├── utils ├── __init__.py ├── dynamodb.py └── processing.py ├── research ├── IEEE2011.pdf ├── paper462.pdf ├── mongodb_connection_test.ipynb └── depth_compression.ipynb ├── config.py ├── scraper.py ├── requirements.txt ├── lambda_iterator.py ├── .gitignore ├── zappa_settings.json └── README.md /utils/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /research/IEEE2011.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/maxlamberti/orderbook-crypto-scraper/HEAD/research/IEEE2011.pdf -------------------------------------------------------------------------------- /research/paper462.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/maxlamberti/orderbook-crypto-scraper/HEAD/research/paper462.pdf -------------------------------------------------------------------------------- /config.py: -------------------------------------------------------------------------------- 1 | 2 | 3 | PAIRS = ['XXRPZUSD', 'XXBTZEUR', 'XXMRZUSD', 'XLTCZUSD', 'XETHZUSD', 'USDTZUSD'] 4 | DEPTHS = [25, 25, 25, 25, 25, 25] 5 | 6 | LOGGING = { 7 | 'disable_existing_loggers': False, 8 | 'version': 1, 9 | 'formatters': { 10 | 'simple': { 11 | 'format': '%(asctime)s - %(levelname)s - %(message)s' 12 | }, 13 | }, 14 | 'handlers': { 15 | 'console': { 16 | 'level': 'DEBUG', 17 | 'formatter': 'simple', 18 | 'class': 'logging.StreamHandler', 19 | } 20 | }, 21 | 'loggers': { 22 | 'scraper': { 23 | 'handlers': ['console'], 24 | 'level': 'INFO', 25 | }, 26 | 'iterator': { 27 | 'handlers': ['console'], 28 | 'level': 'WARNING', 29 | } 30 | }, 31 | } 32 | -------------------------------------------------------------------------------- /scraper.py: -------------------------------------------------------------------------------- 1 | import time 2 | import krakenex 3 | import logging.config 4 | 5 | from pymongo import MongoClient 6 | from utils.processing import query_order_book, mongo_reformat_orderbook 7 | from credentials import MONGO_CREDS 8 | from config import LOGGING, PAIRS, DEPTHS 9 | 10 | 11 | logging.config.dictConfig(LOGGING) 12 | logger = logging.getLogger('scraper') 13 | 14 | 15 | MONGO_IP = MONGO_CREDS['HOST'] 16 | DB_USER = MONGO_CREDS['USER'] 17 | DB_PASSWORD = MONGO_CREDS['PASSWORD'] 18 | 19 | 20 | kraken = krakenex.API() 21 | client = MongoClient(MONGO_IP, username=DB_USER, password=DB_PASSWORD) 22 | db = client.get_database('orderbooks') 23 | 24 | 25 | def scraper(event=None, context=None): 26 | """Target function for scheduled data scraping event. 27 | 28 | Parameters 29 | ---------- 30 | event : dict 31 | AWS Lambda uses this parameter to pass in event data to the handler. 32 | context : LambdaContext 33 | AWS Lambda uses this parameter to provide runtime information to your handler. 34 | 35 | """ 36 | 37 | start_time = time.time() 38 | 39 | for idx, pair in enumerate(PAIRS): 40 | 41 | ob = query_order_book(kraken, pair, DEPTHS[idx]) 42 | item = mongo_reformat_orderbook(ob, pair) 43 | if item: 44 | result = db[pair].insert_one(item) 45 | logger.info("Insert status: {}".format(result)) 46 | 47 | time.sleep(1) 48 | 49 | logger.info("Finished executing handler, took %0.1f seconds.", time.time() - start_time) 50 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | appnope==0.1.0 2 | argcomplete==1.9.3 3 | backcall==0.1.0 4 | bleach==3.1.0 5 | boto3==1.9.111 6 | botocore==1.12.111 7 | certifi==2018.11.29 8 | cfn-flip==1.1.0.post1 9 | chardet==3.0.4 10 | Click==7.0 11 | cycler==0.10.0 12 | decorator==4.3.2 13 | docutils==0.14 14 | durationpy==0.5 15 | entrypoints==0.3 16 | future==0.16.0 17 | hjson==3.0.1 18 | idna==2.8 19 | ipykernel==5.1.0 20 | ipython==7.3.0 21 | ipython-genutils==0.2.0 22 | jedi==0.13.3 23 | Jinja2==2.10 24 | jmespath==0.9.3 25 | jsonschema==2.6.0 26 | jupyter-client==5.2.4 27 | jupyter-core==4.4.0 28 | kappa==0.6.0 29 | kiwisolver==1.0.1 30 | krakenex==2.1.0 31 | lambda-packages==0.20.0 32 | MarkupSafe==1.1.1 33 | matplotlib==3.0.3 34 | mistune==0.8.4 35 | nbconvert==5.3.1 36 | nbformat==4.4.0 37 | notebook==5.7.4 38 | numpy==1.16.2 39 | pandas==0.24.1 40 | pandocfilters==1.4.2 41 | parso==0.3.4 42 | patsy==0.5.1 43 | pexpect==4.6.0 44 | pickleshare==0.7.5 45 | placebo==0.9.0 46 | prometheus-client==0.6.0 47 | prompt-toolkit==2.0.9 48 | ptyprocess==0.6.0 49 | Pygments==2.3.1 50 | pyparsing==2.3.1 51 | python-dateutil==2.6.1 52 | python-slugify==1.2.4 53 | pytz==2018.9 54 | PyYAML==3.13 55 | pyzmq==18.0.0 56 | requests==2.21.0 57 | s3transfer==0.2.0 58 | scipy==1.2.1 59 | seaborn==0.9.0 60 | Send2Trash==1.5.0 61 | six==1.12.0 62 | statsmodels==0.9.0 63 | terminado==0.8.1 64 | testpath==0.4.2 65 | toml==0.10.0 66 | tornado==5.1.1 67 | tqdm==4.19.1 68 | traitlets==4.3.2 69 | troposphere==2.4.5 70 | Unidecode==1.0.23 71 | urllib3==1.24.1 72 | wcwidth==0.1.7 73 | webencodings==0.5.1 74 | Werkzeug==0.14.1 75 | wsgi-request-logger==0.4.6 76 | zappa==0.47.1 77 | -------------------------------------------------------------------------------- /lambda_iterator.py: -------------------------------------------------------------------------------- 1 | import os 2 | import time 3 | import boto3 4 | import logging.config 5 | from config import LOGGING 6 | 7 | 8 | logging.config.dictConfig(LOGGING) 9 | logger = logging.getLogger('iterator') 10 | 11 | 12 | lambda_client = boto3.client('lambda') 13 | 14 | 15 | LAMBDA_NAME = os.environ.get('LAMBDA_TARGET') # name of lambda scraper 16 | TIMEOUT = int(os.environ.get('TIMEOUT')) # seconds 17 | N_SAMPLES = int(os.environ.get('N_SAMPLES')) # samples per timeout period 18 | 19 | 20 | def iterator(event=None, context=None): 21 | """Spaces out lambda invocations for scrape events. 22 | 23 | Cloudwatch event scheduling only allows sampling at minute frequency. 24 | This function is used to achieve sampling in sub-minute frequencies. 25 | 26 | Parameters 27 | ---------- 28 | event : dict 29 | AWS Lambda uses this parameter to pass in event data to the handler. 30 | context : LambdaContext 31 | AWS Lambda uses this parameter to provide runtime information to your handler. 32 | 33 | """ 34 | 35 | invoc_time = time.time() 36 | wait_time = float(TIMEOUT) / N_SAMPLES 37 | count = 0 38 | 39 | while (time.time() - invoc_time < TIMEOUT) and (count < N_SAMPLES): 40 | 41 | exec_start = time.time() 42 | count += 1 43 | 44 | try: 45 | _ = lambda_client.invoke( 46 | FunctionName=LAMBDA_NAME, 47 | InvocationType='Event' 48 | ) 49 | except Exception as e: # not sure which errors might be raised 50 | logger.error('Failed invoking lambda. Error: %s', e) 51 | 52 | exec_time = time.time() - exec_start 53 | logger.info("%sth lambda invocation took %s seconds.", count, exec_time) 54 | 55 | time.sleep(abs(wait_time - exec_time)) 56 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Custom 2 | credentials.py 3 | dynamo_connect_example.py 4 | *.p 5 | .idea/ 6 | .DS_STORE 7 | 8 | # Byte-compiled / optimized / DLL files 9 | __pycache__/ 10 | *.py[cod] 11 | *$py.class 12 | 13 | # C extensions 14 | *.so 15 | 16 | # Distribution / packaging 17 | .Python 18 | build/ 19 | develop-eggs/ 20 | dist/ 21 | downloads/ 22 | eggs/ 23 | .eggs/ 24 | lib/ 25 | lib64/ 26 | parts/ 27 | sdist/ 28 | var/ 29 | wheels/ 30 | *.egg-info/ 31 | .installed.cfg 32 | *.egg 33 | MANIFEST 34 | 35 | # PyInstaller 36 | # Usually these files are written by a python script from a template 37 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 38 | *.manifest 39 | *.spec 40 | 41 | # Installer logs 42 | pip-log.txt 43 | pip-delete-this-directory.txt 44 | 45 | # Unit test / coverage reports 46 | htmlcov/ 47 | .tox/ 48 | .coverage 49 | .coverage.