Selection of Stocks into a Portfilio
In the current document we show how you can pick the best stock and strategy combinations out of a big collecion from a universe.
Setup
Import Libraries
%classpath config resolver maven-public http://software.pschatzmann.ch/repository/maven-public/
%classpath add mvn ch.pschatzmann:investor:0.9-SNAPSHOT
%classpath add mvn ch.pschatzmann:jupyter-jdk-extensions:0.0.1-SNAPSHOT
Imports
// our stock evaluation framwork
import ch.pschatzmann.dates._;
import ch.pschatzmann.stocks._;
import ch.pschatzmann.stocks.data.universe._;
import ch.pschatzmann.stocks.input._;
import ch.pschatzmann.stocks.accounting._;
import ch.pschatzmann.stocks.accounting.kpi._;
import ch.pschatzmann.stocks.execution._;
import ch.pschatzmann.stocks.execution.fees._;
import ch.pschatzmann.stocks.execution.price._;
import ch.pschatzmann.stocks.parameters._;
import ch.pschatzmann.stocks.strategy._;
import ch.pschatzmann.stocks.strategy.optimization._;
import ch.pschatzmann.stocks.strategy.allocation._;
import ch.pschatzmann.stocks.strategy.selection._;
import ch.pschatzmann.stocks.integration._;
import ch.pschatzmann.stocks.integration.ChartData.FieldName._;
import ch.pschatzmann.stocks.strategy.OptimizedStrategy.Schedule._;
// java
import java.util.stream.Collectors;
import java.util._;
//import java.util.function.Consumer;
// log4j
import org.apache.log4j._;
/// jupyter custom displayer
import ch.pschatzmann.display.Displayers
import ch.pschatzmann.dates._
import ch.pschatzmann.stocks._
import ch.pschatzmann.stocks.data.universe._
import ch.pschatzmann.stocks.input._
import ch.pschatzmann.stocks.accounting._
import ch.pschatzmann.stocks.accounting.kpi._
import ch.pschatzmann.stocks.execution._
import ch.pschatzmann.stocks.execution.fees._
import ch.pschatzmann.stocks.execution.price._
import ch.pschatzmann.stocks.parameters._
import ch.pschatzmann.stocks.strategy._
import ch.pschatzmann.stocks.strategy.optimization._
import ch.pschatzmann.stocks.strategy.allocation._
import ch.pschatzmann.stocks.strategy.selection._
import ch.pschatzmann.stocks.integration._
import ch.pschatzmann.stocks.integration.ChartData.FieldName._
import ch.pschatzmann.stocks.strategy.OptimizedStrategy.Schedule._
import java.util.stream...
Deactivate Logging and Caching
var root = Logger.getRootLogger();
root.setLevel(Level.ERROR);
Context.setCachingActive(false);
Context.isCachingActive();
false
Selection of all Stocks and Strategies
First we need to specify the strategies that we want to use for the evaluation. We just use all:
TradingStrategyFactory.list()
[CCICorrectionStrategy, GlobalExtremaStrategy, MovingMomentumStrategy, RSI2Strategy, BuyAndHoldStrategy]
For defining the selection of stocks we use a universe
new MarketUniverse("NASDAQ")
MarketUniverese: exchange=NASDAQ ticker=.*
and we define the optimization period as 2016-01-01-2016-21-31 and the trading period as
2017-01-01 to today:
Context.getDateRanges("2016-01-01","2017-01-01");
[20160101-20161231, 20170101-20180527]
Run the Selection – 1st Attempt
We define a StrategySelector and feed it to the StockSelector.
The optional Restartable parameter is making sure that we save the temporary result every 50 records so that we do not need to reprocess them when we need to restart the functionality.
We store the result in a file and make sure that we do not run the selection again if the file already exists.
// setup of the model
var periods = Context.getDateRanges("2016-01-01","2017-01-01");
var account = new Account("Simulation", "USD", 100000.00, periods.get(0).getStart(), new PerTradeFees(100.00))
var file = new java.io.File("NASDAQ-2017-01-01.json")
if (!file.exists()) {
var universe = new MarketUniverse("NASDAQ")
var strategies = TradingStrategyFactory.list()
var strategySelector = new StrategySelector(account, strategies, periods.get(0), KPI.AbsoluteReturn)
var stockSelector = new StockSelector(strategySelector, new Restartable("restart-v1.ser",50))
// calculate the result
var result = stockSelector.getSelection(200, universe, new MarketArchiveHttpReader())
// save the result to a file
result.save(file)
}
// provide result even if we did not run the selection above
var result = new SelectionResult()
result.load(file)
"**END**"
**END**
Evaluate the Selection
Here is the result of the selection:
Displayers.display(Context.tail(result.getResult(),10));
input |
result |
stockID |
strategyName |
optimized |
Key |
Value |
parameters |
{NumberOfTicks={value=7.0, range={min=1, max=500}}, BuyFactor={value=1.0, range={min=1.001, max=5.0}}, SellFactor={value=1.0, range={min=0.005, max=0.999}}} |
|
Key |
Value |
parameters |
{RealizedGains={value=28314.0, range=null}, Cash={value=10.0, range=null}, ReturnPercent={value=20.0, range=null}, NumberOfBuys={value=7.0, range=null}, ReturnPercentAnualized={value=10.0, range=null}, MaxDrawDownHighValue={value=148193.0, range=null}, NumberOfCashTransfers={value=1.0, range=null}, AbsoluteReturnStdDev={value=2483.0, range=null}, MaxDrawDownNumberOfDays={value=185.0, range=null}, AbsoluteReturnAvaragePerDay={value=39.0, range=null}, NumberOfSells={value=6.0, range=null}, ActualValue={value=120332.0, range=null}, UnrealizedGains={value=-6683.0, range=null}, PurchasedValue={value=127014.0, range=null}, MaxDrawDownPercent={value=68951.0, range=null}, NumberOfTradedStocks={value=1.0, range=null}, MaxDrawDownLowValue={value=79242.0, range=null}, AbsoluteReturn={value=20332.0, range=null}, ReturnPurcentStdDev={value=0.0, range=null}, SharpeRatio={value=0.0, range=null}, NumberOfTrades={value=13.0, range=null}, TotalFees={value=1300.0, range=null}} |
|
Key |
Value |
ticker |
MYRG |
exchange |
NASDAQ |
|
GlobalExtremaStrategy |
false |
Key |
Value |
parameters |
{SignalEMA={value=18.0, range={min=16, max=20}}, EntrySignal={value=20.0, range={min=10, max=30}}, ExitSignal={value=80.0, range={min=70, max=90}}, ShortEMAPeriod={value=9.0, range={min=1, max=20}}, StochasticOscillatorKIndicator={value=14.0, range={min=7, max=21}}, LongEMAPeriod={value=26.0, range={min=20, max=40}}} |
|
Key |
Value |
parameters |
{RealizedGains={value=20387.0, range=null}, Cash={value=120187.0, range=null}, ReturnPercent={value=20.0, range=null}, NumberOfBuys={value=1.0, range=null}, ReturnPercentAnualized={value=10.0, range=null}, MaxDrawDownHighValue={value=103986.0, range=null}, NumberOfCashTransfers={value=1.0, range=null}, AbsoluteReturnStdDev={value=1070.0, range=null}, MaxDrawDownNumberOfDays={value=56.0, range=null}, AbsoluteReturnAvaragePerDay={value=41.0, range=null}, NumberOfSells={value=1.0, range=null}, ActualValue={value=120187.0, range=null}, UnrealizedGains={value=0.0, range=null}, PurchasedValue={value=120187.0, range=null}, MaxDrawDownPercent={value=14888.0, range=null}, NumberOfTradedStocks={value=1.0, range=null}, MaxDrawDownLowValue={value=89098.0, range=null}, AbsoluteReturn={value=20187.0, range=null}, ReturnPurcentStdDev={value=0.0, range=null}, SharpeRatio={value=1.0, range=null}, NumberOfTrades={value=2.0, range=null}, TotalFees={value=200.0, range=null}} |
|
Key |
Value |
ticker |
TMUSP |
exchange |
NASDAQ |
|
MovingMomentumStrategy |
false |
Key |
Value |
parameters |
{SignalEMA={value=18.0, range={min=16, max=20}}, EntrySignal={value=20.0, range={min=10, max=30}}, ExitSignal={value=80.0, range={min=70, max=90}}, ShortEMAPeriod={value=9.0, range={min=1, max=20}}, StochasticOscillatorKIndicator={value=14.0, range={min=7, max=21}}, LongEMAPeriod={value=26.0, range={min=20, max=40}}} |
|
Key |
Value |
parameters |
{RealizedGains={value=20333.0, range=null}, Cash={value=120133.0, range=null}, ReturnPercent={value=20.0, range=null}, NumberOfBuys={value=1.0, range=null}, ReturnPercentAnualized={value=10.0, range=null}, MaxDrawDownHighValue={value=118822.0, range=null}, NumberOfCashTransfers={value=1.0, range=null}, AbsoluteReturnStdDev={value=947.0, range=null}, MaxDrawDownNumberOfDays={value=48.0, range=null}, AbsoluteReturnAvaragePerDay={value=39.0, range=null}, NumberOfSells={value=1.0, range=null}, ActualValue={value=120133.0, range=null}, UnrealizedGains={value=0.0, range=null}, PurchasedValue={value=120133.0, range=null}, MaxDrawDownPercent={value=11666.0, range=null}, NumberOfTradedStocks={value=1.0, range=null}, MaxDrawDownLowValue={value=107156.0, range=null}, AbsoluteReturn={value=20133.0, range=null}, ReturnPurcentStdDev={value=0.0, range=null}, SharpeRatio={value=1.0, range=null}, NumberOfTrades={value=2.0, range=null}, TotalFees={value=200.0, range=null}} |
|
Key |
Value |
ticker |
NICE |
exchange |
NASDAQ |
|
MovingMomentumStrategy |
false |
Key |
Value |
parameters |
{NumberOfTicks={value=7.0, range={min=1, max=500}}, BuyFactor={value=1.0, range={min=1.001, max=5.0}}, SellFactor={value=1.0, range={min=0.005, max=0.999}}} |
|
Key |
Value |
parameters |
{RealizedGains={value=20730.0, range=null}, Cash={value=120130.0, range=null}, ReturnPercent={value=20.0, range=null}, NumberOfBuys={value=3.0, range=null}, ReturnPercentAnualized={value=10.0, range=null}, MaxDrawDownHighValue={value=115644.0, range=null}, NumberOfCashTransfers={value=1.0, range=null}, AbsoluteReturnStdDev={value=1114.0, range=null}, MaxDrawDownNumberOfDays={value=88.0, range=null}, AbsoluteReturnAvaragePerDay={value=39.0, range=null}, NumberOfSells={value=3.0, range=null}, ActualValue={value=120130.0, range=null}, UnrealizedGains={value=0.0, range=null}, PurchasedValue={value=120130.0, range=null}, MaxDrawDownPercent={value=15780.0, range=null}, NumberOfTradedStocks={value=1.0, range=null}, MaxDrawDownLowValue={value=99863.0, range=null}, AbsoluteReturn={value=20130.0, range=null}, ReturnPurcentStdDev={value=0.0, range=null}, SharpeRatio={value=1.0, range=null}, NumberOfTrades={value=6.0, range=null}, TotalFees={value=600.0, range=null}} |
|
Key |
Value |
ticker |
MGPI |
exchange |
NASDAQ |
|
GlobalExtremaStrategy |
false |
Key |
Value |
parameters |
{EntryLimit={value=5.0, range={min=2, max=10}}, RSIPeriod={value=2.0, range={min=1, max=5}}, LongSMAPeriod={value=200.0, range={min=100, max=300}}, ShortSMAPeriod={value=5.0, range={min=2, max=10}}, ExitLimit={value=95.0, range={min=85, max=98}}} |
|
Key |
Value |
parameters |
{RealizedGains={value=-3467.0, range=null}, Cash={value=4.0, range=null}, ReturnPercent={value=20.0, range=null}, NumberOfBuys={value=2.0, range=null}, ReturnPercentAnualized={value=10.0, range=null}, MaxDrawDownHighValue={value=110489.0, range=null}, NumberOfCashTransfers={value=1.0, range=null}, AbsoluteReturnStdDev={value=1242.0, range=null}, MaxDrawDownNumberOfDays={value=370.0, range=null}, AbsoluteReturnAvaragePerDay={value=39.0, range=null}, NumberOfSells={value=1.0, range=null}, ActualValue={value=120122.0, range=null}, UnrealizedGains={value=23889.0, range=null}, PurchasedValue={value=96233.0, range=null}, MaxDrawDownPercent={value=23597.0, range=null}, NumberOfTradedStocks={value=1.0, range=null}, MaxDrawDownLowValue={value=86892.0, range=null}, AbsoluteReturn={value=20122.0, range=null}, ReturnPurcentStdDev={value=0.0, range=null}, SharpeRatio={value=1.0, range=null}, NumberOfTrades={value=3.0, range=null}, TotalFees={value=300.0, range=null}} |
