Overall Statistics
```# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
#
# you may not use this file except in compliance with the License.
#
# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

from System import *
from QuantConnect import *
from QuantConnect.Data import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
from System.Collections.Generic import List
import decimal as d

### <summary>
### In this algorithm we demonstrate how to perform some technical analysis as
### part of your coarse fundamental universe selection
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="indicators" />
### <meta name="tag" content="universes" />
### <meta name="tag" content="coarse universes" />
class EmaCrossUniverseSelectionAlgorithm(QCAlgorithm):

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(2015,1,1)  #Set Start Date
self.SetEndDate(2015,1,3)    #Set End Date
self.SetCash(100000)           #Set Strategy Cash

self.UniverseSettings.Resolution = Resolution.Daily
self.UniverseSettings.Leverage = 2

self.coarse_count = 10
self.averages = { };

# this add universe method accepts two parameters:
# - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>

# sort the data by daily dollar volume and take the top 'NumberOfSymbols'
def CoarseSelectionFunction(self, coarse):

# We are going to use a dictionary to refer the object that will keep the moving averages
for cf in coarse:
if cf.Symbol not in self.averages:
self.averages[cf.Symbol] = SymbolData(cf.Symbol)

# Updates the SymbolData object with current EOD price
avg = self.averages[cf.Symbol]
avg.update(cf.EndTime, cf.Price)
self.Debug("Ticker = " + str(avg.symbol) + " , " +"Fast MA = " + str(avg.fast) + " , "+ "Slow = "+ str(avg.slow) + " , " + "Price = " + str(cf.Price) + " , " + "RSI =" + str(avg.rsi))
# Filter the values of the dict: we only want up-trending securities
values = list(filter(lambda x: x.is_uptrend, self.averages.values()))

# Sorts the values of the dict: we want those with greater difference between the moving averages
values.sort(key=lambda x: x.scale, reverse=True)

for x in values[:self.coarse_count]:
self.Log('symbol: ' + str(x.symbol.Value) + '  scale: ' + str(x.scale))

# we need to return only the symbol objects
return [ x.symbol for x in values[:self.coarse_count] ]

# this event fires whenever we have changes to our universe
def OnSecuritiesChanged(self, changes):
# liquidate removed securities
for security in changes.RemovedSecurities:
if security.Invested:
self.Liquidate(security.Symbol)

# we want 20% allocation in each security in our universe
self.SetHoldings(security.Symbol, 0.1)

class SymbolData(object):
def __init__(self, symbol):
self.symbol = symbol
self.tolerance = d.Decimal(1.01)
self.fast = ExponentialMovingAverage(100)
self.slow = ExponentialMovingAverage(300)
self.rsi = RelativeStrengthIndex(2)
self.is_uptrend = False
self.scale = 0

def update(self, time, value):
if self.fast.Update(time, value) and self.slow.Update(time, value) and self.rsi.Update(time, value):
fast = self.fast.Current.Value
slow = self.slow.Current.Value
rsi = self.rsi.Current.Value
self.is_uptrend = fast > slow * self.tolerance

if self.is_uptrend:
self.scale = (fast - slow) / ((fast + slow) / d.Decimal(2.0))```