* 50 | .cache 51 | nosetests.xml 52 | coverage.xml 53 | *.cover 54 | .hypothesis/ 55 | .pytest_cache/ 56 | 57 | # Translations 58 | *.mo 59 | *.pot 60 | 61 | # Django stuff: 62 | *.log 63 | local_settings.py 64 | db.sqlite3 65 | 66 | # Flask stuff: 67 | instance/ 68 | .webassets-cache 69 | 70 | # Scrapy stuff: 71 | .scrapy 72 | 73 | # Sphinx documentation 74 | docs/_build/ 75 | 76 | # PyBuilder 77 | target/ 78 | 79 | # Jupyter Notebook 80 | .ipynb_checkpoints 81 | 82 | # pyenv 83 | .python-version 84 | 85 | # celery beat schedule file 86 | celerybeat-schedule 87 | 88 | # SageMath parsed files 89 | *.sage.py 90 | 91 | # Environments 92 | .env 93 | .venv 94 | env/ 95 | venv/ 96 | ENV/ 97 | env.bak/ 98 | venv.bak/ 99 | 100 | # Spyder project settings 101 | .spyderproject 102 | .spyproject 103 | 104 | # Rope project settings 105 | .ropeproject 106 | 107 | # mkdocs documentation 108 | /site 109 | 110 | # mypy 111 | .mypy_cache/ 112 | -------------------------------------------------------------------------------- /zappa_settings.json: -------------------------------------------------------------------------------- 1 | { 2 | "ob_scrape_event": { 3 | "aws_region": "us-east-1", 4 | "profile_name": "default", 5 | "project_name": "orderbook-scraper", 6 | "runtime": "python3.6", 7 | "s3_bucket": "orderbook-scraper", 8 | "debug": false, 9 | "log_level": "INFO", 10 | "apigateway_enabled": false, 11 | "keep_warm": false, 12 | "timeout_seconds": 900, 13 | "memory_size": 128, 14 | "events": [{ 15 | "function": "scraper.scraper", 16 | "expression": "rate(1 minute)" 17 | }], 18 | "lambda_handler": "scraper.scraper", 19 | "aws_environment_variables": { 20 | "ENVIRONMENT": "PRODUCTION", 21 | "DB_REGION": "us-east-1", 22 | "TABLE": "kraken-orderbook" 23 | } 24 | }, 25 | "ob_mongo_scrape_event": { 26 | "aws_region": "us-east-1", 27 | "profile_name": "default", 28 | "project_name": "ob-mongo-scraper", 29 | "runtime": "python3.6", 30 | "s3_bucket": "orderbook-scraper", 31 | "debug": false, 32 | "log_level": "INFO", 33 | "apigateway_enabled": false, 34 | "keep_warm": false, 35 | "timeout_seconds": 900, 36 | "memory_size": 128, 37 | "lambda_handler": "scraper.scraper" 38 | }, 39 | "ob_mongo_iterator": { 40 | "aws_region": "us-east-1", 41 | "profile_name": "default", 42 | "project_name": "ob-mongo-iterator", 43 | "runtime": "python3.6", 44 | "s3_bucket": "orderbook-scraper", 45 | "debug": false, 46 | "log_level": "INFO", 47 | "apigateway_enabled": false, 48 | "keep_warm": false, 49 | "timeout_seconds": 900, 50 | "memory_size": 128, 51 | "events": [{ 52 | "function": "lambda_iterator.iterator", 53 | "expression": "rate(1 minute)" 54 | }], 55 | "lambda_handler": "lambda_iterator.iterator", 56 | "aws_environment_variables": { 57 | "LAMBDA_TARGET": "ob-mongo-scraper-ob-mongo-scrape-event", 58 | "N_SAMPLES": "6", 59 | "TIMEOUT": "60" 60 | } 61 | } 62 | } -------------------------------------------------------------------------------- /utils/dynamodb.py: -------------------------------------------------------------------------------- 