Overall Statistics Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio 0 Tracking Error 0 Treynor Ratio 0 Total Fees \$0.00
```import numpy as np
import datetime
from scipy import stats

### <summary>
### Basic template algorithm simply initializes the date range and cash. This is a skeleton
### framework you can use for designing an algorithm.
### </summary>
class StocksOnTheMove(QCAlgorithm):
'''Basic template algorithm simply initializes the date range and cash'''

def Initialize(self):
'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

self.SetStartDate(2016,1,2)  #Set Start Date
self.SetEndDate(2016,1,4)    #Set End Date
self.SetCash(300000)           #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data

# what resolution should the data *added* to the universe be?
self.UniverseSettings.Resolution = Resolution.Minute

# How many stocks in the starting universe?
#self.__numberOfSymbols = 700
self.__numberOfSymbols = 20

# How many stocks in the portfolio?
self.number_stocks = 5

# this add universe method accepts two parameters:

# How far back are we looking for momentum?
self.momentum_period = 30

# Set ATR window
self.atr_window = 20
#self.SetWarmUp(self.atr_window)

# Schedule Indicator Update, Ranking + Rebal
self.Schedule.On(self.DateRules.EveryDay("SPY"),
self.TimeRules.AfterMarketOpen("SPY", 30),
Action(self.rebalance))

self.Schedule.On(self.DateRules.EveryDay("SPY"),
self.TimeRules.BeforeMarketClose("SPY", 0),
Action(self.UpdateIndicators))

# Set empty list for universe
self.universe = []

# Set empty dictionary for managing & ranking the slope
self.indicators_r2 = {}
self.atr = {}

self.last_month_fired_coarse    = None #we cannot rely on Day==1 like before
self.last_month_fired_rebalance = None #we cannot rely on Day==1 like before

def UpdateIndicators(self):

# This updates the indicators at each data step
for symbol in self.universe:

# is symbol iin Slice object? (do we even have data on this step for this asset)
if self.Securities.ContainsKey(symbol):
# Update the dictionary for the indicator
if symbol in self.indicators_r2:
self.indicators_r2[symbol].update(self.Securities[symbol].Price)

# Run a coarse selection filter for starting universe
def CoarseSelectionFunction(self, coarse):

today = self.Time
#self.Log("Day = {} Month = {}".format(today.day,today.month))

# Set the Universe to rebalance on the 1st day of each quarter (can play around with this as required)
if self.last_month_fired_coarse != today.month and (today.month == 1 or today.month == 4 or today.month == 7 or today.month == 10):
self.last_month_fired_coarse = today.month

self.Log("Day = {} Month = {}".format(today.day,today.month))

CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData]
sortedByDollarVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=True)
result = [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ]
self.universe = result
return self.universe
else:
return self.universe

def OnSecuritiesChanged(self, changes):

# Delete indicator from the dict to save Ram
for security in changes.RemovedSecurities:
if security.Symbol in self.indicators_r2:
del self.indicators_r2[security.Symbol]
self.Liquidate(security.Symbol)

# Init a new custom indicator
self.indicators_r2[security.Symbol] = RegressionSlope(self, security.Symbol, self.momentum_period,  Resolution.Daily)

def rebalance(self):

self.target_portfolio = None
today = self.Time
if self.last_month_fired_rebalance != self.last_month_fired_coarse:
# ensure we are fireing after coarse
self.last_month_fired_rebalance = self.last_month_fired_coarse

self.Log("Rebalance")

# get values from dict
symbols, slopes = zip(*[(symbol, self.indicators_r2[symbol].value) \
for symbol in self.indicators_r2 \
if self.indicators_r2[symbol].value is not None])

# sort
idx_sorted = np.argsort(slopes)[::-1] # [::-1] slices backwards i.e. flips to reverse the sort order
symbols = np.array(symbols)[idx_sorted]
slopes = np.array(slopes)[idx_sorted]

# Sort the Dictionary from highest to lowest and take the top values
self.target_portfolio = symbols
self.atr = symbols
#self.Log(str(self.target_portfolio))
self.Log("TARGET PORTFOLIO: {}:".format(str(self.target_portfolio)))

'''
HAVING ISSUES GETTING THE BELOW COMMENTED-OUT LINE 137 to RUN
'''
# Get ATR for the symbol
for symbol in self.target_portfolio:
#self.atr[symbol] = self.target_portfolio.get_atr()
#self.atr[symbol] = SymbolData.get_atr(symbol, self)
continue
'''
Seems that self.target_portfolio is generating QC symbols rather than a list of tickers as string & hence is not indexable. Not sure how to fix yet.