|
Key |
Value |
ticker |
DENN |
exchange |
NASDAQ |
|
RSI2Strategy |
false |
Key |
Value |
parameters |
{ShortCCIPeriod={value=5.0, range={min=1, max=100}}, Signal={value=100.0, range={min=90, max=110}}, LongCCIPeriod={value=200.0, range={min=100, max=300}}} |
|
Key |
Value |
parameters |
{RealizedGains={value=0.0, range=null}, Cash={value=1.0, range=null}, ReturnPercent={value=20.0, range=null}, NumberOfBuys={value=1.0, range=null}, ReturnPercentAnualized={value=10.0, range=null}, MaxDrawDownHighValue={value=107979.0, range=null}, NumberOfCashTransfers={value=1.0, range=null}, AbsoluteReturnStdDev={value=2220.0, range=null}, MaxDrawDownNumberOfDays={value=160.0, range=null}, AbsoluteReturnAvaragePerDay={value=39.0, range=null}, NumberOfSells={value=0.0, range=null}, ActualValue={value=119998.0, range=null}, UnrealizedGains={value=20098.0, range=null}, PurchasedValue={value=99900.0, range=null}, MaxDrawDownPercent={value=28571.0, range=null}, NumberOfTradedStocks={value=1.0, range=null}, MaxDrawDownLowValue={value=79408.0, range=null}, AbsoluteReturn={value=19998.0, range=null}, ReturnPurcentStdDev={value=0.0, range=null}, SharpeRatio={value=0.0, range=null}, NumberOfTrades={value=1.0, range=null}, TotalFees={value=100.0, range=null}} |
|
Key |
Value |
ticker |
PTNR |
exchange |
NASDAQ |
|
CCICorrectionStrategy |
false |
Key |
Value |
parameters |
{NumberOfTicks={value=7.0, range={min=1, max=500}}, BuyFactor={value=1.0, range={min=1.001, max=5.0}}, SellFactor={value=1.0, range={min=0.005, max=0.999}}} |
|
Key |
Value |
parameters |
{RealizedGains={value=23187.0, range=null}, Cash={value=119987.0, range=null}, ReturnPercent={value=20.0, range=null}, NumberOfBuys={value=16.0, range=null}, ReturnPercentAnualized={value=10.0, range=null}, MaxDrawDownHighValue={value=101788.0, range=null}, NumberOfCashTransfers={value=1.0, range=null}, AbsoluteReturnStdDev={value=584.0, range=null}, MaxDrawDownNumberOfDays={value=44.0, range=null}, AbsoluteReturnAvaragePerDay={value=39.0, range=null}, NumberOfSells={value=16.0, range=null}, ActualValue={value=119987.0, range=null}, UnrealizedGains={value=0.0, range=null}, PurchasedValue={value=119987.0, range=null}, MaxDrawDownPercent={value=7803.0, range=null}, NumberOfTradedStocks={value=1.0, range=null}, MaxDrawDownLowValue={value=93985.0, range=null}, AbsoluteReturn={value=19987.0, range=null}, ReturnPurcentStdDev={value=0.0, range=null}, SharpeRatio={value=1.0, range=null}, NumberOfTrades={value=32.0, range=null}, TotalFees={value=3200.0, range=null}} |
|
Key |
Value |
ticker |
BICK |
exchange |
NASDAQ |
|
GlobalExtremaStrategy |
false |
Key |
Value |
parameters |
{NumberOfTicks={value=7.0, range={min=1, max=500}}, BuyFactor={value=1.0, range={min=1.001, max=5.0}}, SellFactor={value=1.0, range={min=0.005, max=0.999}}} |
|
Key |
Value |
parameters |
{RealizedGains={value=22182.0, range=null}, Cash={value=119782.0, range=null}, ReturnPercent={value=20.0, range=null}, NumberOfBuys={value=12.0, range=null}, ReturnPercentAnualized={value=10.0, range=null}, MaxDrawDownHighValue={value=102274.0, range=null}, NumberOfCashTransfers={value=1.0, range=null}, AbsoluteReturnStdDev={value=623.0, range=null}, MaxDrawDownNumberOfDays={value=73.0, range=null}, AbsoluteReturnAvaragePerDay={value=38.0, range=null}, NumberOfSells={value=12.0, range=null}, ActualValue={value=119782.0, range=null}, UnrealizedGains={value=0.0, range=null}, PurchasedValue={value=119782.0, range=null}, MaxDrawDownPercent={value=11169.0, range=null}, NumberOfTradedStocks={value=1.0, range=null}, MaxDrawDownLowValue={value=91104.0, range=null}, AbsoluteReturn={value=19782.0, range=null}, ReturnPurcentStdDev={value=0.0, range=null}, SharpeRatio={value=1.0, range=null}, NumberOfTrades={value=24.0, range=null}, TotalFees={value=2400.0, range=null}} |
|
Key |
Value |
ticker |
UCBA |
exchange |
NASDAQ |
|
GlobalExtremaStrategy |
false |
Key |
Value |
parameters |
{NumberOfTicks={value=7.0, range={min=1, max=500}}, BuyFactor={value=1.0, range={min=1.001, max=5.0}}, SellFactor={value=1.0, range={min=0.005, max=0.999}}} |
|
Key |
Value |
parameters |
{RealizedGains={value=20514.0, range=null}, Cash={value=119714.0, range=null}, ReturnPercent={value=20.0, range=null}, NumberOfBuys={value=4.0, range=null}, ReturnPercentAnualized={value=10.0, range=null}, MaxDrawDownHighValue={value=121480.0, range=null}, NumberOfCashTransfers={value=1.0, range=null}, AbsoluteReturnStdDev={value=1171.0, range=null}, MaxDrawDownNumberOfDays={value=496.0, range=null}, AbsoluteReturnAvaragePerDay={value=38.0, range=null}, NumberOfSells={value=4.0, range=null}, ActualValue={value=119714.0, range=null}, UnrealizedGains={value=0.0, range=null}, PurchasedValue={value=119714.0, range=null}, MaxDrawDownPercent={value=22261.0, range=null}, NumberOfTradedStocks={value=1.0, range=null}, MaxDrawDownLowValue={value=99219.0, range=null}, AbsoluteReturn={value=19714.0, range=null}, ReturnPurcentStdDev={value=0.0, range=null}, SharpeRatio={value=1.0, range=null}, NumberOfTrades={value=8.0, range=null}, TotalFees={value=800.0, range=null}} |
|
Key |
Value |
ticker |
DSPG |
exchange |
NASDAQ |
|
GlobalExtremaStrategy |
false |
Key |
Value |
parameters |
{ShortCCIPeriod={value=5.0, range={min=1, max=100}}, Signal={value=100.0, range={min=90, max=110}}, LongCCIPeriod={value=200.0, range={min=100, max=300}}} |
|
Key |
Value |
parameters |
{RealizedGains={value=0.0, range=null}, Cash={value=2.0, range=null}, ReturnPercent={value=20.0, range=null}, NumberOfBuys={value=1.0, range=null}, ReturnPercentAnualized={value=10.0, range=null}, MaxDrawDownHighValue={value=154090.0, range=null}, NumberOfCashTransfers={value=1.0, range=null}, AbsoluteReturnStdDev={value=2344.0, range=null}, MaxDrawDownNumberOfDays={value=259.0, range=null}, AbsoluteReturnAvaragePerDay={value=38.0, range=null}, NumberOfSells={value=0.0, range=null}, ActualValue={value=119681.0, range=null}, UnrealizedGains={value=19781.0, range=null}, PurchasedValue={value=99900.0, range=null}, MaxDrawDownPercent={value=38559.0, range=null}, NumberOfTradedStocks={value=1.0, range=null}, MaxDrawDownLowValue={value=115531.0, range=null}, AbsoluteReturn={value=19681.0, range=null}, ReturnPurcentStdDev={value=0.0, range=null}, SharpeRatio={value=0.0, range=null}, NumberOfTrades={value=1.0, range=null}, TotalFees={value=100.0, range=null}} |
|
Key |
Value |
ticker |
PFBX |
exchange |
NASDAQ |
|
CCICorrectionStrategy |
false |
Finally we use the selected result as our portfolio and we can run a simulation on it for the
last year:
var account = new Account("Simulation","USD", 100000.00, periods.get(0).getStart(), new PerTradeFees(10.0));
var trader = new PaperTrader(account);
var allocationStrategy = new DistributedAllocationStrategy(trader);
var executor = new StrategyExecutor(trader, allocationStrategy);
executor.addStrategy(result.getStrategies(new MarketArchiveHttpReader()));
executor.run(periods.get(1));
println(account.getKPIValues())
[Absolute Return 25033.923488616943, Absolute Return Avarage per day 48.51535559809485, Absolute Return StdDev 779.2345876615038, Return % 25.033923488616942, Return % per year 12.202221893871315, Return % StdDev 0.007057396060496201, Sharp Ratio 1.0300644489363013, Max Draw Down % 7.728977897596769, Max Draw Down Absolute 9628.355476379395, Max Draw Down - Number of days 45, Max Draw Down - High 124574.7575416565, Max Draw Down - Low 114946.4020652771, Max Draw Down - Period 20171106-20171207, Number of Trades 2, Number of Buys 2, Number of Sells 0, Number of Cash Transfers 1, Number of Traded Stocks 2, Total Fees 20.0, Cash 35.85043716430664, Total Value (at actual rates) including cash 125033.92348861694, Total Value (at purchased rates) 99980.0, Realized Gains 0.0, Unrealized Gains 25053.923488616943]
null
Displayers.display(account.getTransactions().collect(Collectors.toList()));
active |
stockID |
date |
quantity |
requestedPrice |
filledPrice |
fees |
comment |
id |
requestedPriceType |
buyOrSell |
impactOnCash |
true |
Key |
Value |
ticker |
Cash |
exchange |
Account |
|
2016-01-01 |
0 |
0 |
0 |
0 |
|
7109bb39-1f69-45ae-8f28-906b80e8486b |
CashTransfer |
NA |
100000 |
true |
Key |
Value |
ticker |
CARZ |
exchange |
NASDAQ |
|
2017-01-04 |
1426 |
0 |
35.0391 |
10 |
|
dc256381-c8cb-47f7-8c3c-98239b9672a5 |
Market |
Buy |
-49975.7303 |
true |
Key |
Value |
ticker |
PAHC |
exchange |
NASDAQ |
|
2017-01-04 |
1718 |
0 |
29.091 |
10 |
|
16b12b8a-09f2-4316-9bb8-af34cbf83b70 |
Market |
Buy |
-49988.4192 |
var chart = new TimeSeriesChart()
chart.add(account.getTotalValueHistory(),"")
Displayers.display(chart.displayChart())
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA+gAAAJYCAYAAADxHswlAACAAElEQVR42uyd91dU2ba2vz/Nc4deT587zvGHHmDWK0FJJjChqNeEoTG12qY2J0S0bRW1DU0bSQoGMGNjICkKZgR1fnuupqqLonIVUOFZY7xDdu1dVfDU2si75ppz/j9hMBgMBoPBYDAYDAaD0e/j/4GAwWAwGAwGg8FgMBgMDDqDwWAwGAwGg8FgMBgMDDqDwWAwGAwGg8FgMBgYdAaDwWAwGAwGg8FgMBgYdAaDwWAwGAwGg8FgMDDoDAaDwWAwGAwGg8FgMDDoDAaDwWAwGAwGg8FgYNBjdlRVVXU7TkxMlP/93/+NGqWkpETVzwMTuMAFLnCBDSzgAhcEF7gEwyYvLw+DHq7jjz/+6HasH5g+Fi36888/o+rngQlc4AIXuMAGFnCBC4ILXIJh83//938YdAx6/+jGjRvckDCBC1zgAhfYwAIucIELXGCDQcegI4QQQgghhBAKH2HQMeisosEELnCBC1xgAwdYwAUucIELEXQGOejckDCBC1zgAhfYwAIucIELXGCDQcegs4oGE7jABS5wgQ1sYAEXuMAFLkTQGeSgI4QQQgghhBDCoGPQWUWDCVzgAhe4INjAAi5wgQtciKAzMOjkocAELnCBC1xgAwu4wAUucEHkoGPQWUWDCVzgAhe4INjAAi5wgQtciKCHbtTV1cnOnTuNcXUeAwYMMBo8eLBkZmZKfX19j2uqq6tl0qRJ8t///d/26z2N8vJySU5OlqSkJKmoqAj5eXLQEUIIIYQQQogc9Ig06Lm5uVJYWOjRWLe3t8v+/fslIyOj2+O1tbUyduxYqaqqks+fP3t9r5qaGklNTZWGhgajtLQ081iozhNBRzCBC1zgAhfYwAIucIELXIigR/wWd2+RbzXpGkl3HAsXLpQrV674/B45OTlSWlpqPy4rKzOPheo8OegIJnCBC1zgAhtYwAUucIELOehRb9DVGE+ePLnbY99//70cPnxYhg4dKqNGjZLi4mKPrxEXFydtbW3249bWVomPjw/ZeSLoCCZwgQtc4AIbWMAFLnCBCxH0qDboTU1NZju5cw76wIEDZcmSJdLS0mKM86JFi+TUqVNuX0ev7+zstB/r14MGDQrZeceh2+71w8nPz+/2eEpKilldsX2A+i/HHHPMMcccc8wxxxxzzDHH0XEc1QZdi8hNnz5dmpube5zT/PMPHz7Yj1+9eiXDhw8ngs4qGkzgAhe4wAU2sEBwgQtc4EIEPZQGXU35tGnT5N27dy6fk5eXZ6LrtvHmzRtT0d1TDrpWYbeNkpISyc7ODtl5ctARTOACF7jABTawgAtc4AIXctCj0qBnZWXJ/fv33T7nwYMH5ofXLe5v3741hr2ystLt9dqSLT093Zh63S6fmJjYrVVasOeJoCOYwAUucIELbGABF7jABS5E0CPWoNt6lzvKl3OOBli3uutW84sXL3o1/Vp5PSEhQYYMGSIFBQUhP08fdIQQQgghhBCKXUVFBD3UQyPp3vLD+2IQQWdlEcEFLnCBC2xgARe4wAUuRNBj2qDrVvTa2loMOnkoMIELXOACF9jAAsEFLnCBCznoDCLorCwiuMAFLnCBDSzgAhe4wIUIOgYdg44QQgghhBBCyKNezpwpF86fJwcdg84qGiuLcIELXOACF9jAAi5wgQtc+k3FxSJxcXL59Gki6Bh08lDIzYELXOACF7jABhZwgQtc4NJfUmP+LT5eyo4dIwcdg84qGiuLcIELXOACF9jAAi5wgQtc+ktlR4/K1+HD5XpBARF0DDpCCCGEEEIIof5S5cGD0jlqlNzas4ccdAw6q2isLMIFLnCBC1xgAwu4wAUucOkv3d61Sz5bHu3O1q1E0DHo5KGQmwMXuMAFLnCBDSzgAhe4wKW/dHfTJvmQnCwP1q2TR2vXyvWDB8lBx6CzigYTuMAFwQUusIEFXOACF7j0tdSUv8nIkD/z8qR1yhS5sXcvEXQMOkIIIYQQQgihvlbd8uXyato