1 | import boto3 2 | import logging 3 | from boto3.dynamodb.conditions import Key 4 | from botocore.exceptions import ClientError 5 | 6 | 7 | logger = logging.getLogger(__name__) 8 | 9 | 10 | class DynamoConnector: 11 | 12 | def __init__(self, region, table): 13 | self.region = region 14 | self.table_name = table 15 | dynamodb = boto3.resource('dynamodb', region_name=region) 16 | self.table = dynamodb.Table(table) 17 | 18 | def put_item(self, item): 19 | try: 20 | self.table.put_item(Item=item) 21 | except ClientError: 22 | logger.error("Failed putting item into dynamodb table %s.", self.table_name, exc_info=True) 23 | return item 24 | 25 | def get_item(self, search_key): 26 | item = {} 27 | try: 28 | response = self.table.get_item(Key=search_key) 29 | item = response.get('Item', {}) 30 | except ClientError: 31 | logger.error( 32 | "Failed getting item from dynamodb table %s and search key %s", 33 | self.table_name, search_key, exc_info=True 34 | ) 35 | return item 36 | 37 | def update_item(self, search_key, update_expression, attribute_values): 38 | try: 39 | self.table.update_item( 40 | Key=search_key, 41 | UpdateExpression=update_expression, 42 | ExpressionAttributeValues=attribute_values 43 | ) 44 | except ClientError: 45 | logger.error( 46 | "Failed updating item in dynamodb table %s and search key %s", 47 | self.table_name, search_key, exc_info=True 48 | ) 49 | 50 | 51 | class OrderBookTable(DynamoConnector): 52 | 53 | def write(self, ob): 54 | logger.info("Writing orderbook for pair=%s, timestamp=%s to dynamodb.", 55 | ob.get('pair'), ob.get('timestamp')) 56 | self.put_item(ob) 57 | 58 | def get_orderbook(self, pair, ti=None, tf=None): 59 | """Performs a scan operation on the database to return scraped order books. 60 | 61 | Parameters 62 | ---------- 63 | pair : str 64 | Currency pair for which to get order book data. 65 | ti : int 66 | Initial timestamp for range based query. (optional) 67 | tf : int 68 | Final timestamp for range based query. (optional) 69 | 70 | Returns 71 | ------- 72 | list 73 | List of dicts of order book data. 74 | 75 | """ 76 | 77 | fe = Key('timestamp').between(ti, tf) & Key('pair').eq(pair) 78 | scan = self.table.scan(FilterExpression=fe) 79 | 80 | return scan.get('Items', []) 81 | -------------------------------------------------------------------------------- /utils/processing.py: -------------------------------------------------------------------------------- 1 | import time 2 | import pickle 3 | import logging.config 4 | from decimal import Decimal 5 | from requests import HTTPError 6 | 7 | 8 | logger = logging.getLogger(__name__) 9 | 10 | 11 | def pickle_data(path, data): 12 | with open(path, 'wb') as f: 13 | pickle.dump(data, f) 14 | 15 | 16 | def unpickle_data(path): 17 | with open(path, 'rb') as f: 18 | data = pickle.load(f) 19 | return data 20 | 21 | 22 | def query_order_book(api, pair, count): 23 | """Queries the kraken order book. 24 | 25 | Parameters 26 | ---------- 27 | api : Kraken API Object 28 | A kraken api object to query for the orderbook data. 29 | pair : str 30 | Asset pair to get market depth for. 31 | count : int 32 | Maximum number of asks/bids 33 | 34 | Returns 35 | ------- 36 | dict 37 | Kraken response with orderbook data. Empty dict if fails. 38 | """ 39 | 40 | try: 41 | response = api.query_public('Depth', {'pair': pair, 'count': count}) 42 | except HTTPError: 43 | response = {} 44 | logger.error("Failed querying order book data for pair=%s.", pair) 45 | 46 | obook = response.get('result', {}).get(pair) 47 | 48 | return obook 49 | 50 | 51 | def reformat_orderbook(obook, pair): 52 | """Process the orderbook response for write to DynamoDB. 