestamp'>" and datemax.date() == get_datetime().date(): maxctr += 1
for stock in context.history_min.index:
datemin = context.history_min[stock]
if str(type(datemin))=="<class 'pandas.tslib.Timestamp'>" and datemin.date() == get_datetime().date(): minctr += 1
hi = float(maxctr)/stockctr
lo = float(minctr)/stockctr
ratio = (float(maxctr - minctr))/stockctr
context.hilo_index.append(ratio)
#calc zscore of ratio
z_ratio= stats.zscore(context.hilo_index[-context.smoothing:], axis=0, ddof=1)[-1]
ma_ratio = np.mean(context.hilo_index[-context.smoothing:])
context.hilo_MAindex.append(ma_ratio)
context.current_hilo = ratio
#calc slope of MA HiLo index
if len(context.hilo_MAindex)> 7:
x = list(xrange(7))
slope, intercept, r_value, p_value, std_err = stats.linregress(x,context.hilo_MAindex[-7:])
else:
slope = 0
p_value =0
context.slope_index.append(slope)
if len(context.slope_index) > 7:
slope_ma = np.mean(context.slope_index[-7:])
else:
slope_ma = 0
if mavg_long > current_price:
record(mavg_long_vs_price=-1)
else:
record(mavg_long_vs_price=1)
if context.holdtilpositive:
record(holdtilpositive= 1)
elif context.holdtilnegative:
record(holdtilpositive= -1)
else:
record(holdtilpositive=0)
record(zspyslope = z_spyslope)
record(zratio_hi_lo = z_ratio)
record(slopespy = spyslope)
if (spyslope > 0 and slope_ma > 0) and context.holdtilpositive:
context.holdtilpositive = False
elif spyslope < 0 and context.holdtilnegative:
context.holdtilnegative = False
no_spys = context.portfolio.positions[context.spy].amount
if context.buytomorrow:
order_target_percent(context.sh, 0)
order_target_percent(context.spy, 1)
context.buytomorrow = False
return
if z_spyslope > 0:
if no_spys==0: log.info('Buying: z score of the Slope of SPY over 22 days > 0: we are moving up')
context.buytomorrow = True #Buy with delay of 1 day
context.holdtilnegative = True
return
elif z_spyslope < -.7:
if no_spys==0: log.info('Buying: z score of the Slope of SPY over 22 days < -0.7: mean reverting')
order_target_percent(context.sh, 0)
order_target_percent(context.spy, 1)
context.holdtilpositive = True
return
elif (z_spyslope < 0 and not context.holdtilpositive) or mavg_long > current_price or (z_spyslope > 1.0 or z_ratio > 1.5):
if no_spys>0:
if mavg_long > current_price:
log.info('Selling: price below Long mavg 122 days')
order_target_percent(context.sh, 1)
if z_spyslope > 1.0:
log.info('Selling: z score of the Slope of SPY over 22 days > 1.0')
order_target_percent(context.sh, 1)
if z_ratio > 1.5:
log.info('Selling: z score of the Ratio HILO of SPY over 22 days > 1.5')
order_target_percent(context.sh, 1)
if z_spyslope < 0 and not context.holdtilpositive: log.info('Selling: z score of the Slope of SPY over 22 days < -0.0 and not holding till positive')
order_target_percent(context.spy, 0)
return
return