0ebZ4sXxMSjJF48hBx6CzigYTuMAFwQUusIEFXOACF7j0seoXLJCmnBxpmjvXVHO/ePYsEXQMOnkoMIELXBBc4AIbWMAFLnCBS1/rRXa2PM3NlVdTp0rn6NEhYYNBx6CzigYTuMAFLnCBDRxgARe4wAUuHnS1qKjHY68tY/7wxx/lfUqKvE9NDQkbDDoGHSGEEEIIIYSQB2k7tZbp0+WP4mL7Y+/S0qR62zZpt85pLnoo3geDjkFnFQ0mcIELXOACGzjAAi5wiSEuT5Yvl7oVK+Dip0F/M3GiNM+eLRd+/9081p6QINcOHZIvI0dKg2WsiaBj0MlDITcHLnBBcIELbGABF7jAxXcVF0vH6NGmsBlcfNOVkydNjvnF8+fl5cyZ8jorSy6dOydfhg+Xy7/9JhIfb1qtkYOOQWd1kRVXuMAFwQUusIEFXOACF591Z+tW+ZSQIE+WLYOLj7q5e7e0Tp1qvr5QXCwN8+fL2/R0+TZ0qHlM/32wfj0RdAw6QgghhBBCCPkePf8wYYIxmE+XLIGHj6pdtUqeOfF6vmjRXwbdYqot1u5u2UIOOgad1UVWXOECFzjABS6wgQVc4AIX31SzbZu8ycgwlcfVYMLFN2ne+d3Nm7s9pnnoN/btM19/SkqSG/v3E0HHoJOfQ84SXOACB7jABTawgAtc4OJb9Fxbgd3atctsx65fsAAuPurrsGFy2+LWF2wi2qDX1dXJzp07jXF1HgMGDDAaPHiwZGZmSn19vdvXKSoqMtd6G+Xl5ZKcnCxJSUlSUVER8vNE0BFM4AIXuMAFNrCAC1zg0huq3rFD3qalma/v//ST2eYOF9+kbdRKTpzoEzYRbdBzc3OlsLDQo7lub2+X/fv3S0ZGhsvzDx48kNTUVK8GvaamxlzX0NBglGZNbn0sVOfJQUcIIYQQQgj1ljrGjJGan382X9/dtKnPq7hHsjpHjTLV2vvivaJii7s3c60mXSPpzuPt27eSkpIiTU1NXl8jJydHSktL7cdlZWXmsVCdJ4KOYAIXuMAFLrCBBVzgApfekOZLf4uPt/fvvrNlizTPmcN88VFaDM7Gjgh6CAy6GuPJkyd3e+zbt2/GIN+8edOn14iLi5O2tjb7cWtrq8RbkzxU5x1HVVWV+XDy8/O7Pa6LCZqfYPsA9d9IPm5sbIyqnycUxzov4NHz2Plf+DBfmC/MF/4/4v7h/mG+MF98P75t+SGNAtuO/zxwQF7MmsV88eG47uFD+TZ8eK++n/4/ZDuOeoOu0XHdTu6cg75r1y45fvy4zyZ/4MCB0tnZaT/WrwcNGhSy80TQEUzgAhe4wAU2sIALXODSGyr79Vf5mJTULR/95YwZzBcfdOX0aekYPbrP2ES1QdcictOnT5fm5uYe5/7xj3/YC8k5Khwi6OSgI4QQQgghhEKlyoMHTXs127FWcn81bRpsfFDp8ePyKSGhz94vag26mvJp1qR79+5dSLbJ63Z4rcJuGyUlJZKdnR2y80TQEUzgAhe4wAU2sIALXOCiejNxojxZtixk39NtJ0N+c88eeZ2ZyXzxQRVHjsj7CROIoAdr0LOysuT+/fshy2Ovrq6W9PR0s2Vet8snJiZ2a5UW7Hn6oCOYwAUucIELbGABF7jARdU+bpw0WEYtVN+Tc9X2G/v2SeuUKcyXAHYf9DabiDbonrao+7N93ZVBd3W9Vl5PSEiQIUOGSEFBQcjPE0FHMIELXOACF9jAAi5wgcuXESOkcd68kH1Pj9askWeLF/9tOg8ckDeTJjFffNDNvXvl9dSpRND7c1RWVnrND++LQQ46QgghhBBCsaVLZ87I12HD5OXMmSF7Td0u/3jlSvvx9YICeZueDm8f1NcF9TDoLoZuRa+trcWgs4oGE7jABS5wgQ0sEFzgEhSX25bB05xyX5+v5vnj+PEhNdC6Xf7++vX242uFhfIuNZX54oPubN4szTk5RNAZ5KCTs4TgAhe4wAU2sIALXCKdixrjb0OHyoXff/fNEG7ZIi0zZsjnIP/2v79xozxfuNB8/WLmTKnZts1+ri8Kn0XLfNHPr2HBAnLQGUTQWXFFcIELXOACG1jABS6RzkW3l2vRt6r9+316ft0PP0jd8uXybdgwn029K6k5b+9qD9Y6ebIpDGc7V3b0qHxMTma+eJDuMviUmCiPVq+WZ7m5RNAZ5KAjhBBCCCEU6dL85ZezZhnT7cv1zbNnm23VaupLjh8P+H3rVqyQLyNHmq81Wl5x+LD9XF/39o5EaSu6b/Hx8qfFURdNyEFnEEFnxRXBBS5wgQtsYAEXuEQ4F41U6zbpdh8N8bu0NJOHrq29Hq5dG3A/dI386tb6S+fOme3yV4uK7OdKrK/b+9hbRNp8qfn5Z2PQny9eLLVr1hBBZ5CDTs4Sggtc4AIX2MACLnCJZC4Xz583Fdm1Mru2TtPcb2/P16j3Zev6F7Nmyfvx4+VjUlJA34dWbf88ZoxcP3Tor+3y1vdiO3fl5EnpGDuW+eJpgWPtWum0Pgvd0fBgwwZy0BlE0FlxRXCBC1zgAhtYwAUukcxFt5V/6CrG9mdenjTMn+/xuRrlthnnpjlz5Ktl6j9YJt3be14oLpanS5eafx1z2XVru5pLXST4w+Hc5d9+k85Ro5gvDnqfkiL3f/rJfvxsyRL5mJgorzMz5e6WLUTQGeSgI4QQQgghFMl6sny5vZ3ZlVOnjCm+cvq0e6O2f7+0TZ78l0FcvFga5s2z55H3kIPh1lx1iYvrVlTuiWXY1Vy2TpnSoyCcbnv/Mnw4n5FjKkJSkjHptuMX2dmmPZ62u9Ne6OSgM4igsxKN4AIXuMAFNrCAC1wimItGsTUSbjtunD/fbD1399z7GzaYa3psef/ttx7Xtkyfbu+vfmfrVvlqGW7HPHM1+M0ahR82TMqPHu0ecT9/3mx7Z750j6DrQsbtnTvNsbLV7e1q3G/u3UsEnUEOOrlcCC5wgQtcYAMLuMAlkrloBP1xXl63Le9anM1d+zRt51W7alV34+hUgd2mtkmT7Aa9XluqWa977dAh+3l97Kn1ek1z57qMvkt8PPPFQVrV/t6mTYa3pgpoFf26Zcvk89ixUnnwIDnoDCLorEQjuMAFLnCBDSzgApdI5tJgmS2t4O54/vXUqXLHTU6zRsWdt1O/mjZNbu3a1eNazW23GfT3qamm6vut3bv/fu/587vlVDtLK5Q75qzH+nzR9APdqaA94+9Z3HSHgeae6+O+FPcjgo5BRwghhBBCCIWxtP95zfbt3R7TLdSa19w4b56Jcncz3cnJUu5kBtXku6oirsbxbVqalP76q9nerlvp727aZD/flJMjdzdvdvu96db3iw6V3WNd2pJOFyw0Wq5t6TRyXrV/v+FUGkQ/enLQMeisusIELnCBCxzgAhtYwAUuYcBFC75p4Tfn7eUdo0cbtU6d+nde+O+/GzN4wck0a/V3517oWmhOc9O1yvjtXbtMj/WnS5ZI7erV9mtezJ4tNVu3uv0+9fna/o350rNonn4uusW97NdfzU4DT4X9iKBj0MlbIpcLLggucIELbGABF7hEABfdhu5qe/TDH3+UG/v2GZNsy0cvO3pUPrnoea5RcY2GOz5WdeCAicJrn+7qbdvk5cyZUrtmjclht0fvZ8zwWH1cFwh623hGynzR4nqfHbyWLnq0WPwunj1rquP39k4DctAx6Ky6wgQucIELXBBsYAEXuPQyF+1pfvXUKbfXvs3IMIXc1ByqKXyVldXjmurt23v0Qtcc6ca5c8227Edr18rzhQvNdnbHivHuctdt0i3cV0+eZL5Y0ir3H5xa0dnrAlgceztXnwg6Bh0hhBBCCCHUm7JMnclrdlOxXVVvmbEvI0aYHPJHa9bI80WLeprHI0fMeft2dOt1tdCc5q+ryX6+YIE8XrVKbu7ebfqe256n13hqD6ZbuEtOnOBzsnT94EGzIyEcvhcMOgadVVeYwAUucIELbOAAC7jAJcRctCK4FnLzdO2tnTvl6dKl8i411WxTf7BuncvrdLv6vY0bpfyXX6Rt4kTTs1uj49oSTCO8ug3+2uHDppq7Pf990iSzFd7de+t2ei0wx3z5wyxkvHaoB9Cf9xIGHYNO3hJM4AIXuMAFNnCABVzgEmIumlP+0UVOuctI+sKF8jE5WW7u2ePyvFaC1z7dHWPG/GXiu7Zct06ZYsy9Gkzdrv7ZOu+4ff66h/7dum1eDT/z5Y+/8vhnzQqLewmDjkFn1RUmcIELXOACGzjAAi5wCTGXyvx805vcl+fc2bzZbGMvcdPOS7e3q6F2bvf1IjvbbHNXo32ha0u9zby/S0kxUXV376lR+AoP52NpvtzbtMnk9BNBZ5CDjhBCCCGEUBRK+523TJ/u07WaZy7x8XZz7avqFywwrdls+ekaYb/SVfjNW4T8XVqaXDt0iM/qj7+q6rvK/ycH3c9RV1cnO62Jr8bVeQwYMMBo8ODBkpmZKfX19X6ddzXKy8slOTlZkpKSpKKiIuTniaAjmMAFLnCBC2xgARe4RAcXzRn3NSqrheQ0iuvve2n+uhp0e1Rc27p1RcW95ZhrdL/Swxb4WJov2mu+bvlyIujBjtzcXCksLDRG291ob2+X/fv3S4Y1AQM5bxs1NTWSmpoqDQ0NRmlpaeaxUJ0nBx3BBC5wgQtcYAMLuMAlerg8XrlSni5Z0qvv9cQylY6F6FodKre3W96hpKjI7XNNETnLBzFf/jCfk/aRJwc9RMOTQbeZcI2UB3peR05OjpSWltqPy8rKzGOhOk8EHcEELnCBC1xgAwu4wCV6uGi1da2w3tsGXXPQbcfNs2fLnS1b/truPnq0XDl92u1z1czf8NCGLZbmS4Nliu9v2EAEva8MuhrjyZMnB3xeR1xcnLS1tdmPW1tbJT4+PmTnyUFHCCGEEEIoetQ8Z44p/tab71FlGWzHrdnPFi+WR2vXmq+/jBz5d+90F3qVmSm33FSNj7nPShc2tm4lB70vDHpTU5PZTu4ux9zbedsYOHCgdHZ22o/160GDBoXsvOOoqqoyH05+fn63x1NSUsz2B9sKi/4bycePHj2Kqp8nFMeNjY3wcHFsEzyYL8wX5gv/H3H/cP8wXyJlvrRNniyPjh3r0/dv+OknaduwwRxrbvrNa9fcXq8F7B4fOsR8sY7fZmebvvL99f76/5DtOKoNuhaRm25NvObm5oDOE0EnbwkmcIELXOACG1gguMAlEC5apK3MQ5G23pBjYTpvVeFfzpwp1du3M1+60hGqDhwgB703Dbqa7mnTpsm7d+9cPsfbeVc56FqF3TZKSkokOzs7ZOfJQUcwgQtc4AIX2MACLnCJHi4awb547lyfvrdGgV9ZHkerwpue6B6ufTF7ttT04bbucJ4v2pKuP1vOxUQOelZWlty/f9/tc7yddx7V1dWSnp5utsTrdvjExMRurdKCPU8OOkIIIYQQQtEhLc7WOXp0n7/vdctkan/zS2fPypcRIzxe2zRnjtztKigX69LFlMr8fHLQQ2HMneXLOV/POw+tvJ6QkCBDhgyRgoKCkJ8ngo5gAhe4wAUusIEFXOAS+VyuHT4s71NT+/y9S06cMO3VLvuwQNA4b57ZEs98+aug3mUPBfWIoPfzqKys9Jof3heDHHRyuRBc4AIXuMAGFnCBS+Rxud211byv3/vC+fPybdgwuVpUJJ+9eIdgW4tVb9smTTk5kT9fiovlm5d8fXLQ+3noVvTa2loMOquuMIELXOACF9jAAsEFLn5zub9+vTTMn98v7985apRUHD4snxISPF5Xv3ChPFi3LuD3ce7BHqnz5fJvvxlm4XIvYdDDeJCDjhBCCCGEUORJe5P/+cMP/fLeH5KT5dbu3fLR+tfTdY490wP6Ga2fr6Mf8uxDrdJjx7wuZpCDziCCzko0ggtc4AIX2MACLlHPRaPM7ydMsLcGixYur7OypHbVqn55f+2/fm/TJnmfkuLxuqdLlwb1Pf6ZlyedI0dG/H10rauwHhF0Bjno5C3BBC5wgQtcYAMLuMQ0l9dTp8ory8z2R752b3JpT0iQe5s398v7v8jOlscrV8rb9HSvW9QfWyY70PfR534dPjzi76Mb+/ZJ65QpYXMvYdAx6KxGwwQucIELXGADB1jApV+4aP9pLVT2OjMzarjcLi01VcG1F3l/vP/zhQvN9vU3kyZ5NoUrVpht6gEb9JUr/yquFuH3UfX27fJy5kwi6AwMOkIIIYQQil2pgdX+09cPHpR3/dCSrNcM344dZldAv0Vj8/KkOSfHa1RYDfaTZcsCfx/L3H8bOlQunj8f0Z+XtprTlnPkoDOIoLMaDRO4wAUucIENLOASs1zKjxwxBc1KfGgJFkl6uWKF1K5e3W/vf/+nn/5KG/CyK6F2zRp5umRJwO/zZOlS+TJihFw9dSqi76NHFgfdcUAEnUEOOvlcMIELXOACF9jAAi4xyaVz9GgTgdWtxbbe3dHCpWPiRLl+6FD/bbHfscPkn7fMmOHxuodr18rzIIypmtqOMWOk7OjRiL6P+rPiPjnoGHRWo2ECF7jABS4INrCAS79yuXLypNnartXbtVCZMewjR5p+1JHORKPJX7T1WHFxv30PlQcPysekJFMsztN12gNde6EH+j4NCxaY9mSaohDJ99HzRYvk4Y8/EkFnYNARQgghhFDs6c7WrdIybZqpAK550PqYGkpfI7Hh/rO99BK57m2VHj9u+pNrHrq3rfDa5i7Q92maO9csstzcsyeiP7Mmi9PdTZvIQWdg0FmNhglc4AIXuMAGFnCJPS5qCjViqdXby375xTymFcer8vMjnon+bM8tk96f38PFs2dNyoC3wmePrM+gJYjFBI3Qv8nIkJqff47o+6hl+nSTFkAEnUEOOvlcMIELXOACF9jAAi4xx+VTUpIpEOf4mEadq/vZJIVCHxMT5XlJSb9/H5pC4C06rnnX2uYuYGNrfWbaHk8j8ZF8H7VNnixV+/eTg87AoLMaDRO4wAUucIENLOASW1x0+/XnsWNdRp59NXrhqhL92caMCYv5olvcG+fO9Zwvf/KkuS7Q91Bz/mLWLFMNPpLvo/cpKVJx+DARdAYGHSGEEEIIRa+u/Pab6XXu+Jj2nG6ePbvHtdqP+3FeXkT/vPc2bXL5s/WHtHibtxx023WB5v5r5Llh3jx7ob9IVfu4cVJy4gQ56AwMOqvRMIELXOACF9jAAi7Ry0VbqdmKwNmkpvGei0j5oyBbfoWDtNiY7gIIh/nyYcIEE932mkc+e7bc3bw5oPfQVm5Pc3PluZtK8NXbtpn87nC/j76MHCmXzpwhgs7AoJPPBRO4wAUucIENLOASvVy0ddrTpUu7PfbZ+nu29NixHtfe2bIlbKLPAam42PxsZdbPFg7z5W1aWjdz7E66MFK/YEFgW8MnTJDHq1ZJ05w5Ls8/sT57x3SGcLyPLp4/LxIXF1b3EgYdg85qNEzgAhe4wAU2cIAFXELLxTKs34YONVFl22Plv/winxIT3RrFzlGjIpZFZUGB+XnDZb5ob++6Zcu8b8vfuNFEkAN5Dy2IpzsG3C0EaJ90R4MejvfRFc3DHzMmrO4lDDoGHSGEEEIIoZCqpKjIGB/dBm177OG6ddLoprK4RjL1ei0iF0k/Z92KFfJy1iypPHBA3kycGHGf07WCAnmXlhZY7rblTaq3bze56K7Ov5w5M6gidH0h7SYQTCV7ctAx6KxGs0IPFwQXuMAFNrCAS9hzUcOq26y/DB9uouk2w3bHQ49wzWX+M8IKxWml9I9JSaYX+Avr54u0+VJhGVTdqh7Ic7XGgP7c7nY+vM3IkC8jRoT1faTt1dwtMBBBZ5CDjmACF7jABS6wgQVcooKLLae8PSFBSn/91Zh0NXRXT51y+5zrBQXyMTk5ohjUL1xocs8fapG7RYsibr5oBfdAmX+1zPeVoiJjwi+fPt3jvHL5Fh9vX6AJJRf9vm/v2hX06+gOgJczZpCDzsCgsxoNE7jABS5wgQ0s4BK9XLR6uxaIa5s0yeQ5a6T1sw9/y+p248r8/Ihh8Cw31xhU/VlrV62KuPmiKQXaai2Q50qX+W6xDK7zzoiL587J1+HD/zLvXRXSQ8nl2eLFIdk+rzn0DW7SLoigBzDq6upk586dxrg6jwEDBhgNHjxYMjMzpb6+vsc15eXlkpycLElJSVJRUeH1/bxdH+x5ctARQgghhFA0SAuEPVi/Xl5bf4c3zZ0rtZZhf52V5fV5auzrLYMSKT+nthnT4nDNQbQr609dLSryaeHEWRfOn5dvw4aZr/Vz1s/YVW637qAo6YW6Ag/XrDHmv+Lw4aBeR6vQP3HqNEAOehAj17ohCgsLjRF3N9rb22X//v2SkZHR7fGamhpJTU2VhoYGo7S0NPOYu+Ht+mDPE0FHMIELXOACF9jAAi7RwuWVZcZv7doldyzT+iI72+SXa6V2b69ZcuKEiYyqAYwEBrqtXb/fN5bXuLl3b8TNlyunTgVUxVyj4p1d1d+1bZ6p1t61lV11e+dOMwfep6RIheXXQs3lzxUr5M3kyT61kvO2A6LWMvtE0EM8PBl0m0nXSLrjyMnJkdLSUvtxWVmZeczd8HZ9sOfJQUcwgQtc4AIX2MACLtHCRQuHVe3bZ4qQaSS11TJTNgPrTa1Tp0r1tm2RsVNg/nz5kJws7ePGmTZykTZfHI22X5F3y9h/djD2msd+rcuIq1FvmzjRFAnUAmxaiC3UXHSL+3NL+r3rIoDuYijTWgd+vo5G/u9t2kQOel8bdDXGk63J4Tji4uKkra3Nftza2irx8fFuX8Pb9cGeJ4KOYAIXuMAFLrCBBVyigYsW79K8Zs1DvvD776aYWIeXAnGOumsZpnAo3OWTwZszx+TZa771pV7Ite5tXTx71nw+weau604C3S6u5lxb6akx1+i8Rrg1mh5qLo1dxlqZ63toy7dAWvS1TJtm//6IoPeRQW9qajLbyZ1z0AcOHCidnZ32Y/160KBBbl/H2/XBnnccVVVV5sPJz8/v9nhKSopZXbF9gPovxxxzzDHHHHPMMccch82x9XfsZ8uw/llQYD/fMXGifOkq6OXL6z25f1++WYZXI9Lh/vO2zZsnHy1DpRHcSPy8blZWmu/d3+ffPXfO7IywHdcWFppdEq8XLpRPs2bJrYoKc/3rBQvk5eHDIf/+2+bMkeodO+zHnzIyzFZ6f1/vw5Qp0uBgkMPh84lqg65F5KZPny7Nzc1+R8SJoLMaDRO4wAUucIENLBBc/OOi1bw1H9vxMS0Up2bOn9d+YZk8fd4fv/8e1gw0AtuUk9Ntm3ikzRdxaIXmq64dOiTv0tK6ReLV6Gt6gu6csEfWLcP+cN26kHNptYz1jX377Me6pb4qgOr/2sNeW7YRQe8Dg66mfJp1w7x7985tTrlWVbeNkpISyc7O9piD7un6YM+Tg45gAhe4wAUusIEFXCKRS0lRkZRbJke3s2su8g2nXPOXltl+62DmfJG+lm6Lr1uxIqwZqCFtsAxVp0PLr0ibL1qN3d+ifNoK741lip0L5jmnMdQtXy5/5uWFnMu71NS/c961KKHl+24F0BddayVc/u03ctD7wqBnZWXJ/fv33T6nurpa0tPTzRZ43f6emJjosfWZt+uDPU8EHcEELnCBC1xgAwu4RCIXbZ+mxlx7Sr+2DGsPs11cbOTv6zdnZ8udLVvCmsGbSZPkxu7dUupQoCzS5ssXzZ93iHr79Nnv3WsWJ7xdpxXStVJ6qLlo/rtjzrl2Cqj5+We/F4FMakIAc5MIugdj7ixfzjlWUk+wPtwhQ4ZIQUGBV9Pv6fpQnKcPOkIIIYQQijRpdLx97FjTruv6wYOhKwQ2f77cs0x/OP/sus1bt3tH8ucXSBRZo9UatfZ2nS7aaKX73v6eda7c93OuXDl5MqAWc72tqIigh3pUVlZ6zQ/vi0EEnRV6BBe4wAUusIEFXMKZi1bqVrOkW5k/JSaG9PXrFy6UB135y+Eqza23tVeL1PnSMXasMav+PKdm+3Z5MXOm9+u2bTP1BELKpbhYvjnlzWvbtUdr1/r1Olq9/euwYWH3OwaD7mLoVvTa2loMOvlcMIELXOACF9jAAsHFA5e7W7b83RItxFuFny5ZYrZIhzMD563WkThftId7yYkTfj3nzubN0pyT4/W6m3v2mLSH+gUL5M/Hj0Py/Wrv9i9Ovdu1VsGfftYr0MWDlz4sMpCDziCCzgo9ggtc4AIX2MACLhHB5cOECdLog1ELRI4FxsJVn8eM6VEYLdLmi+58cMyh90W+bl2/XlBgFjG0Uvyt8vKQfL+6INLu0INd9Wj1anuuu8+m2JpbT5YtI4LOwKAjhBBCCKHoUNukSXLTod1VKPV45cqwMVDupJFcjehG8mf4ITnZVOH35zm6nVxbqHm7rsqaG7od/cuIEXIpRJyuHT4s71NTuy8YbNjgd65785w5cnfzZnLQGRh0VqNhAhe4wAUusIEFXKKDi+YvXy0q6pXXVxOoucXhzMBVi7JImy/vU1KkwjK9/jxHC8Q597t3Ja2UXnnwoJkn1VeuhGbe7d8vbZMnd99yv3WrNM+e7dfraIE/jfATQWdg0Mnngglc4AIXuMAGFnCJeD25c6dHLnAo9WDDBqm3zErYMiguNlu3I32+BFKJXvuQV1lG2Z9c/WeVlSH5fqt37JAWW92DLt32saq842dn2sudPUsOOgODzmo0TOACF7jABTawgEvk68HJk6bFWm+9/t1Nm6Rp7tyw/fkvWubu64gRET9fNBJe6Ud7PM1X19x7f4oC6jb6u047DUI5L6pcRNW95rGPGxeWv2Mw6Bh0hBBCCCGE/DdKW7b4va3YH9Vs3SovevH1g9WV06elY/ToyK8jYBlbf6LhWlm/YcECvyPu1woLQ/L9PtT890WLuuelW6/9LiXF59fQPu6vMzPD8vPAoGPQWY2GCVzgAhe4wAYOsICL32qyjJK/ra380W0XW5nDSdqazFUUNtLmi7ZBu7l3r+8R94kTTfs0v6L01nMehqhWgc65Oqd5p1H9T0lJftU3cDb5RNAZ5KCTzwUTuMAFLnCBDSzgErH6NHOmqZ7dW69vemiHaZRTVWaZwo8uTGGkzZdXWVlya/duj/natgi7FgTsHD3aFH/z5z1ap0yRxhDle2vhQDXY3XYznDrl124Grfj+oBfnLjnoGHRWo2ECF7gguMAFNrCAS5+qIzHRVM/urdc3ecWTJoXtz6+Vz9+72FYdafNFdynobgV357XPu8TFycXz5+XBunUB1QXQAm6P/awU706N1vvf27ixez0A63v7OmyY79v6rXl1w49t/UTQGeSgI4QQQgih/s8z37TJbQEx0z/7l1967b21Bdbb9PSwZXPd4vLWh1Zj4a4XM2dKzfbt7nPtT56Ub0OHmgUTjYR7MvOBvoc/ejljhlS7eC39Hi/4WIhOo+36c5GDzsCgsxoNE7jABS5wgQ0s4BIx0hzrpnnzXJ7rtP421ehqr0WojxyR9xMmhC0bd5XDI22+aKE/TzshtOJ558iR8mTZMvPvxQCqsTfNmSNP/Mhz97Zd/sa+fa5N9+nTXp+v2+E7R40K298xGHQMOvlcMIELXOACF9jAARZw6SGzbXj4cLd54P5ELAORv4W/Qintta3G1NM1t/bskVcu2ETafNEt67pTwt153SWhCzW6WPIiOzug99Cc7xY/Wrl5kqkI76Jvu/Za9/aZ2RZW3oRZ6gQ56Bh0VqNhAhe4wAUuCDawiGEuvhhrrez9Jj1dvowc2aMo2KVz51z2AA+ltCDZ5376+1cjrI9XrfJq4l+6qDIfafNFzfP9n35yv5XfMsNqir/Fxwdcc0Arpj8PUb2CT4mJUnrsWM+UiwkTTGFBnTcqrbJf/uuvPa7TwoaN1s9MBJ2BQUcIIYQQQv0fGbfMtZotb/njT5YuNe2stL+0cx66aTHWy3+bXv7tt37bivxlxAh5vHKlx2vUrDaHcZ92X1W/cKEp/uZxK/+kSfJh/HifItSu9NSaS7VeFjz8WTzRueH8+OexY6VjzBizqKPSr7+4WEQyVeBXrw7bzwODjkFnNRomcIELXOACGzjAIoa4qOFSM+OtD7TmG2tk9XVWliny1S0//PBh+ZSW1vtb7P2ozB3K3QW6ff/J8uUer9NK4o0u8vMjbb48twzrQ6e2Zd12UuzebeZAMO+hCz2NP/4Y/PdbXGwWl/Rfb9e6W0TStIRbu3YRQWdg0Mnngglc4AIXuMAGFnDpf2mxr+dLlpj88mseWl9pTq/2+n64bp3ple1s8j+52N4dUlkmTHw0Y6GU/swafW10UxzPJo061y9YEPHz5WlurtR6iCi728rvjzRdoM3DNnpfdenMGZNy4dMCz9mzLtMw2jVX3cXWd3LQGUTQWaWHCVzgAhe4wAYWcOlzvUtLk6oDB0wF8ke2qKYLE6wGXk2O5vs6RyLVtL2ZM6fXv1eNoF/sxUJ07nLv36emeo0aP1qzRp4tWRLx80UXbDxt57+zZYs0B/lZa4T+5Q8/BP29lhw/bgx2oPPHXjuhjxd9iKBj0BFCCCGEEOoh7f2sObxa9O3u5s3yctasv7Y5L1xoimy5i1RqRFkLb9m3d2/aZKp/9/b36y7fuDel2/qVi7cWb4/z8rxug4+IHSIrVkidB/N8z+IRbFE1LczWYBnPYL/XisJCs3ji6/WayuE4b7X6u9ZUCOfPA4OOQWc1GiZwgQtc4AIbOMAiRrioKbflk9v6QasZ16Jaasg1wmi2eR89Kh8dWpy1TJtmoub26PHatfIiBBFRrwZLe607GKy+iiir8fZWoK7OuuZPy6RH+nzR6Ln+zG6j3z/+6LVegdd5t2mTvA7yNWypFa56z7uTLjpVHDny924Aa/6HY2E/IugYdPK5YAIXuMAFLgg2sIhBLrpV2bGlluaZqwl+m5YmLdOnGwNvN0IOvaK1ortjTraa09YQtc3yJO2DXuaipVavMrIMnBo52xZ/d9e1Tp0qTTk5ET9fateskacuturbz69aZaqwB/MeNT//LO9DYNBv79ghLX7kw+sc1rlsn8e6qNIHC0sxm4NeV1cnO3fuNMbV3/MNDQ0yd+5c+c9//iP//Oc/JSMjQ6qrqz2+X3l5uSQnJ0uS9YuioqIi5OeJoCOYwAUucIELbGABl15TcbHZqq7Vre3R0XXr5P7Gjablmpqo15bptBmqF13b31UP1q83Vd1t2811S/zzPjDo750ioH2hNxMnSlV+vtlBoDsJ3F2nfDQPPdLni+aHayV3tzsFVqww2+CDMtaWJ3sTgsj1XT9TK9TM33bY+aGpCzq3iaD30sjNzZXCwkIZMGCA3+fVKB87dkw+fvwoHz58kMOHD8v333/v9r1qamokNTXVGHtVWlqaeSxU58lBRwghhBBCvanrBQUe86q1mFbn6NFSUlRkjLv2x3Y8r9ucG7vMUXNOjtzdsqXXv+e36enm++5LTra85dbJk+XGvn3uzd/06cZ4Rvq80MUXV9XobfJW5d0XaeE92+JPUIsJfm631/mq9RK6bXn30LmAHPQQDXcG3dP5IUOGyKNHj+zHT548MZFtdyPH+iVUWlpqPy4rKzOPheo8EXQEE7jABS5wgQ0s4NKr22jz8uSZZbY8XaOFvHRL8xMX+dWan/4pMVFu7dkjr6ZNk8eFhb3+PWu+seMW5d6Wvfd6cbG8zsw07cHcRtozMqTy4MGIny+a8tDgoQicLtRoS7mgcsfz8+V9CAy6bk+v8yOa/2zxYvsuBy2M2B9dAWIyBz0Qg37x4kWJj4+XyspKuX//vsyaNUuePXvm9jXi4uKkra3Nftza2mqeH6rzjqOqqsp8OPnWRHYcKSkpJj/B9gHqv5F83NjYGFU/TyiOdV7Ao+ex87/wYb4wX5gv/H/E/cP949txy7Jl0nTypDlumzhRao8e9Xj9wxMnpMMynq8sI6/Gzfm8Pr9z/Hj5YBnnBsvE9vb3/3bGDGk6c6bPeN09d046UlPNsUbIX1r83F2v+fF3LAaRPl9eFhbaC7i5Ov/OMsRPd+0K6v3unz4tn6dMCfr71cKEr3bv9v33+/r10rZpkzkut+ZuuzV3w/Hz0P+HbMcxa9BbWlrMNvfly5fL7NmzJS8vTz59+uT2NQYOHCidnZ32Y/160KBBITtPBB3BBC5wgQtcYAMLuIRa7ePGmSii5o5rlXZfoocfExNN5Nrd9m2NtmqF83t9EIl0ziEO6fb5jIwerb9uWebP1v/8Ty9t1EzV+zNnIn6+3Nm61WNl8xdaNC/IegNa4+BTCNqbaaHCexs3+r59X1M1urbvaxcCXXQJ998xMWvQs7Oz5fLly/bjO3fueITRlxF0ctARQgghhFCoDLqaGjWaH8aP920b8YoVJg/bXe73ZcuUfh06tE+Kt6k5VJOuVeZDXgzOMuia4+6cj93QZegedOXhV7uoHG7fCh8Fc6Rm+3Z76z1V6fHjptr/864aBKFYJCk9dsykRwT7vb60vs9q6/v1d/FBuw7o1vinXlI8yEHvR4M+ePBg+fLlS7eI9nfffecxB12rsNtGSUmJMfmhOk8EHcEELnCBC1xgAwu4hNygW38/atTw2ZIlHguBdTNTv/4q3+Ljjalyd43m8/YFF63Y/T411fwczS5amgWj11Om/NVKrav3uymItnSpPe/cZu7ubdhgouXPHCqdayV8XfyIhvmiOyV0juj80J0THaNHywvLp+gijS5EaHE3LfIWzHtcPXlSOt3w8ketuk3eQ+G+HsXpdu827fB0PrcnJHRrMUgEPcwMulZRv3btmjHmHR0dUlxcLFlZWW5fQ1uwpaenS1NTk9TX10tiYmK3VmnBnqcPOoIJXOACF7jABhZwCbU6LJOlkWLT29uPquvaQuzy6dP9zqVh3jwTqb69a5cxjqGOoL+zzL9jNXo1pjVd27lNMbzMTFPRXh/X76Pk+HFz7tqhQ/LOTVQ/0uaLbut/ZfkgNbH6s1/oSl1QY67pDG8mTZLKAweCeg+z6yIEn58yV/a+Xq+7QDRlQ3++j8nJft0D/fU7JqINuhpvZ/l6XludaQT7f/7nf+Tf//63LFiwQF69euXR1Gvl9YSEBFMBvsD6sEN9ngg6gglc4AIXuMAGFnAJpTTyq5FeNTahblfWF1zUWJmt+cXF5mex9WEPhfR11YCrUbfnpaen2yuzX7f+1WNbH/Any5ZJY1e185sOueqRPl9u7N1rIuedTgb6wYYNpi2fLmL4Y4pdSSPx34YPD/p71W3yusPD1+vLjh2TLyNGmJ8lUn7HREUEPdRDK7t7yw/vi0EOOkIIIYQQCkYaFdXIr5oUVwXNwl2aB63GWL9umzTJmMlQvfZn629rNeG6lVsXAMyOA8ukXjl58i9zZxlBjb7q1m/NR9cocMeYMaYa+J3Nm02edjTMEW1jpws4mkrg+HhFYaEx6B+Sk83PHOz7WAbLzjlQaXFCbzs7HHXl1CnzvtrXPlI+Dwy6i6Fb0WtrazHorEbDBC5wgQtcYAMLFLFctG/5l+HDjQFVYxnpXDQHvHb16tDtLrDYKCM1oRo1v3T2rHwdMeJvc2cZQY0q63b/mp9/No9pj3jd7v5o7Vp57pCTHsnzRX92NeHa277btnT9+S1DrDswNOc+2PdRthctxgG/hmXudcHpgh8mX+sL+Fp7gQg6I+YNOnluMIELXOACF9jAAi69J40aapTYtk050rlorvgLD+3A/JEWufs2dKj5Wg2cRsO1Kv0Hi1U3Q2hd0zp5sr0wmRo+ZaqV8R/n5UXFfNHt61pArcGFkVVTrYsUV/yIWrs16GPHBvU6ugNE0xyi/XcMBh2Dzmo0TOACF7jABTZwgEUUctHe0xoZ1TxqrWQd6Vz05/mYlBSS17JFx/VrreytW9m1mrlzFFkjyMrQsaWctmLTqPJ9N3nNkTZf9GfTrf1/ulhw0MJqptJ9MJHvLnUkJEhJEFvNtUBfewgqwRNBZ2DQEUIIIYRQn8tW5EwLrUVFvrStUJxDLr0uPDibal+khcZsfbl1p4FWu3/444+m77njdXqNY166Lfqu2+PdRdAjbiHn6FHptLje3bSpxznlK3FxQeeOm8UO6z0q8/P9+96OHDHb2u058SkpUX/fYtAx6KxGwwQucIELXGADB1iEKReNODr26fZH2rv6dS9EzvuTiykU59AHWw3by5kzA9rW7bjtX82jbnWvXbOm23Vv09LMNnfnvOdbu3aZ3t7RcB9pv3stJOiq17n2nrelAgSrjxkZZpeCP8/RQn26QKCLIlrMTqvNE0HHoGPQyeeCCVzgAhe4wAYWcOkXafQ20MJo1du2yctZs6KKixZmczTRWgCvce5c/w2RZfJbp0yxH2urNc01r9m+vXsEWduP+Zn3HGn3kRaAUxNe5qJSuxbm+xqC9mhmsWPVKtNT3t+FFP3eSo8fl9s7dkjL9OnkoGPQMeis0sMELnCBC1xgAwu49J9Bf7xyZUDPvbdpU0DmNZy53NX2ZrZCcVrEbdgweb5okf+LF5bZezljhv24yeKkhdKuO/X71vSAdj//Bo+0+0i372uU2lWeuc69Lw6V7YNaXNm6tUcKgVeW+/f/tTX+wAGzBb8pJ4cIOgYdg44QQgghhPpHWqQs0FxnzakOxLyGdb70kSOmcJnZmn38uOHz3E/TZ4y+mj2HxQttn6bbvJ17bLdYJj5UhenCeYu7Lc/bWQ82bDD59qF4n5u7d/udcqFb4rVqvra5i8b5TA46Bp3VaJjABS5wgQuCDSwiiItGD+t++CGwbbOWsa9bvjy6uGihuBEjTKG4W3v2mArrDQH0uXY2e3e3bnWZa6357e8dW6/F2H10x+ISqi3udy5e9Hs3wp0tW+TD+PGm7/yf1n3Qm/OZCDqDHHTy3GACF7jABS6wgQVcPEqrlj9ZujSg5z5dsiTg/PVw5tI2caLJIVeTrVXGtSd5sIsXNy2z3zFmjEuD/s7PyuFRdR8VF5sCbSGZL48fm2j8JYcq/N6kEfw31uf9LDfX5MM/ciriRw46Bh2Dzio9TOACF7jABTawgEufSbddP7OMdiDPbbD+0HfXqzuSuWjk+9Hq1fI6K8tsQW8KoIWcGr5aH8xe1b59Jl+d+yg08+VdWppcLyjw+Tm6wKSF4bTugC7E3Nu4kQg6Bh2DjhBCCCGE+keaG1wfwBZulZoa3aIcbUw0f1x/Nq3grlHVF7aicf4sXsyfL/d/+ok51sfSz+3uli3ma+XvLb9fdzk0Wp+VttfT3QzamYAcdAw6Bp1VepjABS5wgQtsYAGXPteF8+dNXnRTgJXYX02bZvp1RxsXLRSnFde1WFz19u0B9UF/kZ1tCo9xH/XtfaR55E+6Ugu0QnzHqFGed0ssXmxM+qfERGPSb7jo1U4EnYFBJ88NJnCBC1zgAhtYwKXXdfm330zObiAG1JarXXXgQPTNl672apqbr1W+A+mN/WHCBLmzeTP3UR/fR7oo8nLWLHOsRl3bCHp6jm5r1zQNXajSz9y5Tz056Bh0DDqrizCBC1zgAhfYwAIufaKSoiJjYPxtTWWTVoBXAxuN80UjqlrFXaW56P48V5+j7dmuOfU85z7q/fuoorDQLI6YOgCLF5vPweNOB8vM37FMvV6nhQFj4XcMBh2DjhBCCCGEwlBlR4+ardxvMjICNrHlhw9Ht+mzTFvrlCm+L3qcOGFy16v272eO9YMunj9vCh9eKC6WhnnzTMs8T9fr4osuqMQSIww6Bp1VepjABS5wgQts4ACLMOSiEV7t8/3Bzz7cdoNumfvS48ejer7oFn7NTfYpp//33822/9pVq7iP+vE++pSUZBafNDquZt3Tc7TFWmV+fkz9jsGgY9DJc4MJXOACF7jABg6wCEMuVZYxeZueLu3jxgX0fI0UXz15Mqrny/WDB+WtLzsMiotNX/iWadPM19xH/XcfafFCTb3Q1A2Ji/P4ebyfMEEqjhyJqd8xGHQMOqv0MIELXOACF9jAARZhyOXm7t3SapkYb3m6bnPQredpobloni+6y0B7a3u7TttzabT28unT3Ef9fB89W7LE9Dd/n5oqX4cPl0tnzrh9jqZ4lPTiLhAi6AwMOkIIIYQQ8knVO3bIy+nTTQXrQJ7/dcQIuXj2bFQz0ujqex9SAEz18BkzmFdhIO1/rtXZP1vepmPMGI+7PHp7kYkcdAYGndVFmMAFLnCBC2xgARefpG3AmnNyTJTx4rlzfj//W3y8KcYVzfNFc5m1H7q36x7n5cmTZcu4j8LgPtICfW8mTTJt0z5qPvqvv7qfw0OHmtoBRNAjxKDX1dXJzp07jXEN5Hx1dbVMsibHf//3f8uAAQOMPI3y8nJJtn4BJFkTqaKiIuTnyUFHMIELXOACF9jAAi6OkcaG+fOlY+xYueJnLrkaczXo0T5fSo8dM8XwvF3XNHeu3Nu4kfsoDO6jK6dOSefo0aYN4LuUFLlWWOjyet39oYtTsfY7JqINem5urhRaH6g7Y+3pfG1trYzVFgtVVfL582ev71VTUyOpqanS0NBglJaWZh4L1Xki6AgmcIELXOACG1jAxVGP1q41vaI/JiZKmWVE/XmuRtx729yEw3zRXvHtPvyNrNXAteI791F43EdfRo0yCytaVb/KTZX2q5aR/zxmTMyxiYot7t4i367OL1y4UK5cueLze+Tk5Ehpaan9uKyszDwWqvPkoCOEEEIIIVfbst+lprqNMrqT5u0GWlwukqQ7C3SHgbfrOkaPNoaPeRUeep+SIu/Hj5dXmZlu+5yb9IWkpJhjE7MG/fvvv5fDhw/L0KFDZZT1y6u4uNjja8TFxUlbW5v9uLW1VeLj40N23nFoVF8/nPz8/G6Pp1gTWbc/2FZY9N9IPn706FFU/TyhOG5sbISHi2Ob4MF8Yb4wX/j/iPsnlu4fNecNGzbIpxkz5GFXBN3X51dfuWKij9E+X26XlspX6+d8UFQkzdnZLq9/cueOfBk5kt+3YfT75Z01p9Wkt82dK83W3HZ1ve54aI8y/+PuWP8fsh3HrEEfOHCgLNFeiC0txjgvWrRITp065fY19PrOzk77sX49aNCgkJ0nBx3BBC5wgQtcYAMLuDjq+eLF8nDtWtM3+tauXf5t/T5+3LSoivb5cuncOfkyfLjc2bLF7Zb+yvx8swuB+yh87qMX2dmm+r7WBri7aZPL6x+uW+dTfQFy0KPEoGv++YcPH+zHr169kuHWTR0OEXRy0MnNQXCBC1zgAhtYxBYX3ZJ+/dAhYzSf5uZK+7hxpkCcFop7MXu21Gzd6tfr+VrdPNLni1b41krfuoBhiuK5qFqvPD8lJnIfhdF9dP3gQbm9c6fUL1woDywj7ur6Z9bn9njlSnLQY8Wg5+XlSVNTk/34zZs3pqK7pxx0rcJuGyUlJZKdnR2y8+SgI4QQQgjFrjRKrnnSGln80/o7VStcv5w50xj21ilTjFnx5/V87Q8eDRLLmN/esUO+DhsmV4uKepzX6u2Nc+cyz8JQunhSu2qVy3NvMzJMSzZy0GPEoD948MD88LrF/e3bt8awV1ZWun0NbcmWnp5uTH19fb0kJiZ2a5UW7Hki6AgmcIELXOACG1jELhfnNmCvMzNN5fFqy3g2zpsnzxctsp+7eP68fEhO9tgb/dqhQ/IuLS0m5otG0HUhQxc4Kl1UatcIrUZquY/C7z7684cfpG7Fih6Pa4s1TV3QuU4EPYIMuq13uaP8Oa8AdKu7bjW/ePGiV1OvldcTEhJkyJAhUlBQEPLz5KAjmMAFLnCBC2xgEZtc3kya1K0N2NMlS0wF65t79si9n34yJt12rrorWlx+5Ijb19O8azX4sTBfvo4YIfe1mF5iojHqzudr16wxPLmPwu8+emR9Ns9cfDb3Nm2KmR0gUZmDHuqhkXRv+eF9MYigs7KI4AIXuMAFNrCIDS7ObcDubt4sHWPGmIjwzb17zTZ3x+h62+TJcn/9evff87593Z4TzfNF0wHU6OmOAW1N5zJKu3w591EY3kc6hxssQ+r8uKZ3vJg1KybZYNBdDN2KXltbi0FHCCGEEEK9Li0QZ2sDZt+ifviwiZJr//OyY8fsRc60+Ju2T9OIcfOcOW5fU3tLa4/pWOCnCxlqwnXhQgvrOZ9/snSpPHaT54z6V3c2b5bmnJzu98OZM9I5alTM9q3HoIfxIILOyiKCC1zgAhfYwCK6uWildS0K55wvfuH8eVP8TIu96ddq1i8UF5vWa0+WL5eyX381ld7dva4WTWuZMSMm5stn629kNeHNs2fL66lTe5zX/P2HP/7IfRSG95Gma7x0mqf6WelnGatsMOgYdPLcYAIXuMAFLrCBAyz6iYuabO3R/cKFIVHTXtaVZ67Xlf3yi9kKX3LixF/GdOxYKT1+3OXr1mzb1utbhMNlvmiv7OcLF8qzxYtN3r7zeVu7Ou6j8LuPNH3DeVHlw/jx3eoxkIOOQcegs7oIE7jABS5wgQ0sUJ9w0a3rGh1Xc+npuraJE01LKs3NtT2mLdnuuiiK5m7rcLTOF92FoEX0tF2XsnTuha4clAf3UfjdR5UHD8qbjIy/r7EM+/uUlJhmg0HHoCOEEEIIof7Knx471vR71i3anq5Tk9mekGAijvatwOvWucy5Vpne3w6V36NZWu1bdyA8WL/ebHcvceqFrosa1du3M9/CUJrC4Vit/YX1WT3YsCGmmWDQMeis0sMELnCBC1xgAwdY9BMXjfh66mduk/ZJ12Jojo9pITndDuzqejWr9QsWxMR80RQBzbfX1lzaWq4qP7/b+VdZWXJr927uozC8j64XFNgLJF4tKpLO0aPl0tmzRNAx6Bh08txgAhe4wAUusEGw6FsuF37/Xb4NHerTtY/Wru3ZL7q4+K9q104RY/v1XrbNR8t8eZuebiq412zdaoqLOW9n15Z0Vfv3cx+F4X106dw5Y9B1Duv8rl+4MObZYNAx6KzSwwQucIELXGADB1j0A5crp0+biGEwr6GF4h6vXNnjcc3Hfupl23y0zBfNz2+bNMlUrn+ybFmPXuhq4DVSy30UnveRLqpoP3TdTXJr166YZ4NBx6AjhBBCCKF+UKn2N09ICOo1tFCcRo57ROQsk1q3fHlMcGydMsWY8Jt79pj+8M55+ZrjXNFVDR+Fn7TQoXYsaJk+HR4YdAw6q/QwgQtc4AIX2MAGFv3DRXPI36emBvUajW5aiGmvdOdIcrTOl9dZWcaEa2uum7t392jbpZXydTGE+yg876Orp07Jt/j4mGutRgQdg06eG0zgAhe4wAXBBhZhxEXzonVrdjCv8XzxYpNv7vz40yVLpHbNmpiYLy3Tppn+59cPHZKyo0flo2XIu6UBjBljTCD3UZjeR8XFZq5qTQbYYNAx6KzSwwQucIELXGADB1j0C5fbu3bJK8tcBvMauo39TxeR8ueLFsnDH3+MifmibdTax42T8iNH5OL58z16oWsRsktnznAf8fuFCDoDg44QQgghhFzrTlfV8WBew10xuAbrj/z7MdJPWvPwtQWdbRu76YV+4oT9vFbKj+XoLCIHnYFBZxUNJnCBC1zgAhtYwMWLtHK1GulgXuPBunUuW1Np33TtCx4L86U5J0c6tVXXyZPm+M2kSfZ8Zn9a2XEfwYUIOoMcdG5ImMAFLnCBC2xgEaNcalevNrniwbzGXcuEqxnvYVq1H7iL6u7ROF8a582Tr8OHy+WubezNc+aYyuD6tW5t1y3u3Ef8fiEHnYFBZxUNJnCBC1zgAhtYwMXv/HF/VL1tm7ycNatnXrb1mJ6LhfmiuxDMNvbz582x9kK3cdWouvaK5z7i9wsRdAYGHSGEEEIIuZUp5OaiArs/Mm3FsrJ6PK6VzbUIXUxwXLhQJC7u79SBn34y7ef069Jff5VPSUnMN0QOOgODzioaTOACF7jABTawgIuHrdmaJ75xY1CvUXnggMm5dn5ce4Hf3Ls3JuaLGvSvDnnmN/fssfdCrzhyxPRI5z7i9wsRdAYGnTwUmMAFLnCBC2xgARe30m3oNdu3B/UaFYWF8j41tcfjbZMnmz7rsTBfdCfCl+HD7ceOvdCvFxTI2/R07iN+v5CDzsCgs4oGE7jABS5wgQ0s4OJeJsq9Z09Qr2G2cHeZUUe9yciQyoMHYyOCbhn0TodCcI690G/s328WK7iP+P1CBL0PRl1dnezcudMY10DO20ZRUZEMGDDA6/uVl5dLcnKyJCUlSUVFRcjPk4OOEEIIIRT9unj2rLycOVPehsBEXzl1yvQAd378XWqqXCssjJlc/o7Ro7s9ZuuFfmv3bnnlIkcfIXLQe2Hk5uZKofWLx5259nZex4MHDyTV+gXmzaDX1NSY6xoaGozS0tLMY6E6TwQdwQQucIELXGADi9jgUn7kiInwqoks/+WX4Mz+uXOmxZjz4x/Gjw/6tSMmgr54sXx2WqRomzjRbPF3V+We+4jfL0TQe3F4M9fuzr99+1ZSUlKkqanJ62vk5ORIaWmp/bisrMw8Fqrz5KAjmMAFLnCBC2xgERtcarZulVdTp8q3+Hi5dvhw0K+nr3OhuLjbY1q5XLe/x0oOunMrtWbr7+y7mzebfuj6NfcRv1/IQQ9zg/7t2zdjkG/evOnTa8TFxUlbW5v9uLW1VeKtX4ahOk8EHcEELnCBC1xgA4vY4PJ06VJ5nJdnqqw7G+tA9GXkSLl85ky3x9p1i3dRUczkoLc7/Z38pKvHvLZca+hqucZ9xO8XIuhhbNB37dolx48f9/k1Bg4cKJ2dnfZj/XrQoEEhO+84qqqqzIeTn5/f7XGN9uvqiu0D1H855phjjjnmmGOOOY6s44+zZ8vjrsh5KF7vS1KS1Fy61O1855gxcuX06Zjg+XbNGvncVSjPdv7pjh3SOG+ePN+yRd6sXcv84zhijmPWoP/jH/8wjzuLCDqraDCBC1zgAhfYwAL1JhctaHY1hNHtD8nJUn70aI+o+iWnqHq0zpf6hQvlU0JCt8d0d0Lr1KnSPHu2tMyYwX3E7xci6JGQg+7PNbodXquw20ZJSYlkZ2eH7Dw56AgmcIELXOACG1hEPxetLO6cLx2s3qalydPcXGmdMsVUh9e2YhIfL5fOno2J+VK/YEGPVnNl2n4uKclUcH+8ciX3Eb9fyEGPNoNeXV0t6enppqBcfX29JFq/BBxbpQV7ngg6gglc4AIXuMAGFtHP5faOHfJq2rSQfn+tliHXnuovsrOlevt20/u72nqfP0KQ3x4J80Xz73Xhw/GxC1290HUxpPT4ce4jfr8QQe8rY+5pi7o/W9hdPdd5aOX1hIQEGTJkiBQUFIT8PH3QEUIIIYSiPFK2YoU8WbYspK/ZYhn+dutvzOuHDsHYQWrO2/n7GUWYoiKCHupRWVnpNT+8LwYRdFYWEVzgAhe4wAYW0cXl5YwZUrN9e0i/P33NLyNG9HrEPNLmy8fERHmdmcl9xO8XIuiRPnQrem1tLQadPBSYwAUucIELbGCBQsqlfdy4kPcn1+3t71NSmC9O0hx0reQOF36/kIPOwKCzigYTuMAFLnCBDSyiRB8nTpRHa9YE/Tra9qxz1KiQf39vMjLkxaxZzBcnXfrtN7l47hxc+P1CBJ2BQUcIIYQQippcZutvMK2SHuzrmNZfU6b0zvfZx9vbEULkoGPQiaCzsggXBBe4wAU2sOhzdSQkyLPFi4N+ndpVq0LyOswXuMCFCDoGHYNOHgpM4AIXuMAFNrCISX1JSpLnCxcG/TrNs2fL3c2bmS/cR3BB5KBj0FlFgwlc4AIXuMABNrAIKII+bpw0WH80B/s6H5KTpeLIEeYL9xFcEBF0DDpCCCGEEAooB33sWGmcO9ftec1Pv71zp8fXuHT2rHwZPlwukCuOECIHHYPOKhpM4AIXuMAFDrCBRWDqtAx6c3a2+wj76NHyZNkyj69RlZ8vbzMymC/cR3BBRNAx6OShwAQucIELXOACG1gEqq9jxsjLGTNcntPWaV+HDpXHK1d6fA3dIv8qK4v5wn0EF0QOOgadVTSYwAUucIELXGADi4AN+siRbs11zbZt8n7CBK/V2dWg14eg0BzzBS5wIYKOQcegI4QQQgjFrL4NGyZtkya5PNeUkyPNc+ZIs/Wvp9d4tmSJPFqzBp4IIXLQMeisosEELnCBC1zgAhtYBCqJj5d3aWkuz30ZOVKeLF8urzMzPb5GvfVH94MNG5gv3EdwQUTQMejkocAELnCBC1zgAhtYBKILv/8u34YOlY/JyS7Pt1t/n93as0fepaR4fJ2muXPl7qZNzBfuI7ggctAx6KyiwQQucIELXOACG1gEIm2Ppjnon938HfZ12DApPX7c7XmbXsycafLVmS/cR3BBRNAx6AghhBBCKABplfaOUaPMVvYe5v3cub96m3dF2f/w0OP81bRpcmvXLpgihMhBx6CzigYTuMAFLnCBC2xgEYhKTpyQjsRE+RYf38OAa+S8PSHBfN1pmfjLlpl39zqtkyfLjX37mC/cR3BBRNAx6OShwAQucIELXOACG1gEorJff5WOlBQTKdeIueO5a4cO2YvHfRg/Xsp/+cXt67xNT5frBQXMF+4juCBy0DHorKLBBC5wgQtc4AIbWASiiiNH5JNlwj+PHStXT57sdu7m7t326u1tGiHfv9/t62ivdH0t5gv3EVwQEXQMOkIIIYQQCkAa9dbo98ekJCk7evSvre3HjskHy3Df2bJFmmfPNo+9mDVLan7+2e3rfEpMNM+DKUKIHHQMOqtoMIELXOACFzjABhYBqOrAAXmfmWm2suuWdn3s+ZIlJif90dq18nzhQvOYtlF7nJfn9nU+jxkjV0+dYr5wH8EFEUHHoJOHAhO4wAUucIELbGARiG7u3Ssfs7PNFvaq/fv/ykkfPdpExOsXLZK6H34w17XMmCEt06e7fR2tAn/pzBnmC/cRXFD05qDX1dXJzp07jXH19/yAAQOMBg8eLJmZmVJfX+/1/crLyyU5OVmSkpKkoqIi5OeJoCOYwAUucIELbGARXrpt/S35ZvZs0ybt9q5d8tIy4rWrVkmLdayPPVy3zlyn29tfzprl9nU04n7BQxs25gtc4AKbiDfoubm5UlhYaIx2IOd1tLe3y/79+yUjI8Pje9XU1Ehqaqo0NDQYpaWlmcdCdZ4cdIQQQgih8FPNtm3yYuZMeWGZ9Lrly03k/OL58/JsyRKz7f3O1q3muorDh01euqvXuGBd/23YMHgihGIjB92TAfflvJp0jaR7Gjk5OVJaWmo/LisrM4+F6jwRdAQTuMAFLnCBDSzCT3c2b5bXCxZIw/z50j5unDHs+vj99eul3frb7OaePeZYTfvX4cNdRskv//ab6ZPOfOE+ggscYiIHPViDrsZ58uTJHq+Ji4uTtrY2+3Fra6vEx8eH7Dw56AgmcIELXOACG1iEn+799JO8++EH007tU1LS339Q79snX0aMsBeOU2l0XXPUnV/jalGRfI7CnZDMF7jAhRz0kBv0pqYms93cWw76wIEDpbOz036sXw8aNChk5x1HVVWV+XDy8/O7PZ6SkmI+PNsKi/4bycePHj2Kqp8nFMeNjY3wcHFsEzyYL8wX5gv/H3H/9PXx823b5MOPP8rT3bul2vradr7m8mWTV37n4kX79a+ysuRxYWGP13teUSHt48czX/h9y+8XeLg81v+HbMcxbdC1iNz06dOlubnZ63sQQUcIIYQQij09Wr1anuXmui38dvX0afvxs8WLzfXO1107fFjep6bCEyFEDrq782rKp02bJu/evfPpPTRfXKuw20ZJSYlkZ2eH7Dw56AgmcIELXOACG1iEn7S3edOaNT5d+2DDBpOr7vjYx6QkebxqlbyZOJH5wn0EFziQg+7ufFZWlty/f9/n96iurpb09HSzJV63wycmJnZrlRbseXLQEUzgAhe4wAU2sAg/aeX21p9/9u2P7H37TL9027H2Pdcoe92yZfJ66lTmC/cRXOAQ3Tnotl7mjvL1vC/PdR5aeT0hIUGGDBkiBQUFIT9PBB3BBC5wgQtcYAOL8NLTJUukftMmn67VYnAdY8faj2u2b5d262+/+gULpGX6dOYL9xFc4BAbEfRQj8rKSq/54X0xyEFHCCGEEOpfPV+4UB6uW+fz9V9GjjRt1fTrxnnzjDFvmTHD9FGHJ0IoJnLQQz10K3ptbS0GnVU0mMAFLnCBC2xgEePSnPKnO3b4fP1b6+/IyoMH5Y/iYvk2dKjJYdf8czXrzBfuI7jAgQh6BA9y0MnNQXCBC1zgAhtY9K+a5syRl4cP+3X93U2b5ML58/Jt2DC5sXevKRRXv3Ah84X7CC5wiO4cdAw6q2isLMIFLnCAC1xgA4ve1IvsbKk7cMDn6zVi/mTpUik9ftzkn5cdOyado0fLUzet2pgv3EdwQUTQMegIIYQQQsgHaQ757Z07fb6+Zts2eTlzplTl55ut7Rc1kq6V3FesgCdCiBx0DDqraDCBC1zgguACG1gEqteZmVL7yy8+X19x5Ih8GD9ean7+WV7MmmUe+zp8uGnXxnzhPoILHIigY9DJQ4EJXOACFwQX2MAiQGlf88bff/f5eo2Yfx02TB6tWSPPFy82j3WMGSNPly5lvnAfwQUO5KBj0FlFgwlc4AIXBBfYwCJQaVX2B6dO+fWcT0lJ0jBvntSuXm2OTS/0RYuYL9xHcIEDEXQMOkIIIYQQClQa/b65Z49fz3k1bZqJvN/ZssUc1y1bJtXbt8MTIUQOOgadVTSYwAUucEFwgQ0sAjboY8fKvbNn/XrOs8WL5VNiotzYt4/5wn2E4EIEHYNOHgpM4AIXuCC4wAYWodCX4cPlyf37fj3n/vr18mXECCk7epT5wn2E4EIOOgadVTSYwAUucEFwgQ0sgtUFbZE2bJjfXG7s329aq130M/LOfIELXGCDQcegI4QQQgghF7p68qR8HjvW7+eVHz0qYhl0GCKEyEHHoLOKBhO4wAUucIALbGARApme5hMm+M+luFjKf/2V+cJ9hOBCBB2DTh4KTOACF7jAAS6wgUUoVLV/v6nGDhfmC1zgQg46gwg6K4sILnCBC1xgA4t+VPWOHfJyxgy4MF/gAhci6Axy0BFCCCGE+lP3Nm6UxnnzYIEQIgedQQSdlUUEF7jABS6wgUV/qnbNGnm6ZAlcmC9wgQsRdAY56OTmILjABS5wgQ0s+lNPli6VxytXwoX5Ahe4RH8O+uvXr2X9+vUycuRIGTx4sKxbt848PmDAAPvXGHQi6KwsIrjABS5wgQ0sApH2MG+dMiWo12iw/lC+v2EDc4R7By5wif4IelpamowbN05aWlq6mXL9Wo07gxx0hBBCCKFAdfXUKZG4OK/XfRkxwlRrd3XuxcyZUrNtGzwRQtGfgz5s2DDJy8vrFjXXqLp+fe7cOdw5EXRWFhFc4AIXuMAGFgFLe5irQb/w++/urysulm/x8XJ7xw6X5zUCf2PfPuYI9w5c4BL9EfSSkhIZOnSo3L1715jylStXypo1ayQ9PV06Ojp8eo26ujrZuXOnMa6BnC8vL5fk5GRJSkqSiooKr+/n7fpgz5ODjmACF7jABS6wgUVodHPvXmO+L5054z7KXlQkX4cPl4c//ujy/PvUVLl2+DBzhHsHLnCJ/hx0Hbdu3ZKpU6fKv/71L0lISJA9e/ZIe3u7z8/Pzc2VwsJCY/D9PV9TUyOp1i/dhoYGI91yr4+5G96uD/Y8EXQEE7jABS5wgQ0sQqc7W7YY833l5Em311Tm58sn62/Q5wsXujzfPm6clJw4wRzh3oELXKI/gh7K4c6gezqfk5MjpaWl9uOysjLzmLvh7fpgz5ODjhBCCCEUOj1as0a+jBwppcePuzfxW7dK25Qp8iozs8c53Rr/behQuXL6NDwRQtGfg97fBj0uLk7a2trsx62trRIfH+/2NbxdH+x5x1FVVWU+nPz8/G6Pp6SkmO0PthUW/TeSjx89ehRVP08ojhsbG+Hh4tgmeDBfmC/MF/4/4v7x9bh55UrpGDNG7p4/7/b6x6tWSUturnRaf2M5n687cEDedT3O/cPvW37fMl9681j/H7Id95tBV9PsTv62WQvEoA8cOFA6Ozvtx/r1oEGD3L6Gt+uDPU8OOoIJXOACF7jABhahU9PcuWb7+rXCQrfXNCxYIA+tvzu/Dhtm2rI5nmubPNlewZ05wr0DF7jERA6649B2a1o07vvvv/crDz0aI+jkoJN/guACF7jABTawCE6vs7Lkw/jxUnnwYI9zl3/7TeqWLTNb22/t2iUftYCvg5HXrzX/3FYBnjnCvQMXuMRkDvqlS5eMmT5z5kyf5KBrVXXHqvLZ2dkec9A9XR/seXLQEUIIIYRCJ63A/jY93bRJcz6nkXHNL29PSJDyX36Rz2PHyuO8vL8j6/PndztGCKGYzEHXyLma6W3WL83eNujV1dWmpVtTU5PU19dLYmKix9Zn3q4P9jwRdAQTuMAFLnCBDSxCp89jxkjr1KkmQu587mlurjRroCQ+Xi6fPi1PlyyR2lWr7NH1zlGjulV/Z45w78AFLjEZQVcTq2b62LFjAeex+3NeK6lre7chQ4ZIQUGBV1Pv6fpQnCcHHcEELnCBC1xgA4vgdaG42ETIX86cKdVdeeSO0uJv2mLtz5UrzfG9jRtNzrp+rdXfm+bMYY5w78AFLrGVg+7KPH/33XeyZMmSbsXU+mNUVlZ6zQ/vi0EEnZVFBBe4wAUusIGF/yopKjIV3Jsto6390B3PXT11ykTI1cTbHlOz/jYjQ/6wHvuYmCjXnfLWmSPcO3CBS0xG0MNl6Fb02tpaDDpCCCGEUASqescO+TJihDTOny/3fvqp27m7lmF/OWtWt8d0W7v2TL+9a5fJW4chQogcdAYRdAQTuMAFLnCBDSxCoNs7d0rL9OlSv3ChPFi3rtu5ppwcub9+veuc9SlT5O6mTcwR7h24wCU2Iuieep8H0wcdg04eCkzgAhcEF7jABhY2qSmvX7Dgr+Jva9Z0N+Jjx0rpsWM9nvMmI8Nsfb/o1A+dOcK9Axe4xEQOOgODzioaTOACF7jABTaw6A09XbpUHq9cKXXLl8ufDu3SKg4fNj3PXT3nXWqqvM7MZI5w78AFLuSg24a2IPvXv/7lU4VzDDpCCCGEEHIlUxxu82Zj0p8sW2Z//NHq1Wbbu6vnXC0qkiunT8MPIRSbOejacmzEiBHyj3/8o9v29sGDB0tVVRXunAg6K4sILnCBC1xgA4uA1DZpklTt329apj1bssT++OusLFNAjjnCfIELXIigO41Ro0aZaukdHR0ycOBA2bBhg2lvpoa9rq4Od04OOrk5CC5wgQtcYAOLgPQpIcHkmT9Yv97koutjmluuldovnTnDHGG+wAUu5KA7j0GDBsnq1avN19pzPDc3Vz59+mSi6Hv37sWdE0FnZRHBBS5wgQtsYOG/iovl27BhcuH33+Xexo3SOG+eefzmnj0mss4cYb7ABS5E0F2MjIwMSUhIkG/fvsnKlSvl+++/l5s3bxqDnp+fjzsnBx0hhBBCyG+VFBXJ566/me5s3SrNs2ebr99MnGhy02GEECIH3cW4deuW/Nd//ZcUFhZKW1ubTJ8+Xb777juZMWOGfPjwAXdOBJ2VRQQXuMAFLrCBhd+qPHhQ3mZkmK813/yl9belbnfXFmpaxZ05wnyBC1yIoHeNlJQUOX78uHz8+BH3jUEnDwUmcIELXOACG1iEXDU//ywvZs0yX9/as0deZWbKx+Rkee6mejtzhHsHLnCJ2Rx0Lf62fPly+c9//mO2tT948AAXTgQdwQQucIELXGADi5BJW6k9W7z4r59n/35pnTxZPlgGXSPrzBHmC1zgQgTdxWhpaZFNmzaZnucTJkyQEydOmAJxDHLQEUIIIYSC0XPLnD9au9Z8fb1ru/vnMWPk6smT8EEIkYPuaWiu+YEDB0yBODXrq1atkocPH+LMiaCzeobgAhe4wAU2sAhIL2fNkprt283XFYWF8j41Vb4NHWqquzNHmC9wgQsRdB+G5qRnZmaaCu7aB10j6gxy0MnNQXCBC1zgAhtY+Ku36ekmcq5flx09Kp+SkuxV3ZkjzBe4wIUcdA9Dt7UXFBRIXFycDBkyRDZu3CjPnz/HmRNBZ/UMwQUucIELbGARkMx29qIi83XJiRPyeexYeZeWxhxhvsAFLkTQ3Y03b97Ijh075N///rfph37mzBnp6OjAkZODjhBCCCEUsC6ePy9fhw2zb2e/cvq0dI4YIS3TpsEHIUQOuvNobm6WdevWmZzzNWvWmFA+gwg6gglc4AIXuMAGFqFQ2bFj8ikx0X586exZY9gb5s9njjBf4AIXIujOIz093eSXt7e3474x6OShwAQucIELXGADi9D+kbtvn7RNnmw/vlBcLN/i46Vu+XLmCPMFLnAhB52BQWcVDSZwgQtc4AIbWPSV7m7eLE1z5nR7TOLi5OG6dcwR5gtc4EIEvbdHXV2d7Ny50xhXV6O8vFySk5MlKSlJKioqup1raGiQuXPnyn/+8x/55z//aXLhq6urPb6fp9cLxXly0BFCCCGEAtfjvDx5smxZt8e0xdqdrVvhgxAiB723R25urhQWFpr2bM6jpqZGUlNTjRFXpaWlmcdsQ43ysWPHTIs37cd++PBhkxvvbnh7vWDPE0FHMIELXOACF9jAIjg1LFggD9av72HQb+3ezRxhvsAFLkTQ+2q4Mug5OTlSWlpqPy4rKzOP2Ya2dXv06JH9+MmTJyay7W54e71gz5ODjmACF7jABS6wgUVwejVtmtzetauHQa/s6ovOHGG+wAUu5KD3k0HX3uptbW3249bWVomPj7cfX7x40RxXVlbK/fv3ZdasWfLs2TO37+Ht9YI9TwQdwQQucIELXGADi+D0fsIEqSgs7P54V8s15gjzBS5wIYLejwZ94MCB0tnZaT/WrwcNGmQ/bmlpMdvcly9fLrNnz5a8vDz59OmT2/fw9nrBnnccVVVV5sPJz8/v9nhKSopZXbF9gPovxxxzzDHHHHPMMcd/HXeOHCm3S0vhwTHHHEfsccxG0LOzs+Xy5cv24zt37niEQQSdVTSYwAUucIELbGARvrp05ox8GTECLswXuMCFCHq45qBr1XTbKCkpMabcNgYPHixfvnzpFtH+7rvvPOage3q9YM+Tg45gAhe4wAUusIFF4Ko4ckQ+jB8PF+YLXOBCDno4GnRtmZaeni5NTU1SX18viYmJ3VqbaRX1a9euGWPe0dEhxcXFkpWV5fY9vL1esOeJoCOYwAUucIELbGDhv64WFZk8c63U/tr6Ww4uzBe4wIUIej8ac2c5Dq2UnpCQYCq2FxT8//bOxC2Ka9vb/5q5R5NzkpvP3JtHHNEjgzI4RY0KorlOGA1qjMYYNSpOQUTjPCckDmFSiAqOkIMDg1NAnEVdX61tV6e66REaeuD9Pc/voat3V3X3y66qXnvvtXdxl3XQtQf7o48+ko8//ljmzZsnDx8+DBj0BzpeJMpZBx1jjDHGODy/TUqScyUlcm3NGmn+4guYYIzj2gnRgx5p6czuwfLD+0L0oNOyiOECF7jABTawCBKgDx0qtT/8ILfy86Xxq6/gQn2BC1zoQU806VD0hoYGAnTyUGACF7jABS6wgUUc9KA3rFhh8s+brR+2cKG+wAUu5KAjAnRa0WACF7jABS6wgUUULFaArkPbn6ekSMXevXChvsAFLvSgIwJ0jDHGGOO+9m+//GKGuHdkZ8ur5GSYYIzJQUcE6LSiwQQucIELXGADi2jYrH0+cqRZ//zBjBlwob7ABS70oCMCdPJQYAIXuMAFLrCBRTR89uhR03P+ZvhwublkCVyoL3CBCznoiACdVjSYwAUucIELbGARDZcdOiQvrN9Hr0aPlmvffgsX6gtc4EIPOiJAxxhjjDGOhiv275fnaWmwwBiTg44I0GlFgwlc4AIXuMAGFtF05Z498nT8eLhQX+ACF3rQEQE6eSgwgQtc4AIX2MAimj63a5c8zsqCC/UFLnAhBx0RoNOKBhO4wAUucIENLKLp6p075dGECXChvsAFLvSgIwJ0jDHGGOOo/qjdvl3apkyBBcaYHHREgE4rGkzgAhe4wAU2sIimL27dKg+nToUL9QUucKEHHRGgk4cCE7jABS5wgQ0soulLmzbJgxkz4EJ9gQtcyEFHBOi0osEELnCBC1xgA4toum7DBrmXkwMX6gtc4EIPOiJAxxhjjDGOpq98/7205uXBAmNMDjoiQKcVDSZwgQtc4AIbWETT19askeYvvoAL9QUucKEHHRGgk4cCE7jABS5wgQ0soukbq1bJnfnz4UJ9gQtcyEFHBOi0osEELnCBC1xgA4toun7FCrmdnw8X6gtc4EIPOiJAxxhjjDGOpv9ctkxufvklLDDG5KAjAnRa0WACF7jABS6wgUU03bhkifynoAAu1Be4wIUedESATh4KTOACF7jABTawiKZvLV4sDcuXw4X6Ahe4kIMeC2psbJTNmzebwNWXKisrJT09XdLS0qSqqqpLeW1trUyaNEnef/99GTBggHEgBTteT8vpQccwgQtc4AIX2MAidN9ZuFBurFwJF+oLXOBCD3osKD8/X0pKSnwG1nV1dZKZmSnNzc3GWVlZ5jlbDQ0NMmbMGKmpqZGXL18Gfa9gx+tpOTnoGGOMMcbhuXnePLn+7bewwBiTgx5L8hWg5+XlSXl5uXu7oqLCPGdr/vz5cvbs2ZDfI9jxelpODzqGCVzgAhe4wAYW4bllzhy5unYtXKgvcIELPeixHqAPGTJE2tvb3dttbW2SlJTk3v70009l9+7dMnToUBk1apSUlpYGfI9gx+tpOTnoGCZwgQtc4AIbWITnu7m5cnn9erhQX+ACF3LQYz1AHzhwoHR2drq39fGgQYM8yhctWiQPHjwwgfOCBQvkyJEjft8jlOP1pNwpHXav/5yioiKP5zMyMsw/z25h0b/xvF1fX59Q3ycS2y0tLfDwsW0bHtQX6gv1hfsR549z+/6sWfIf6/cS5w/1hest9SWet/U+ZG/32x50zT9/+vSpe/vhw4cyfPhwetAxxhhjjOPED6dPl4uFhbDAGJODHg856Dpruq2ysjLJyclxbxcUFEhra6t7+9GjR2ZG90A56IGO19NyctAxTOACF7jABTawCM9/ffaZXNi2DS7UF7jAhRz0WA/QdQm17OxsE4Q3NTVJamqqx9Jm169fN19eh7h3dHSYgL26utrvewQ7Xk/LyUHHMIELXOACF9jAIjy3T5okNT/+CBfqC1zgQg56rATm3nZKZ0pPSUmRwYMHS3Fxsc8AWIe661DzU6dOBQ36gx2vp+X0oGOYwAUucIELbGARujuys+W89ZsKLtQXuMCFHvQElvakB8sP7wuRg44xxhhj7N9PMjKkavduWGCMyUFPZOlQ9IaGBgJ0WtFgAhe4wAUusIFFDPtZerpU7N0LF+oLXOBCDzoiQCcPBSZwgQtc4AIbWETTL1JSpOzAAbhQX+ACF3LQEQE6rWgwgQtc4AIX2MAimn45erT8fuQIXKgvcIELPeiIAB1jjDHGOJruHDVKzhw7BguMMTnoiACdVjSYwAUucIELbGARTb8ZNkxOnTwJF+oLXOBCDzoiQCcPBSZwgQtc4AIbWETTkpQkv5aWwoX6Ahe4kIOOCNBpRYMJXOACF7jABhbR8m+//CJvhw6FC/UFLnChBx0RoGOMMcYYR9O/Hzr0rgcdFhhjctARATqtaDCBC1zgAhfYwCJ6PnPkiJkkDi7UF7jAhR50RIBOHgpM4AIXuMAFNrCIZg/64cPycswYuFBf4AIXctARATqtaDCBC1zgAhfYwCKaLjt4UF6MHQsX6gtc4EIPOiJAxxhjjDGOpiv275dnqamwwBiTg44I0GlFgwlc4AIXuMAGFtF05U8/ydNx4+BCfYELXOhBRwTo5KHABC5wgQtcYAOLaLqqpESeZGbChfoCF7iQg44I0GlFgwlc4AIXuMAGFtH0+eJi6cjOhgv1BS5woQcdEaBjjDHGGEfT1UVF8mjiRFhgjMlBRwTotKLBBC5wgQtcYAOLqH7WHTukbfJkuFBf4AIXetARATp5KDCBC1zgAhfYwCKavrBtm/z12Wdwob7ABS7koCMCdFrRYAIXuMAFLrCBRTR9sbBQHk6fDhfqC1zgQg86IkDHGGOMMY6mazdtkvszZsACY0wOOiJApxUNJnCBC1zgAhtYRNN1GzbIvZwcuFBf4AIXetBjRY2NjbJ582YTuPpSZWWlpKenS1pamlRVVfk9zqFDh2TAgAFB3y/Y8XpaTg46hglc4AIXuMAGFqH58vffy928PLhQX+ACF3LQY0X5+flSUlLiM7iuq6uTzMxMaW5uNs7KyjLPeev69evmdcEC9GDH62k5PegYJnCBC1zgAhtYhO6ra9dKy5w5cKG+wAUu9KDHmnwF13l5eVJeXu7erqioMM851dHRIRkZGdLa2ho0QA92vJ6Wk4OOMcYYYxy6r337rTRbP2RhgTEmBz0OAvQhQ4ZIe3u7e7utrU2SkpLc22/fvjUB8oULF/weI5zj9bTcqZqaGvPPKSoq8nheGxN0+IPdwqJ/43m7vr4+ob5PJLZbWlrg4WPbNjyoL9QX6gv3I84fe/vODz9I0/z5nD/UF6631Je439b7kL2dsAH6wIEDpbOz072tjwcNGuTeLiwslAMHDgQ8RjjH62k5OegYJnCBC1zgAhtYhO76lSvl9sKFcKG+wAUu5KAnQg/6e++9Z/bzdiz0oJODTv4Jhgtc4AIX2MAisBuWL5dbixfDhfoCF7iQgx4vOeg6a7qtsrIyycnJCesY4Ryvp+XkoGOMMcYYh9HjVFAgjUuWwAJjTA56PATotbW1kp2dbSaAa2pqktTU1IBLmwUL0IMdr6fl9KBjmMAFLnCBC2xgEbo1ONcgHS7UF7jAhR70GArMAw1R15nSU1JSZPDgwVJcXBxWkO8rYA92vJ6Wk4OOYQIXuMAFLrCBRWi+uXix/Ll8OVyoL3CBCznoia7q6uqg+eF9IXrQaVnEcIELXOACG1j49u1Fi6T+66/hQn2BC1zoQU906VD0hoYGAnSMMcYY4xj1nfnz5cY338ACY0wOOiJApxUNJnCBC1zgAhtYRNPN8+bJtdWr4UJ9gQtc6EFHBOjkocAELnCBC1xgA4toumXuXLn63Xdwob7ABS7koCMCdFrRYAIXuMAFLrCBRTTdOnu2XFm3Di7UF7jAhR50RICOMcYYYxxN38vJkboNG2CBMSYHHRGg04oGE7jABS5wgQ0soun7M2dK7caNcKG+wAUu9KAjAnTyUGACF7jABS6wgUU0/WD6dLlUWAgX6gtc4EIOOiJApxUNJnCBC1zgAhtYRNN/TZ0qF7ZuhQv1BS5woQcdEaBjjDHGGEfTbVOmyB/bt8MCY0wOOiJApxUNJnCBC1zgAhtYRNOPJk2S6h9/hAv1BS5woQcdEaCThwITuMAFLnCBDSyi6Y7sbDlfXAwX6gtc4EIOOiJApxUNJnCBC1zgAhtYRNOPMzLk3O7dcKG+wAUu9KAjAnSMMcYY42j66bhxUrlnDywwxuSgIwJ0WtFgAhe4wAUusIFFNP0sLU0q9u2DC/UFLnChBx0RoJOHAhO4wAUucIENLKLpFykpUnbgAFyoL3CBCznoiACdVjSYwAUucIELbGARTb+0fhf9fugQXKgvcIELPeiIAB1jjDHGOJp+lZwsZ48ehQXGmBx0RIBOKxpM4AIXuMAFNrCIpl+PHCmnjx+HC/UFLnChBx0RoJOHAhO4wAUucIENLKLpN8OGyamff4YL9QUucCEHHRGg04oGE7jABS5wgQ0somlJSpLfSkvhQn2BC1zoQUcE6BhjjDHGfen6FSvkj23b3m1bgbkG6HDBGJODHkNqbGyUzZs3m8DVlyorKyU9PV3S0tKkqqrKo2zAgAHGH3zwgUydOlWampqCvl+g40WinB50DBO4wAUucIENLHz7eWqqPPj8c/NYh7brEHe4UF/gAhd60GNI+fn5UlJSYgJtb9XV1UlmZqY0NzcbZ2Vlmee89eLFC9mxY4dMmDAh4HsFO15Py8lBxzCBC1zgAhfYwMK/O5OTpXPUKDMxnFoniYML9QUucCEHPQblK0DPy8uT8vJy93ZFRYV5zpc0SNee9EAKdryeltODjmECF7jABS6wgcWv7iHsvzryy8v375cXY8fKvdxcufHNN2Z5NV1mjTpCfYELXOhBj5MAfciQIdLe3u7ebmtrk6SkJJ/7a+A8efLkgO8R7Hg9LXeqpqbG/HOKioo8ns/IyDCtK/Y/UP+yzTbbbLPNNttsJ9r2/Z075bUrANfte/v2Sfvs2VJj/TZ6MX683LpwQTqtgB1ebLPNdqJtJ2yAPnDgQOns7HRv6+NBgwZ1eV1ra6sZbh4sBz3Y8XpaTg86hglc4AIXuMAGFu98Z/58M6Td3r61eLH8p6DAPH5s/W67sm6dvEhJoY5QX+ACF3rQE6kHXSeZ+/zzz+Xu3btB36Mve9DJQSf/BMMFLnCBS5yw6aNlvvpbPXmUnS2vR4z4e4K4sWPl6po15vHV776TtilT5FlaGucP9QUucCEHPZ5y0HXWdFtlZWWSk5Pj3tagfPr06fL48eOQ3iPY8XpaTg86hglc4AIXuMQPm6o9e+SvqVPlhXVv/u2XX6gnEbQ9AdzboUPdbJ9bwXj1zp3msc7grr3rfRmgc/7ABS5woQe9hwF6bW2tZGdnmyHsOnw9NTXVY2mzadOmybVr10J+j2DH62k566BjjDHG8eEra9eaAFIDdO3J7bDu73CJnC9t3ix/ffaZvBozRs4ePvwuQE9JkfIDB9yvaZk7t08nicMY475yXAfo9lrmTjulM6WnWBf0wYMHS3FxcVj7+gr6Ax0vEuX0oGOYwAUucIFL7LIxvebTppkc6HMlJeY5nV28L5f76g/15M6CBdKwfLk8HT/eMNfndHm1M8eOuV9TvWOH+T9w/lBf4AIXetD7gaqrq4Pmh/eFyEEnNwfDBS5wgUuMsCktNUOub375pfzmzDu3Hr8ZPlxOnTxJPemBtTf80qZN5vHTcePk3K5d0j55svxhBeL6nHO4O+cP9QUucCEHvZ8F6DoUvaGhgQCdVjSYwAUucIELbDwCdF9B4rP0dKn86SfqSXftYnt17VopO3jw3dB167n7s2ZJ3caNcurECdMIwvlDfYELXOhB76cBeqyIHHSMMcY4Nnz2yBG/Oc+ai35hyxY4ddMV+/aZIew3lywxQfq93FzzfLP1I/Xa6tXy++HD8nL0aFhhjMlBJ0AnQKcVDSZwgQtc4AKbd/nnT8aP91nW/MUXcs21BBj1JHzXbtxoJtq7P2OG3J09272cmgbsfxYUSMXevWaUAucP9QUucKEHnQCdAJ08FJjABS5wgQts5MK2bWZmcZ/7W0GkBpPez58vLpZGH89TT7ry095yXTZNe8rt2drrV66UOwsXyvldu6SjDyeE4/yBC1zgQg46ogcdwwQucIELXGKYzZV16+RuXp7vsrVrpXX27C7P31y8WF7F2dDsaNSTezk5cvn77+XNsGEea5vbzP/Yvt0sZ8f5Q32BC1zoQSdAJ0DHGGOMsTR8/bXczs/3/cNqxw4z47j383dzc+XVqFHwC2Jd41yXrXtp/c7RYN1+/uKWLfJw6lSp3bTJDH+HFcaYHHQCdAJ0WtFgAhe4wAUusDHBuQbpvsp0SPaLsWO7PP9k3Dh5O2yYmZGceuLfb5OS5PSJE/IkM1Maly71SBHQoe1Xvv9eWv2MXuD84boCF7jQg44I0MlDgQlc4AIXuPQzNjrUWodc+yo79fPPIkOGyK+OJdhOHz8ur0eMMDO/6wzw1BM/1iXWrADdV5kuXaf8rq9aJU3z53P+UF/gAhdy0AnQCdBpRYMJXOACF7jA5lczQZxOFOevvHPkSDljBeX2tr5Wh70/zsgww7epJ75tGjIsdr7Kzhw7ZoJ3ncld8/k5f6gvcIELPegE6AToGGOMMTZLrFXt3h0wj9qefdz0hlhB5S0rqHw4fbpcKiyEoR+bNc7HjPFbrnnp2nv+57Jl8MIYk4NOgE6ATisaTOACF7jABTa/Bh2q/jgrS87t2uXe1sBcJzcza6SvXk098eOK/fvlWWqq3/L2iRPl/syZZpg75w/1BS5woQedAJ0AnTwUmMAFLnCBSz9nc+rkyXc55gEme9NlwP6wh8Bbr+u0AvrfrYD+P0uXxtVa6H1dT6pKSuRJRobfcl2+ToN0nSiO84f6Ahe4kINOgE6ATisaTOACF7jApZ+z0Z5zf3nStu/PmiV1Gzeax5V798pzV6/wtTVrpGXuXOqJH1cXFcmjCRP8lmvjxtNx48xoBM4f6gtc4EIPOgE6ATrGGGPcz22WUUtJCfiaFh3KbgXj+vjq2rVyd/Zs8/hiYaFZyxuOvq2T6ekEfP7Kr373nclR/2P7dnhhjMlBJ0AnQKcVDSZwgQtc4NLf2VTt2WMmiQv0mluLFknDihXmcfO8eXLjm2/M45odO+Tl6NHUEz++tGmTPJgxw//nsQJzXa7uvCO/n/OH6wpc4EIPOgE6ATp5KDCBC1zgApd+yqZ6586Aw7DVOsv4zS+/NI+fZGbK+eJi87js4EF5PXw49cSPL69fL3dzcwOOXng7dKhU7N3L+UN9gQtcyEEnQCdApxUNJnCBC1zg0t/ZXNi6NeAwbPX11aulyfpRZdb1HjFCfvvlF3fZGytAP3XiBPXEhzUtQGe691f+W2mpmaBPGzo4f6gvcIELPegE6AToGGOMcT937caNZqmvUHqCNae6fdIkj7JnaWlR7wGOVd9YuVLuLFwY8DXK/vSxY/DCGJODToBOgE4rGkzgAhe4wKW/s7mydq205uUFfM3FrVvNZHB/FhTIrfx8jzKPJdioJ35TA+DCdQUucIENAToBOnkoMIELXOACF9gEHr6+apU0zZ8f8DXnd+6UjgkT5MH06abH3Vmma3lrkE896WoNzrVRAy5cV+ACF9gkQIDe2NgomzdvNoGrL1VWVkp6erqkpaVJVVVV2OWRPl6470cPOi2LGC5wgQtcos+mYflyubV4ccDX6BD2Z9Y9/lVysvx+6JBnEGrtqz3F1JOu1uHt9StXwoXrClzgAptECNDz8/OlpKREBgwY0KWsrq5OMjMzpbm52TgrK8s8F2p5pI8X7vuRg44xxhjHhm8uWSL/CdLLe/boUekcNUqe+1gvPZQe+P7qZsf68RhjjBNkiLuvAD0vL0/Ky8vd2xUVFea5UMsjfbxw348edFrPMFzgApf+wkVn6q4qKYlZNtrLeyNIL6/O2v42KcnnkmG1Qdb67s/15O7s2XJ53Tq4cF2BC1xgk+gB+pAhQ6S9vd293dbWJknWjTPU8kgfL9z3640A/fTJk1K+b5/b1Tt2SHVR0Tv/+KPU6PbOnX+XW8+5bb2mRv86ytWac2f2VbuO1WV/1/Htx3Z5s/WDrMb5Hl7l7s+mn8s+vvXX+f7niovdn927/Lz9vq7Pbt7Lep2/z+6z3Ouz1/jY3/maLuUBPruv/VusH3g+97c/6/btAb97zbZtHuX29zL/W32dc/9duzy+uzmO4/Nrufd3d5ZXabnXd9fjVzjL9TnHZ9dtHQZqyq0f496fXf/a5erKPXvMa5SL2d9Rbvb3+uym/Kef3pXv3u352XV/63Glq7zSLnd89hqvcu/Pro/1M9mfT9/L+dlNubWf87Pb3O3PUuXa35R7fXa1s9z7s5v9XcfX925WLq73cJdbXEy5xcn7s5vj2+U+Pru+7pyz3PnZXXVF64V9fO/PXu0ot+387Poad7nub382Z7lVbz329Tr+eWe512evcZXfPnfu7/0dn10/q8f+zmO7fN5xjfP+7Ob9nddIr89uth3H935vX9dIj2ug63hdroF2PfZ1jXNws//n/q6Bes31VR7oGuj92X1eAx2f3Vnuvg7Y10Cva5B9nSk7cEAalywxS5HFal5ky9y5cjWEXl5dr7thxYqu+enW/+dxVhY5oj58f8YM04ABF3KK4QIX2CR4gD5w4EDp7Ox0b+vjQYMGhVwe6eOF8341NTXmn1Nk/aBxKiMjw/zz7BYW/RvOdoP1w77TOsbLcePkeVqavLV+DL0dOVLejBolr11/3yQnu8vfjBjxrswud73GLte/+nots8vfOsrd+9vl1mN9P3e5vt7r/T3Krf31ePp8p+sYznLz/mPG/L2/llv7uPd37ev+/Ho8f+X6WbXcYmKXv7bL9b21XI/vLLe++xvHa7rs7yx3Hd/J31e5z/3t99fyYcMC7+9drnbtb/7fWp6e/q589Oi/y7XMu1zZBil/q+XWsV+73tt8fqtMyzu13Gbj2tfw9yp/41X+ylWu3+O1XU+c5amp7/YfO9bN542W+StXNna59dou5fax7fKUlC77649u8xmsbbtcP1+nxcIuf+OqI3a5/jV12nV8+3x4ZR3XlFuf462P8k5nueu7v3GV63aXci2zy3X/f//7Xbme467/iyl3nZ9dyvWz63u4zkH9v3TZ3y63/t92udYD92f3sb/hY9V3LbfPH1NXrXrnsb/l147ju8ut/e069drFxlf5G1f5a7vcdQ1zl7vqp32N0XKP/V3l5n/lXa6fy97XxU/fx13uOu/c1wd9f+9y17XLvp7otrvcdVx3uWvbXe56rtNx/dX38y43+1tcXus10VHe5frrqu8e5c7rp6u+eVzfva6vHuV6DfEut5g6//9aHzpdDO366lFu1cc3Wgdd+3f3/tbT7fr6+oDlbVaAfm/fvqDH0+93yQo2vctrz5yR19Z3jdb3C2fbdl+9n64v37B3b8zysLdbWlri4v+X6PUlXrapL9SXcLf1PmRv04Pej3rQMcYY41jz6ePHTQ+7NnTo41j8jA+nT5eLhYVBX6fD4H2u111aqj8E5NTJk/zPvfxo4kQz+gIWGGPcD3LQddZ0W2VlZZKTkxNyeaSPF+77kYNO/gmGC1zg0p+46AzolXv3xiSb9kmTzPD9nrzHq9Gj5fcjR6gnXn6SmRnV+Qe4rsAFLnAhB72PAvTa2lrJzs6W1tZWaWpqktTUVI+lzYKVR/p44b4f66CTf4LhAhe49CcubVOmyB/bt8ckm8dWEHmuh0Hk03HjzBwX1BOvhhnr91DF/v1w4boCF7jAJhECdA3Mve2UzpSekpIigwcPluLi4i77Byr3FfT35HihlNODjmECF7jApb9y0dnPL3//fUyyiUQQ2T5xopmIj3ri6ZdjxnRZNx4uXFfgAhd60OO8Bz3Sqq6uDpof3hciBx1jjHF/8e38fGn4+uuY/GwvIzA8XZdZu9SLs5WfjtP8dp1AMFbnHsAYY3LQY0Q6FL2hoYEAnVY0mMAFLnCBSx+53grOby9cGJNsdHb8UydO9Og9WubMkavffdfleV1qTlda6PaxS0ulbsMGsyJDJHK5+7qe6Nrxv1nfgfOH6wpc4AIbAvSYFzno5OZguMAFLv2Fy+X16+Vebm7ssbGCRw0ie2uEQOWePWaG9+4et/zgQfP52idPlivr1sVVPTn18889a5zg/IELXOBCDjoBOgE6rWgwgQtc4AKXXvh8O3aYIDPW2Ojwax2G3dP3+HPZMrn55Zddnq/avdv0fp/xtTxbCNb87ZfW74Pr335reunjqZ6cPXw4Imw5f+ACF7jQg44I0DHGGOMIumLvXnmWlhZzn6vMCoBfROD+e231amm2fnR5P1+9c6fpRe7uEnMV+/YZbsrvxdixcfU/15nxX48YQf3HGGNy0AnQaUWDCVzgAhe4xJLPHjkStXzkQGw0gNaZxnv6HnU//CD3Zs4iaFkAAC1BSURBVM3q+t7bt0vnqFHdXmf93O7dZi1xfazH+WPbtripJ+eLi6UjK4vzh+sKXOACG3rQCdDJQ4EJXOACF7jEmp+MHy+XI5BHHUk2tRs3mmXWevzjywqcda137+cvFhaaBgCd6K07x60uKpJHEyeax3r8+m++iZt6cnHLFnk4dSrnD9cVuMAFNuSgE6DTigYTuMAFLnCJNWvA9jgjI6bYXFm7Vlpnz+75cG5HT7d3z7oOUb++alW3jnvBCvz/+uwz8/j66tXS8sUXcVNPdFK7u3l5nD9cV+ACF9jQg06AjjHGGMeiNUDXXuWY6dUoKJCbS5b0PJf94EGfOeJXvv9eHluBe2M330PXVtc11u2c7qfjxsXN/7p+5Uq5E6Wl9TDGmBx0RIBOKxpM4AIXuMAliHW5tTZfs7mXlsq9nBzzty/ZtMydK9fWrOnxe+g66m+GD+/yvE4ep7PXN8+b121ed13L02n+vs6K3t0Z4fu6nmjDhzaAcP5wXYELXGBDDzoBOnkoMIELXOAClxi0BpnPU1PN5Gy6XfHTTyZA1ud1vXCdTK4v2bR99pkZRh6J99HZ2nXtb+9e5Aeffy73Xb3g4fqqxcY5rF2Hu/dkBEJf1hNtlNDl4Th/uK7ABS6wIQedAJ1WNJjABS5wgUuMWvOx7YD1zvz5ZhK1M8ePm/XCuzvbeXfZ6ARxFd1cAs3br0aPNkPdnc/p+uiteXnuid7C9Y1vvpE7Cxa4t3WofGMPhuT3ZT25N3OmycHn/OG6Ahe4wIYedAJ0jDHGOEZ96uRJeZWcLDU//ihPx4+Xp+npJrB9PXx4RIabh+zSUp+93t21NjR4D2XXYd6ah93dmeL/XL5cbi5e7N7WifbaXJPGxbo1lUGXmaPOY4wxOegE6LSiwQQucIELXGLY92fOlJfWfU8DdZ34rPKnn0wP9O1Fi/qMjTYKvIzgvffM0aPmuzhnbNfvo73or0eM6NYxG73yuDX/XPPQu7uefF/WE11Wr2rPHs4fritwgQts6EEnQCcPBSZwgQtc4BLr1knhOrKz5YV1/ztfXCzP0tPl4fTpfcZGc7kfZ2VF9L3KDxww36d20yaz3WT9ELthBeyaX69l4R5PA/z6r7/2eE5HHeiM7rFeT16OHi2/98KcApw/cIELXMhBRwTotKLBBC5wgQtcesGnjx83PcJ/bNtm8rSfp6X1GZvGpUvN8PpIv9+5Xbukc9QouaprrM+ZY9Za14nedLh6uMfyNdFas3WsUNZV16H7Ffv2vcv7t46h231WT0pLzZwCv/3yC+cP1xW4wAU29KAToGOMMcZxYQ3kkpKkduNGM9t5JHPCg/nqd9+ZZdZ649i3Fy40owN0lEDdhg1mhMCLlBT5LczvphPM6Vrq3p/7rvV8sH11P7P0m824Dyds06H42khBHccYY3LQCdBpRYMJXOACF7jEkTU/W5cT06BTc7gjnbfsj433DOmR9KVNm0yDg/rS5s3u76nBeli5+rNmSd3GjR7Pab7+8xAmndMefG3wOH3ypLy1AnVdj72v6on23D/rhdEQnD9wgQtc6EFHBOjkocAELnCBC1x6M1fZuv81LF9u8rXv98LSXP7Y6ORtN7/8sle+k85Q3z5xoplx/Q/XOut3Z8/u0hvutObgX/Ja4/zhtGlm5nbvUQedycny++HDAT/DtVWrzDBzbfDQYFkbCB4UF/fJ/1QbIl6MHcv5w3UFLnDB5KAToNOKBhO4wAUucIknax74rfx8Yw2YnbOW9yYbfS8N0nvjO2kvt44G0Lz66qKidz/QvvrKLLvmdyTByJGGgfM57fX2tTb8g+nTTVpAoM/QaL2fDjO/vG6dWfKs2fpR