53 | 54 | Parameters 55 | ---------- 56 | obook : dict 57 | The kraken response returned by query_order_book(...). 58 | pair : str 59 | The currency pair denomination of the orderbook. 60 | 61 | Returns 62 | ------- 63 | dict 64 | The orderbook in format for DynamoDB insertion. Returns empty dict on error. 65 | """ 66 | 67 | if not obook: 68 | item = {} 69 | logger.error("Orderbook for pair=%s is empty. Can't reformat for DB insertion.", pair) 70 | else: 71 | asks = [] 72 | bids = [] 73 | for order in obook['asks']: 74 | asks.append([Decimal(order[0]), Decimal(order[1]), order[2]]) 75 | for order in obook['bids']: 76 | bids.append([Decimal(order[0]), Decimal(order[1]), order[2]]) 77 | item = { 78 | 'pair': pair, 79 | 'timestamp': int(time.time()), 80 | 'asks': asks, 81 | 'bids': bids 82 | } 83 | 84 | return item 85 | 86 | 87 | def mongo_reformat_orderbook(obook, pair): 88 | """Process the orderbook response for write to MongoDB. 89 | 90 | Parameters 91 | ---------- 92 | obook : dict 93 | The kraken response returned by query_order_book(...). 94 | pair : str 95 | The currency pair denomination of the orderbook. 96 | 97 | Returns 98 | ------- 99 | dict 100 | The orderbook in format for DynamoDB insertion. Returns empty dict on error. 101 | """ 102 | 103 | if not obook: 104 | item = {} 105 | logger.error("Orderbook for pair=%s is empty. Can't reformat for DB insertion.", pair) 106 | else: 107 | item = { 108 | 'pair': pair, 109 | 'timestamp': time.time(), 110 | 'asks': obook['asks'], 111 | 'bids': obook['bids'] 112 | } 113 | 114 | return item 115 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Cloud scraper for cryptocurrency order book data. 2 | ![python version](https://img.shields.io/badge/python-3.6-blue.svg) 3 | 4 | Create an event schedule to scrape order book data from a public exchange API (here implemented with [Kraken API](https://www.kraken.com/help/api)), compresses it into relevant features, and insert it into a private database. The scraper is hosted serverless on AWS Lambda making it virtually free to run. 5 | 6 | # Resources 7 | - [Kraken visualization of their data](https://support.kraken.com/hc/en-us/articles/115000364388-Trading-Glossary) 8 | - [Kraken API](https://www.kraken.com/help/api) 9 | 10 | ## Tech 11 | - [Zappa](https://github.com/Miserlou/Zappa) - deploy Python Lambdas and schedule events 12 | - [krakenex](https://github.com/veox/python3-krakenex) - API for Kraken exchange 13 | - [AWS Lambda](https://aws.amazon.com/lambda/) - serverless compute service 14 | - [AWS RDS](https://aws.amazon.com/rds/) - relational database service 15 | 16 | ## Installation and Setup 17 | 18 | ### Clone 19 | Clone this repo to your local machine. 20 | ``` 21 | $ git clone https://github.com/hexamax/orderbook-crypto-scraper.git 22 | ``` 23 | 24 | ### Install Requirements 25 | Zappa requires an active [virtual environment](https://virtualenv.pypa.io/en/latest/installation/) to deploy. Either install and activate your own virtual environment or execute the following steps. 26 | ``` 27 | $ cd ohlc-crypto-scraper 28 | $ virtualenv -p python3 venv 29 | $ source venv/bin/activate 30 | $ pip install -r requirements.txt 31 | ``` 32 | 33 | ### Configure Database 34 | Write your database credentials into the corresponding fields of the database configuration file located at `ohlc-crypto-scraper/db_config.py` 35 | 36 | **WARNING**: The scraper was written, used and tested for a PostgreSQL database only. For compatability make sure to be running a Postgres instance as well. To set up a low cost RDS Postgres instance on AWS check out this [tutorial](https://aws.amazon.com/getting-started/tutorials/create-connect-postgresql-db/). 