I'm also not sure if ATR would need updating as I only need it once per month for rebalance
'''
self.Log("TARGET PORTFOLIO: {} : ATRS : {}".format(str(self.target_portfolio,self.atr[symbol])))

# new symbol? setup indicator object. Then update
#if symbol not in self.indicators:
#    self.indicators[symbol] = SymbolData(symbol, self, self.atr_window)
#self.indicators[symbol].update(data[symbol])

# Enter or exit positions
for symbol in self.universe:

#k = 1/np.sum(self.atr)
# Case: invested in the current symbol
if self.Portfolio[symbol].HoldStock:
# Exit if not a target aset
if symbol not in self.target_portfolio:
self.Liquidate(symbol)
elif symbol in self.target_portfolio:
#k = 1/sum(self.atr.values())
#self.SetHoldings(symbol, k/float(self.atr[symbol]))
continue

# Case: not invested in the current symbol
else:
# symbol is a target, enter position
if symbol in self.target_portfolio:

# Update ATR for the stock in the new dictionary
#self.atr[symbol].update_bar(self.Time, data[symbol].Price)

self.Log("{} {} {}".format(symbol, self.Securities[symbol].Price, self.indicators_r2[symbol].value))

# Send Orders - Equal Weighted
#self.SetHoldings(symbol, 0.99/float(self.number_stocks))
# Send Orders - Volatility Weighted
#k = 1/sum(self.atr.values())
#self.SetHoldings(symbol, k/float(self.atr[symbol]))

class RegressionSlope():

def __init__(self, algo, symbol, window, resolution):
# set up params of per-asset rolling metric calculation
self.symbol = symbol
self.window = window
self.resolution = resolution

# the value we access, None until properly calulated
self.value = None

# We will store the historical window here, and keep it a fixed length in update
self.history = []

# download the window. Prob not great to drag algo scope in here. Could get outside and pass in.
hist_df = algo.History([symbol], window, self.resolution)

# Case where no data to return for this asset. New asset?
if 'close' not in hist_df.columns:
return

# store the target time series
self.history = hist_df.close.values

# calulate the metrics for the current window
self.compute()

def update(self, value):
# update history, retain length
self.history = np.append(self.history, float(value))[1:]

# calulate the metrics for the current window
self.compute()

def compute(self):

# Case where History faiiled to return window, waiting to acrew
# prevent calc until window is statisfied
if len(self.history) < self.window:
return

# copied from previous
x = np.arange(len(self.history))
log_ts = np.log(self.history)
slope, intercept, r_value, p_value, std_err = stats.linregress(x, log_ts)
annualized_slope = (np.power(np.exp(slope), 250) - 1) * 100
annualized_slope = annualized_slope * (r_value ** 2)

# update value
self.value = annualized_slope

class SymbolData(object):
def __init__(self, symbol, context, window):
self.symbol = symbol
"""
I had to pass ATR from outside object to get it to work, could pass context and use any indica
var atr = ATR(Symbol symbol, int period, MovingAverageType type = null, Resolution resolution = null, Func`2[Data.IBaseData,Data.Market.IBaseDataBar] selector = null)
"""
self.window    = window
self.indicator = context.ATR(symbol, self.window)
self.atr       = 0.0

"""
Runtime Error: Python.Runtime.PythonException: NotSupportedException : AverageTrueRange does not support Update(DateTime, decimal) method overload. Use Update(IBaseDataBar) instead.
"""
def update(self, bar):
self.indicator.Update(bar)

def get_atr(self):
return self.indicator.Current.Value```