2NxHy9hpfv8D11r3nD9cV+ACF9jQg06AjjHGGMeJNVe7ee5cE8DqhGqh5FdHwjq8XYe598axdfbyV2PGyOOMDDm3e/e7XuX16+XerFl+93k7bJg8mjDBk01WlpzftavLazWQv5uTE/Az6ARzOumeLtV2NzfXBPS9MUu+L2vDwkWv0QAYY4wJ0AnQaUWDCVzgAhe4xLh1GLgGrvUrV0r1zp0mYO8LNjpBnE641hvfSWcv1+HlOmy9Yu9e85wG6ro2uK/X6xrq2tutdi5NZq8R7/16XXrtiRX8B/oM2oP9aNIkaZ09W24tWiRnreO+Tk42Q+R78/9pr9XeV5P9cV2BC1zgQg96jOj06dMyduxYGW/d7M6ePetR1tzcLHPmzJH/9//+n/zzn/+UCRMmSG1tbcDjVVZWSrp1I01LS5OqqqqIl5ODjmECF7jABS5dfd8KJP+aOlWurVkjZ1zLrvUFG3uG9d76XprzrWuilx08aLZPnTjhnlXd+7VVruD9Xm6umTDPfl5nfi/zsX76hW3b5K/PPgs8MsH67aM959qbrY0f+tyr7Oxur6Eeqi+vX28mx+P84boCF7jgfpSDfurUKSkoKJCOjg4TjE+ZMkUqKirc5Roo79+/X549eyZPnz6V3daN79NPP/V7vLq6OsnMzDTHUmdlZZnnIlVODzqGCVzgAhe4+LYuJaZDu3UIuG6/HDNGyg4d6nU2Oty7N4dhP7eCa+0RP3v0qEfAXW79PvF+7cWtW00jhXdw+yo52WN/2zrSwHs4vLf1vTTPXnvx7YaIB/n5vTas37amKFzzWrud84frClzgApsED9A1ANfg3FZjY6NMnjzZvT148GCpr693b9+8edP0bPtTnnUzKS8vd29rsK/PRaqcHHSMMcbYt5vmzze9x/YM5m1Tppge4r7Ik/Y1AVuk/Dgr69267idPup/TILzLrOy/vlvbvHXOnL+Hh584YZ7XHnfn/u4e9z17/A6Xt63vrbPGa5Bf45qo7rK1HSgPvsdD+0tLzftFsoEFY4zJQY8DpaSkyMOHD93bT548kU8++cSjhz0pKUmqq6vl2rVrMsu6Gd2+fdvv8YYMGSLt7e3u7ba2NrN/pMrpQccwgQtc4AIX3765eLFZ19sOlnVys+urVvU6Gw2gz/mYgC1S1iHo1g8EjyHtOjGdPdzc6T8LCtxLvul+tZs2mf2sHxM+j6298IHWQrdz2pWpBurlrmHyl63fRy9Hj+6176wNAY8zMzl/uK7ABS64v/Wga473okWLTFCsgfpi6+Y+cOBAd/mDBw9ML/uSJUskNzfXDId//vy53+Ppvp2dne5tfTxo0KCIlTtVU1Nj/jlF1k3MqYyMDJOfYP8D9W88b7e0tCTU94nEtjbcwKPrtvdf+FBfqC/9q740f/utvE5JkWuuodxN69bJo6+/7vX70Yvx480Ebr31/drnzDEzszvLteHhgfWbxfv1HStWyB3X+u931q+Xx0uXykXrt87r4cN9Hv/WpUvSOWaM5/exfkfoWvKXKivlTlmZPLcC5XLr+2mQ31hf737dy7Q0uWMduzf+n9rY0rpyJecP11uut9QX+Di29T5kbyf8JHHJycmmp7qkpESGWzcxWzk5OXLmzBn39uXLlwPCoAedVjSYwAUucIFLdKw50Z0jR0rFvn3v8rG3bDFDwXubzUvrvvt7Lw7FbsnLMxPFeU/upkP4fU2UZ69rrr3dukTb2cOH5ZWf3m4z4ZzXsZ+NG2dmbtd9WubMMUP4NX/9peP3hbLQWd2v9dJ66DrsXvPjOX+4rsAFLrif9aB76/jx47JixQr39gcffCCvX7/26NH+17/+FTAHXWdht1VWVmaC/EiVk4OOMcYY+7aufa7DsH+3AlI7QH0xdmyvv68Gz6et3w+9dfw78+ebhgfnczqj+0sf93udcd0Z2D7JzJTaH34wE835PL49/N0xfF6Del2iTRs6dOk6XaLN174anGu+e6S/r92w0NvLuGGMMTnoMSydpV170sdYN4T79++7n9dZ1M+dO2cC81evXkmpdbOYNm2a3+PoEmzZ2dnS2toqTU1Nkpqa6rFUWk/L6UHHMIELXOACF9/WQFTXDD9tT4Zm3bO1d1i3ddKx064J0yLKxkeA2xu59ZoH7v2+Omzdu2FAA/lK1wgCM+zf+gGnQbvOwO63gUGP42KjnJShx/fx8d2Uha6r7jfw76a1V/72woW9Evhz/sAFLnChBz0ONGDAAOMPP/zQ5Jh7TwCnS51pD/ZHH30kH3/8scybN89jUjnd11s687pOPqczwBcXF0e8nHXQMUzgAhe4wKWrddi3mUzNOYFbRoaZwK3h66+7BrkRYKPBv/cQ8Yj/T776yudn18npzlu/E+zt3375xQTXztnaL2zZYpZRexVgQjed7E17zPWxDtV/6cpJD4WF9nTb67NHwvo5dRTEzSVLOH+4rsAFLjgAm34zxD0c6czuwfLD+0L0oNOyiOECF7jAxQpGt2+Xt16zld/LyTFrgttB7qmffzazllc4epnNsOr9+33Oih6MjQa2r0IIaHviP5cv9xmg383NNcudOYeGd2dI//O0NKlwramujRmhzJ5us7g/a5ZcXrcuYt9VP3+59b859csvnD9cV+ACF0wOenjSoegNDQ0E6BhjjHEs/HDRAF2HZzuea1y61Pj2okVmKLcG6PoaDQR1DXB3EFxQ0K0edg1sNcDtze+lAbCvoLnxq6+k0dHT/MeOHWZCt3CPr3nqVSUl5vHFwkJ5OG1ayPveys+Xtm68Z6Ae9LOu3nyMMcb9OAc9nkUPOi2LGC5wgQtcfjVLgena3x7B7fr1phf9bl6e6V3XgPpFSorUWc9rz7cG9aanfdYs6UxODpvNud27TYAbje9rfzd7WyfJ0xzucI/zaOJEqS4qMo+vfvedmbk91Hpy1XrPjuzsiH2n1yNH9uqEe5w/cIELXOhBRwTo5KHABC5wgQtcomQdsq0BdLsVhGoPufYQ66Rpdo/7a+s57QXWvOs3w4eHzabGCmwfTZoU1e/m/nwFBd3K3dZGjQtbt74bSbBsmdz88suQ64nOGP/IxTMS1tENv8Xp8HauK3CBC1zIQUf0oNN6huECF7jAJYDtSdxeWPdGnclc10rXtcLt8roffjC9tjrkXQN05wRrobC5aAW2f4UxJLw3vps9y3rLF190a11ys3b6pk3m8Z0FCwyjUOtJ5d69AWeID8dmBnmvOQQ4f7iuwAUumB50AnSMMcY4gazBuc4M3jZlilnCq3nevC653H9NnWqC9LIDB8I6dt3GjWaitKh9N51UzfWZTU/4li1hH0OH/19xTfSmQ+brNmwIed+zhw8HnCE+3AaH10FGMWCMMSZAJ0CnFQ0mcIELXOASx9bh7TqE/V5urgmmNSD39TpdtkyHjYfDxuR9R3HNbg3KL7qGp2tPtvZoh3sMbbC4tnr1O1aTJ0vNjh0h1xN7abdIfJezR4/KqyDzAHD+wAUucIENAToBOnkoMIELXOAClzj2/ZkzTfB6Z/58aZ80Sa5/+63vYHfaNHewGyqb66tWSZN13Gh9N31vMyS9tNSMEtBZ6sM9hs5wX//11+ax5ulrXnk49UR7vU+fONHj76LrqXdnmTjOH7jABS7koBOgE6DTigYTuMAFLnCJE9+dPVuejhtnJlF7agXqdr51oKHeobLR4N975vi+tDY66CR1Vbt3d7snW5dq03XiNf/+rRXkhxJsO1mY1AAruO7pd9G16Z/18pJ1nD9wgQtc6EFHBOgYY4xxFK29ymeOHzfDuDVf2l8P8Z2FC6V+5cqwjq3Ls9WHMKlab1knudOZ3C9t3hzW+uVONyxfLrcWL5bzFhcd5h/u/k8yMkwDQU+/i65L/2T8eOosxhiTg06ATisaTOACF7jgROdSu3Gjmam93M9EcH/qMmUBlhjzxUYDynMlJVH7TtrbrUPMtQf8phVkd+cY9jB9HfqvM8GHW09CzVsP5vPFxRFdU53zBy5wgQs96AToBOjkocAELnCBC1xi1DU//iiSlOQ3T/uaFaA2BwhQvdnocXSZs2iv263D93WYfd369d3a357oTr+7Buvh1hPnMm09+v9YQb4G+5w/XFfgAhc4kINOgE4rGkzgAhe44ATnoj20gdbZrv3hB5NTHiobPV53hoRH2q2zZ5uh+92Zwd0eJq9D9bX3urqoKOx60mIF91etIL+n3+OCrik/dSrnD9cVuMAFDvSgE6BjjDHGiW5ds1snggvYgztpUsjH0x73lrlzo/69NIfcTBBXWtqt/S8WFpoeeD3G6WPHwt7/djdy93350qZN8mDGDOoqxhiTg06ATisaTOACF7jg/s6l8qefzHJsobJpsn4c3YjiBHHuhoI1a6Rz5MgeDS1/PnZsWL3XThY6O77OBN/T71G3YYPcy8nh/OG6Ahe4wIEedAJ08lBgAhe4wAX3dy4Ve/cGHALvzebRhAkmrz3qn720tEd58H9s3Wp6z/1NnheMhTZS3HGsBd+4dKmc78bEeXYuPOcP1xW4wAUO5KAToNOKBhO4wAUuGC7ywrqH+gtUnWx+s4JinT399PHjcf+dy/fvDzh7fTAWuna8M3Wgc9QoaVixIvyRAKtXS7P1g5Pzh+sKXOACB3rQCdAxxhhjLA+nTze50CENh09Lg5nmjhcWGm72tjZc6JJ14R7nxsqVZi16mGKMMTnoBOi0osEELnCBC4aL6UnWNcWDsbm8bl3c50tHqp78sW2bPMnIeDey4OefTZrArUWLwj6mTnZ3q5truXP+wAUucKEHnQCdAJ08FJjABS5wgUuCWScquz9rVlA2t/Lz5c9ly6gnv75bHk2HtZs8/v37zZJvoTZe6FryZYcOvctd/+orY84fritwgQscyEEnQKcVDSZwgQtcMFzMWuLPUlODstFlyS5u2UI9sVx24IC8SEl59/z27fI4M1MeTZwY2o/NZcvkVXLyu0aPxYtNLzrnD9cVuMAFDvSgE6BjjDHG2MyIrkO0f3f16vqzBpW/Hz4ML8tnjx6VTleQrTOx358500y2F8q+GpTbS8Rp/vmNCKynjjHG5KDHeYB++vRpGTt2rIwfP17Onj3bpby2tlYmTZok77//vgwYMMA4kCorKyU9PV3S0tKkqqoq4uX0oGOYwAUucIFL7/l5aqqZBM4fm7KDB+XlmDHUE8cw9TfDhr3rES8okJtW0P3W2g5l6TddVk2XeNPXNs+bZ2Zy5/zhugIXuMChH/egnzp1Sgqsm0lHR4c0NzfLlClTpKKiwl3e0NAgY6ybcE1Njbx8+TLo8erq6iQzM9McS52VlWWei1Q5OegYJnCBC1zg0rtus34L6MRn/thc2rzZY9Zy6smvfwfZX3xhgmwd8h7KuuqPrd852tihS71psK498Jw/XFfgAhc49OMcdO2p1uDcVmNjo0yePNm9PX/+fJ+96v6Ul5cn5eXl7m0N9vW5SJXTg45hAhe4wAUuvWtd01vX9vbHRmd5D3fd8ESvJzpJ3Jljx+ThtGlysbBQ2idNkpodO4KmE+iSbO3W7y5tENGJ5XSSPs4fritwgQsc+nEPekpKijx8+NC9/eTJE/nkk0/c259++qns3r1bhg4dKqOsm0+pdTMJpCFDhkh7e7t7u62tTZKSkiJW7pT26us/p6ioyOP5jIwM07pi/wP1L9tss80222yzHdr23WXLzGRl/srbZ882gSS8/t7WnPO6U6fk9dixcuPgQbmbmys3raA70P6Xrd9UnePGycOFC+XqmjXyyOJ699AheLLNNttsh7CdsAG65ngvWrTIBMUaqC9evFgGDhzoLtfHWv7gwQPzmgULFsiRI0f8Hk9f39nZ6d7Wx4MGDYpYOT3oGCZwgQtc4NK7rl+5Uu5Y93t/bN4MHy7ndu2inji2n6anmxnwtUdch7br5G/BlqGr3bRJHkyf/i5vfckS+WvqVLNkG+cP1xW4wAUO/bgH3Z4kLjk52fRUl5SUyHDr5mJL88+fPn3q3tYg3lkezR50ctDJP8FwgQtc4BJ5a+/4PR9rodts3gwdKqdOnqSeOLY7srLk/K5d7lz066tWSdO8eQGPoQG8BvKaTnB39mwz1D3osHjOH7jABS6wSfwA3anjx4/LihUr3Ns6gVxra6t7+9GjR2ZG90A56DoLu62ysjLJycmJWDk96BgmcIELXODSu64pKvK5jreyOX3ihOklpp541hMNri9u3SpvRoxwT6T3IMhEehqUX/n+e7N2+pPMTOnIzpbzxcWcP1xX4AIXOPT3HnTVs2fPTE+69pjfv3/f/fz169fNl9ch7jqZnAbs1dXVfo+jS7JlWzcYDeqbmpokNTXVY6m0npazDjrGGGPcu9Yh2s9TUvyWvfBT1p+ts9prsP3S9Rvk3O7d8iQjI+A+GpRrqkDVnj2m5/3J+PHmMTwxxji4EzZAt9c1//DDDyU3N1du377tMwDWwF2HmuuybN77e0tnXtfJ5wYPHizFxcURL6cHHcMELnCBC1x6z+51vUtLu7DRgFKXBqOeeNYTnYG9/uuv5akVZOu2zujeOXKk3/1/s9hqLr+dKvAsNdU0fFTs28f5w3UFLnCBAz3o3ZP2pAfLD+8LkYNObg6GC1zgApfI+lVyspw9cqQLmwtbtpjJzKgnnvWkZe5cs/Tco0mT3M+9HjFCTh8/7nN/nVBOg3J7W9dA16Xayg4e5PzhugIXuMCBHPTuSYeiNzQ0EKDTigYTuMAFLnBJMJvh17t3d2Fzef16s4QY9cSznuis983Wj8WHjrzzp+PG+R2yXvvDD3J/xgz39rXVq82ohbNHj3L+cF2BC1zgQA96fIscdIwxxjjCOdXTpsnFwkL3dnVRkfw1ZYrc+OYbuTN/Poy8rL3nOsxdJ35zM5w61YOhzu7u7gX66iuzj71dtXu3LmUjp/v57PgYY9zvc9AJ0GlFgwlc4AIXDBdvt3zxhVxbs8a9rcuG6QzljVZgqaaeeNYTXTLtr2nTpMnReNGsDL/9VsoOHDA966Jz+bgCcA3mL69b9/cxSkvfDYmP8wCd8wcucIELPeiIHHRyczBc4AIXuETYjUuWyH+WLnVv38rPl7fDhpm1vW9YwTr1xLOe3Fi50ixN5+wV/09Bgdy0OP6xY4e8HD3a5JjbQ9jNjO0lJZw/XFfgAge4kINOgE4rGkzgAhcMF7gEtuZEa061vd02ZYq8Sk01PcGah0498awnV9esMcuqNaxY4X7uytq10pqXJ7WbNsmDzz83M7xrTroOdTczuP/8M+cP1xW4wAEu9KAToGOMMcY4sC9t3myCSnv4tS4ZpnnpjzMzzUzuMPJ03fr18jwtzSMt4I/t26Vt8mSTHqAjD7SRQ5/TIP1pejrcMMaYHHQCdFrRYAIXuGC4wCW4zzvWO6/Yu1eep6TIvWXL5IV1j9W10Kknf3Rp0Hg5ZozUbdjgfq5i/34TtOuw9z8LCuRebq4ZfaCvuTdzJucP1xW4wAUu9KAToJOHAhO4wAXDBS7B/fvhwyZvWh/fXrBAnqemyoMffzQTmZUfOEA98aon2jOuOeYXtm51P6dD2HXpNB3mrsPddSk2nQVf8/sbHfn9nD9cV+CC4UIOOgE6rWgwgQtc4AIHuPh3aamZFE7zpTWo1Hz0+p9+krdJSSwF5qOenC8uNo0X+tf5vDZytE+aJBe2bTO96Nqbfn/WLI+eds4fritwwXChB50AHWOMMcYB/WLsWCk7eNDM5q49vjrUXZcKg01XV2rjxdChhpHz+Y7sbJMeoOWan67L12n+ueahww1jjMlBJ0CnFQ0mcIELhgtcQnLHhAlSvXOne2j2haoqaXLM7E49+XtbGzJ0dMHZI0c8ntfech3mfvr4cbm0aZOZBV9ncNeRCZw/XFfgAhe40INOgE4eCkzgAhcMF7iE5PszZ0rtDz/I3dmz5fL338MmQD05c+yYyJAhXQJvnb1dA3R9rI0duhSbroHO+cN1BcMFLuSgE6DTigYTuMAFwwUuIbtp/ny5sWqVPJw6VS5u2QKbAPXk9IkTJgfd+3W3Fi+WzuRk87h8/355ZT2+l5PD+cN1BcMFLvSgE6BjjDHGOHT/uWyZ3LQCTM2j9p78DIfmm0uXyivXbPinrCBe89T/U1AAG4wxJgedAJ1WNJjABS4YLnAJ3bo0WOucOWaJNe39hU349US5XSwsdG9rgK7pApw/1BcMF7jQg06ATh4KTOACFwwXuIRsXdP7r88+k9cjR5pJzmDT83qiAbouuQYX6guGC1zIQSdApxUNJnCBC4YLXEJ25Z498nTcOBNUwiYy9eTM8eNmjXm4UF8wXOBCDzoBOsYYY4xDDyaPHZPOESPkpSuHGmOMMSYHHdGDTisaTOACF7jAJUrWNbu1Fx021BO4wAUucKEHHRGgk4cCE7jABS5wiaJfjB1rZnGHDfUELnCBC1zIQe8jnT59WsZaN+Dx48fL2bNn/b7u0KFDMmDAgKDHq6yslPT0dElLS5OqqqqIl9ODjmECF7jABS5946fWb4P2SZNgQz2BC1zgAhd60PtCp06dkoKCAuno6JDm5maZMmWKVFRUdHnd9evXJTMzM2iAXldXZ16nx1JnZWWZ5yJVTg46xhhj3HfW3vM267cBLDDGGJOD3gfSnmoNzm01NjbK5MmTPV6j5RkZGdLa2ho0QM/Ly5Py8nL3tgb7+lykyulBxzCBC1zgApe+c7v1m6Dts89gQz2BC1zgAhd60PtCKSkp8vDhQ/f2kydP5JNPPnFvv3371gTIFy5cMNvBAvQhQ4ZIe3u7e7utrU2SkpIiVk4OOoYJXOACF7j0na+tXm0MG+oJXOACF7iQg94H0hzvRYsWmaBYA/XFixfLwIED3eWFhYVy4MAB93awAF337ezsdG/r40GDBkWs3KmamhrzzykqKvJ4Xnv79Z9nt7Do33jerq+vT6jvE4ntlpYWePjYtg0P6gv1hfrC/Yjzh/OH+kJ9ob4k2rbeh+zthJ8kLjk52fRUl5SUyPDhw91l7733ngnKvU0POsYYY4wxxhhjctB7UcePH5cVK1b4LQ8lB11nYbdVVlYmOTk5ESsnBx3DBC5wgQtcYAMLuMAFLnAhBz2hA/Rnz56ZnvQxY8bI/fv3ux2g19bWSnZ2tplQrqmpSVJTUz2WSutpOTnoGCZwgQtc4AIbWMAFLnCBCznoCRmg20PWP/zwQ8nNzZXbt28HfX2wgF1nXtfJ5wYPHizFxcURL6cHHcMELnCBC1xgAwu4wAUucKEHPeGHuIej6urqoPnhfSFy0DHGGGOMMcaYHPR+HaDrUPSGhgYCdFrRYAIXuMAFLrCBBYYLXOACF3rQETno5OZguMAFLnCBDSzgAhe4wIUcdAJ0AnRa0WACF7jABS6wgQWGC1zgAhd60BE56BhjjDHGGGNMDjoBOgE6rWgwgQtc4AIX2MACwwUucIELPeiIHHTyT+AAF7jABS6wgQVc4AIXuJCDToBOgE4rGkzgAhe4wAU2sMBwgQtc4EIPOiIHHWOMMcYYY4zJQSdAJ0CnFQ0mcIELXOACG1jAAS5wgQtc6EFH5KBjmMAFLnCBC2xgARe4wAUu5KAToBOg04oGE7jABS5wgQ0sYAEXuMAFLvSgI3LQMcYYY4wxxpgcdAJ0AnRa0WACF7jABS6wwbCAC1zgAhd60BE56BgmcIELXOACG1jABS5wgQtsCNAJ0GlFgwlc4AIXuMAGDrCAC1zgAhd60BE56BhjjDHGGGOMCdAJ0GlFgwlc4AIXuGDYwAIucIELXOhBR8Gl/yinFi5caP5hGGOMMcYYY4wTzwUFBQToCCGEEEIIIYRQvIkAHSGEEEIIIYQQIkBHCCGEEEIIIYQQAXqUVVNTk1CTIRQVFTEpBEzgAhe4wAU2sIALXOACF9g4JowjQEdRkfcs9QgmcIELXOACG1jABS5wgQtsCNBRFBRu6xBM4ILgAhe4wAYWcIELXOACGwJ0hBBCCCGEEEIopkSAjhBCCCGEEEIIEaAjhBBCCCGEEEKIAB0hhBBCCCGEECJAR72tAQMGeDjUfXr6fh988IFMnTpVmpqaurymsrJS0tPTJS0tTaqqqrqUNzY2yubNm+Xf//63z/eora2VSZMmyfvvvx/W93LqxYsXMnr0aHn+/Hmf8I91HpH6/ycqk76qL/HGp6yszLzHwIEDzd+zZ8/2ax7Oa+2//vUvmTBhgpw/f576koD3Iu/v09/vRZHikSj3oUjzSLT7UKT5JMK9KFJMEvE+FMn6Ek/3IgL0BA/Qo3Fj1BvJjh07zIXBqbq6OsnMzJTm5mbjrKws85xT+fn5UlJS4vNzNDQ0yJgxY8z68S9fvuz259Pjf/zxx1JcXNwn/4dY59GXP4zikUlf15d44FNfXy//+7//K+Xl5fL06VPz95NPPpEbN24E3Vd/NCRifbGP39nZKQ8ePJDdu3fLhx9+KNeuXev39SXR7kXeOnTokMydO7ff3osixSNR7kOR5pFo96FI8kmUe1GkmCTifSiS9SWe7kUE6P0wQH/16pUsXbrUXOy1Z+Thw4ce+2hr5KhRo8xJ/X//93/y+PHjblVGbTFyKi8vz1w8bVVUVJjnQv3s8+fP73HLqF60Ro4cKVeuXJHhw4ebbV/f/dNPP5UFCxZ4fHct1yUTUlNT5b/+678Sgkeg9/F+T++y6upqw+q///u/5ciRIwlTR8KpL/4+z5MnT2TatGmGzZYtW7rduxarfPS6cODAAY/n9u3bZ563pefOnDlzzHewP0tPextjub74Ov7WrVtl9uzZIV17ffFKFD6Jdi9ySoMCbcT466+/+u29KBI8Euk+FGkeiXYfiiSfRLkXRYpJIt6HIllf4uleRIDeDwP0DRs2mIv169evpaioSJYsWeKxj27fuXPHDKU6ceKELFu2LOz31go3efJkj+eGDBki7e3t7u22tjZJSkoK+bPrDxVtDRw6dKg5UUpLS8P+XNryVlBQYB7r34MHD/r87qqTJ096fHctz83NNUNx3r59mxA8evLDaObMmYaVtvb9z//8T8LUkXDqi7/Ps3btWjl27Ji5mezfv79bN7pY5jNs2DC5f/++x3P37t0zPx5tLV++XH788Udz44tEa3Ss1xdfx29paTHvEcq11x+vROCTaPcip7777js5fPhwv74XRYJHIt2HIs0j0e5DkeSTKPeiSDFJxPtQJOtLPN2LCNATPED31UqoFy47D0Iv3FpBAlVebaUPR62trWaYhneuheYHOVt+9fGgQYNCPol0/0WLFplhO1qhtVchnBZz/SGjLW86jESln09zuuwfON7v2dHR4XFR03JtAQtXscqjpz+MtHXe5hrujS4emIRbX7zPMWeuYKLx0fd0fg67BVrzlJ03HruO9PRHUTzUF1/H1+uFvkco115/vBKBT6Ldi2zdvn1bMjIywg6SE+1e1FMeiXYfiiSPRLwPRZJPotyLIsUkEe9Dkawv8XQvIkBP8ADdl+yhK7b/8Y9/BNzHmacTbKIGbdH//PPP5e7du12O09NWNL1J6YljS4egOFtJg0l7f/QHplO6bfcKeb/nmzdvgrKJZx49/WEUqCwRmIRbX5zb3jefROOjvRbaS+GUfl7nsZSBrxupL26JUF98HV9/UDg/T6BrbyBe8c4n0e5FtrQX29eER/3tXtRTHol2H4okj0S8D0WST6LciyLFJBHvQ5GsL/F0LyJA74cBura+ao+Iv300z8kpnV0wFGnlmz59ut/cDM2r0NkKbWlOR05OTsifXYd2aSuUrUePHplckVClMyT6OpH0efs9nSeQnig62Uh3W1tjnUcoP4zsC7W2qIb6wyhRmIRSX/zx0UlrnDe5ROOjOVg6ZNKpvXv3euT96Q8k7fkL5wYcz/XF1/E3btzoMVQu0LXXH69E4JNo9yKVzm4fbi9Kot6Lesoj0e5DkeSRiPehSPJJlHtRpJgk4n0okvUlnu5FBOj9MEDftWuXyTHxt4/mG+rsvXph1+UZtNUqFOlkJIFmitQTLDs72/zg02EeerL5aw3z9dmvX79uLrp6EukFRH886gQxoUhzP/x9D23Z0nJ9z1mzZsmtW7d8XuTD/VEUyzxCqTPasqd5fTq0TvP8IvHDKF6YhFJfAvHRz3D06FHT+6hLhziHlyUCHz2OBgw6oYm/mXM1/3HTpk1dZv/WSVhu3ryZcOeQ8/jaIq4TFemwZHtoarBrrz9eicAn0e5FKh36r//j3ri2xNu9qCc8EvE+FCkeiXofimR9SZR7UaSYJOJ9KJL1JZ7uRQTo/TBA1wpWWFgo48ePl/fee8/jdTrbpw6dSklJkY8++si05OgEE6G+X7A1BvUiqscePHiwz+VCgh3j119/NcMudQjIqVOnQmYxceLELss62Lpw4YIp1/fS12gLYnJysvuHaXd/FMUyD1/S3BedodJ549NWQp0cSn+UR+KHUbwwCaW+BOKj54yW6efQi632ZCRanTl9+rRpdfa39uyzZ89M67CWOz/Db7/9Jv/85z8Tjofz+Hrt1FlzdWhbqNdef7wSgU8i3ou8f/T293tRd3kk6n0oEjwS+T4UqfqSSPeiSDBJ1PtQpOpLPN2LCNAR6uaPnkSTDrPxXqMR9Vx6YXf2fiGEEPci7kPchxBC/kSAjhA/isx313VW/bXWo/CkrazKVIfa6ZqizjU1EUKIexH3Ie5DCCECdIQQQgghhBBCiAAdIYQQQgghhBBCBOgIIYQQQgghhBABOkIIIYQQQgghhAjQEUIIIYQQQgghAnSEEEIIIYQQQggRoCOEEEIIIYQQQgToCCGEEEoE6XrKwbxq1Sr3a+3HCCGEECJARwghhFAvB+wE4QghhBABOkIIIYQI0BFCCCECdIQQQgihQAG6rzJ9bsWKFcYff/yxfPLJJ7J06VK5dOmSzJgxQwYPHiwfffSRLFiwQF68eOGx77lz5yQ1NVWSkpLk0KFDwEcIIUSAjhBCCCHUkwB9yJAh8tNPP0lHR4fU1dWZ5zQo1+cePXokly9fNs8VFxe796uqqjKvuXnzphw9elTee+89uX37Nv8AhBBCBOgIIYQQQt0N0EN9bvXq1e7tcePGycyZM81j7VnX8l27dvEPQAghRICOEEIIIdTbAbrzOe09954tfu3atfwDEEIIEaAjhBBCCPVlgD5r1izJyckBOEIIIQJ0hBBCCKFoBuhXrlyR999/X86cOSOdnZ2ARwghRICOEEIIIRSNAF118eJFmTJlivzjH/9geTeEEEIE6AghhBBCCCGEECJARwghhBBCCCGECNARQgghhBBCCCFEgI4QQgghhBBCCBGgI4QQQgghhBBCiAAdIYQQQgghhBAiQEcIIYQQQgghhBABOkIIIYQQQgghRICOEEIIIYQQQgghAnSEEEIIIYQQQogAHSGEEEIIIYQQQgToCCGEEEIIIYQQATpCCCGEEEIIIYQI0BFCCCGEEEIIIQJ0hBBCCCGEEEIIEaAjhBBCCCGEEEIE6CBACCGEYli1tSLFxd2z7osQQgghAnSEEEIIEaAjhBBCiAAdIYQQQgghhBAiQEcIIYQQQgghhBABOkIIIYQQQgghRICOEEIIIYQQQgghAnSEEEIIIYQQQogAHSGEEEIIIYQQQt3T/we22z6SNiTndwAAAABJRU5ErkJggg==)
import scala.collection.JavaConversions._
var chart1 = new TimeSeriesChart()
chart1.add(account.getCashHistoryForAllDates(), "Cash");
chart1.add(account.getTotalValueHistory(), "Total Value");
chart1.add(account.getActualValueHistory(), "ActualValue");
for (id <- account.getStockIDs()) {
var data = account.getActualValueHistory(id);
chart1.add(data,id.toString())
}
//chart1.addLabels(account.getTransactions())
Displayers.display(chart1.displayCharts())
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Though at first sight it seems to be promising, the result is in fact quite disapointing: We only get 2 stocks which are used for trading and they are just bought and never sold. So it is just by luck that we were successfull!
Run the Selection – 2nd Attempt
On our 2nd trial we want to use the followig additional criteria:
– no penny stocks: rate > 5.0
– activly traded: number of trades >= 4
Therefore we define the following predicates which drive our selection
import ch.pschatzmann.stocks.strategy.selection.SelectionState;
import java.util.function.Predicate;
class NumberOfTradesPredicate( numberOfTrades: Double) extends Predicate[SelectionState] {
override def test(state:SelectionState):scala.Boolean = {
return state.result().getDouble(KPI.NumberOfTrades) > numberOfTrades;
};
}
class PriceLimitPredicate(account:Account, date:Date, limit: Double) extends Predicate[SelectionState] {
override def test(state:SelectionState):scala.Boolean = {
return account.getStockPrice(state.getStockID(), date)>limit;
};
}
import ch.pschatzmann.stocks.strategy.selection.SelectionState
import java.util.function.Predicate
defined class NumberOfTradesPredicate
defined class PriceLimitPredicate
// setup of the model
var periods = Context.getDateRanges("2016-01-01","2017-01-01");
var account = new Account("Simulation", "USD", 100000.00, periods.get(0).getStart(), new PerTradeFees(100.00))
var file = new java.io.File("NASDAQ-2017-01-01A.json")
if (!file.exists()) {
var universe = new MarketUniverse("NASDAQ")
var strategies = TradingStrategyFactory.list()
var predicate = new NumberOfTradesPredicate(4.0).and(new PriceLimitPredicate(account, periods.get(0).getStart(), 5.0));
var strategySelector = new StrategySelector(account, strategies, periods.get(0), KPI.AbsoluteReturn, predicate)
var stockSelector = new StockSelector(strategySelector, new Restartable("restart-v1A.ser",50))
// calculate the result
var result = stockSelector.getSelection(200, universe, new MarketArchiveHttpReader())
// save the result to a file
result.save(file)
}
// provide result even if we did not run the selection above
var resultA = new SelectionResult()
resultA.load(file)
var trader = new PaperTrader(account);
var allocationStrategy = new DistributedAllocationStrategy(trader);
var executor = new StrategyExecutor(trader, allocationStrategy);
executor.addStrategy(resultA.getStrategies(new MarketArchiveHttpReader()));
executor.run(periods.get(1));
account.getKPIValues()
[Absolute Return 0.0, Absolute Return Avarage per day NaN, Absolute Return StdDev NaN, Return % 0.0, Return % per year 0.0, Return % StdDev NaN, Sharp Ratio NaN, Max Draw Down % null, Max Draw Down Absolute null, Max Draw Down - Number of days null, Max Draw Down - High null, Max Draw Down - Low null, Max Draw Down - Period null, Number of Trades 0, Number of Buys 0, Number of Sells 0, Number of Cash Transfers 1, Number of Traded Stocks 0, Total Fees 0.0, Cash 100000.0, Total Value (at actual rates) including cash 100000.0, Total Value (at purchased rates) 100000.0, Realized Gains 0.0, Unrealized Gains 0.0]
Selection with Sharpe Ratio
In the long run we might be better off by using the Sharpe Ratio instead of the Absolute Return as selection criterium. So here is our next try:
// setup of the model
var periods = Context.getDateRanges("2016-01-01","2017-01-01");
var account = new Account("Simulation", "USD", 100000.00, periods.get(0).getStart(), new PerTradeFees(100.00))
var file = new java.io.File("NASDAQ-2017-01-01S.json")
if (!file.exists()) {
var universe = new MarketUniverse("NASDAQ")
var strategies = TradingStrategyFactory.list()
var strategySelector = new StrategySelector(account, strategies, periods.get(0), KPI.SharpeRatio)
var stockSelector = new StockSelector(strategySelector, new Restartable("restart-v1S.ser",50))
// calculate the result
var result = stockSelector.getSelection(200, universe, new MarketArchiveHttpReader())
// save the result to a file
result.save(file)
}
// provide result even if we did not run the selection above
var resultS = new SelectionResult()
resultS.load(file)
var trader = new PaperTrader(account);
var allocationStrategy = new DistributedAllocationStrategy(trader);
var executor = new StrategyExecutor(trader, allocationStrategy);
executor.addStrategy(resultS.getStrategies(new MarketArchiveHttpReader()));
executor.run(periods.get(1));
account.getKPIValues()
[Absolute Return -95743.38202537596, Absolute Return Avarage per day -187.73212161838424, Absolute Return StdDev 5843.812344481103, Return % -95.74338202537596, Return % per year -47.21591442347308, Return % StdDev 0.07739180282189885, Sharp Ratio -0.6909314558908972, Max Draw Down % 98.06741328837373, Max Draw Down Absolute 211245.8531461507, Max Draw Down - Number of days 251, Max Draw Down - High 215408.8152860403, Max Draw Down - Low 4162.962139889598, Max Draw Down - Period 20170111-20180104, Number of Trades 1, Number of Buys 1, Number of Sells 0, Number of Cash Transfers 1, Number of Traded Stocks 1, Total Fees 100.0, Cash 0.482232928276062, Total Value (at actual rates) including cash 4256.617974624038, Total Value (at purchased rates) 99900.0, Realized Gains 0.0, Unrealized Gains -95643.38202537596]
Electronic Monkey
We can compare the results above with an Monkey Picker which just pick 50 random stocks and strategies
var account = new Account("Simulation","USD", 1000000.00, periods.get(0).getStart(), new PerTradeFees(10.0));
var trader = new PaperTrader(account);
var allocationStrategy = new DistributedAllocationStrategy(trader);
var executor = new StrategyExecutor(trader, allocationStrategy);
var randomStrategies = TradingStrategyFactory.getRandomStrategies(new MarketUniverse("NASDAQ"),new MarketArchiveHttpReader(), 50)
executor.addStrategy(randomStrategies);
executor.run(periods.get(1));
println(account.getKPIValues())
Optimization
We can also evaluate if parameter optimization makes a difference. We optimize the parameters based
on the closing rates of the year 2015. Then we select the stocks that perfomred best in 2016.
Finally we evaluate the performance for 2017
// setup of the model in background
var periods = Context.getDateRanges("2015-01-01","2016-01-01","2017-01-01","2018-01-01")
var account = new Account("Simulation", "USD", 100000.00, periods.get(0).getStart(), new PerTradeFees(100.00))
var file = new java.io.File("NASDAQ-2017-01-01-optimized.json")
if (!file.exists()) {
// run selection
var universe = new MarketUniverse("NASDAQ")
var strategies = TradingStrategyFactory.list()
var optimizer = new BinarySearchOptimizer(new SimulatedFitness(account), KPI.AbsoluteReturn)
var strategySelector = new StrategySelectorOptimized(account, strategies, periods.get(0), periods.get(1), optimizer)
var stockSelector = new StockSelector(strategySelector, new Restartable("restart-v2.ser",20))
// calculate the result
var resultOptimized = stockSelector.getSelection(200, universe, new MarketArchiveHttpReader())
// save the result to a file
resultOptimized.save(file)
}
// Run evaluation
var resultOptimized = new SelectionResult()
resultOptimized.load(file)
var trader = new PaperTrader(account);
var allocationStrategy = new DistributedAllocationStrategy(trader);
var executor = new StrategyExecutor(trader, allocationStrategy);
executor.addStrategy(resultOptimized.getStrategies(new MarketArchiveHttpReader()));
executor.run(periods.get(2));
account.getKPIValues()
Displayers.display(account.getTransactions());
2 Comments
SannyOnerb · 10. December 2018 at 16:07
Make a more new posts please 🙂
___
Sanny
Johnny Zoulek · 28. August 2018 at 7:52
some genuinely great blog posts on this site, regards for contribution.