37 | 38 | ### Zappa Settings (Optional) 39 | The `zappa_settings.json` file was initialized with some sensible defaults and will run fine without additional manipulation. However, here are some easy changes you can make to customize your deploy: 40 | - Specify the rate at which the data scraping event is executed by changing the [rate expression](https://docs.aws.amazon.com/AmazonCloudWatch/latest/events/ScheduledEvents.html#RateExpressions) located in `zappa_settings.json > events > expression`. 41 | - Specify the [aws region](https://docs.aws.amazon.com/general/latest/gr/rande.html) of your deploy in the `aws_region` field. 42 | - Specify a custom name for your S3 bucket using the `s3_bucket` field. 43 | 44 | ## Deploy and Schedule 45 | 46 | ### Initial Deploy 47 | Use the following command for the initial deploy only. 48 | ``` 49 | $ zappa deploy scrape_event 50 | ``` 51 | Zappa will spit out the deployment information to your terminal and let you know if the deploy was succesfull. If the deploy was succesfull your data scraper should now be up and running. 52 | 53 | Subsequent deploys are possible by calling zappa update. 54 | ``` 55 | $ zappa update scrape_event 56 | ``` 57 | 58 | ### Schedule 59 | If you decided to change the rate expression in the `zappa_settings.json` file you can easily reschedule your scraper. 60 | ``` 61 | $ zappa schedule scrape_event 62 | ``` 63 | 64 | ### Undeploy 65 | This will remove the Lambda function. 66 | ``` 67 | $ zappa undeploy scrape_event 68 | ``` 69 | 70 | ## Logs 71 | You can monitor your scraper's AWS CloudWatch logs directly from the console. 72 | ``` 73 | $ zappa tail scrape_event 74 | ``` 75 | -------------------------------------------------------------------------------- /research/mongodb_connection_test.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "from pymongo import MongoClient" 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 2, 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "username = 'xxxxxxxx'\n", 19 | "password = 'xxxxxxxx'\n", 20 | "host = 'xxxxxxxx'" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 3, 26 | "metadata": {}, 27 | "outputs": [], 28 | "source": [ 29 | "client = MongoClient(\n", 30 | " host,\n", 31 | " username=username,\n", 32 | " password=password\n", 33 | ")" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": 4, 39 | "metadata": {}, 40 | "outputs": [], 41 | "source": [ 42 | "db = client.get_database('orderbooks')" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": 5, 48 | "metadata": {}, 49 | "outputs": [], 50 | "source": [ 51 | "results = db.XXMRZUSD.find({}, limit=10)" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": 6, 57 | "metadata": {}, 58 | "outputs": [ 59 | { 60 | "name": "stdout", 61 | "output_type": "stream", 62 | "text": [ 63 | "[{'_id': ObjectId('5ca927509ca0b000018881d1'), 'pair': 'XXMRZUSD', 'timestamp': 1554589520.2777784, 'asks': [['67.76000000', '22.637', 1554589518], ['67.91000000', '0.330', 1554589518], ['67.93000000', '24.176', 1554589517], ['67.94000000', '25.000', 1554589519], ['67.96000000', '85.344', 1554589506], ['68.00000000', '2.834', 1554589513], ['68.01000000', '53.393', 1554589512], ['68.05000000', '22.500', 1554589517], ['68.10000000', '10.932', 1554589437], ['68.26000000', '139.122', 1554589516], ['68.27000000', '50.050', 1554589481], ['68.28000000', '41.007', 1554589250], ['68.38000000', '82.696', 1554589499], ['68.39000000', '500.500', 1554589489], ['68.72000000', '0.400', 1554589514], ['68.73000000', '1336.863', 1554589417], ['69.20000000', '250.250', 1554589365], ['69.43000000', '100.100', 1554587794], ['69.52000000', '0.554', 1554582930], ['69.75000000', '0.900', 1554583415], ['70.29000000', '50.050', 1554588297], ['70.41000000', '0.840', 1554569105], ['70.45000000', '1.690', 1554569996], ['70.48000000', '50.050', 1554584866], ['70.54000000', '4.210', 1554569088]], 'bids': [['67.61000000', '43.972', 1554589518], ['67.53000000', '25.000', 1554589508], ['67.44000000', '7.742', 1554589518], ['67.43000000', '4.031', 1554589508], ['67.41000000', '434.443', 1554589511], ['67.39000000', '2.833', 1554589500], ['67.37000000', '2.834', 1554589453], ['67.36000000', '22.500', 1554589518], ['67.23000000', '50.050', 1554589513], ['67.22000000', '30.000', 1554589506], ['67.21000000', '41.007', 1554589211], ['67.16000000', '500.500', 1554589498], ['67.15000000', '127.462', 1554589268], ['66.90000000', '703.500', 1554589460], ['66.89000000', '2.865', 1554589139], ['66.68000000', '11.635', 1554589507], ['66.66000000', '3.732', 1554589488], ['66.56000000', '24.282', 1554589507], ['66.47000000', '250.250', 1554589226], ['65.74000000', '36.916', 1554589507], ['65.65000000', '10.140', 1554554707], 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null, 74 | "metadata": {}, 75 | "outputs": [], 76 | "source": [] 77 | }, 78 | { 79 | "cell_type": "code", 80 | "execution_count": null, 81 | "metadata": {}, 82 | "outputs": [], 83 | "source": [] 84 | } 85 | ], 86 | "metadata": { 87 | "kernelspec": { 88 | "display_name": "Python 3", 89 | "language": "python", 90 | "name": "python3" 91 | }, 92 | "language_info": { 93 | "codemirror_mode": { 94 | "name": "ipython", 95 | "version": 3 96 | }, 97 | "file_extension": ".py", 98 | "mimetype": "text/x-python", 99 | "name": "python", 100 | "nbconvert_exporter": "python", 101 | "pygments_lexer": "ipython3", 102 | "version": "3.6.8" 103 | } 104 | }, 105 | "nbformat": 4, 106 | "nbformat_minor": 2 107 | } 108 | -------------------------------------------------------------------------------- /research/depth_compression.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 42, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import krakenex\n", 10 | "import numpy as np\n", 11 | "import pandas as pd\n", 12 | "import seaborn as sns\n", 13 | "import matplotlib.pyplot as plt\n", 14 | "from requests.exceptions import HTTPError" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 301, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "kraken = krakenex.API()" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": 524, 29 | "metadata": {}, 30 | "outputs": [], 31 | "source": [ 32 | "def get_order_book(pair, count):\n", 33 | " \"\"\"Queries the kraken order book.\n", 34 | "\n", 35 | " Parameters\n", 36 | " ----------\n", 37 | " pair : str \n", 38 | " Asset pair to get market depth for.\n", 39 | " count : int \n", 40 | " Maximum number of asks/bids\n", 41 | "\n", 42 | " Returns\n", 43 | " -------\n", 44 | " dataframe\n", 45 | " Order book as dataframe with columns price, volume, timestamp, type, cumvolume, relprice.\n", 46 | " \"\"\"\n", 47 | " \n", 48 | " try:\n", 49 | " response = kraken.query_public('Depth', {'pair': pair, 'count': count})\n", 50 | " except HTTPError as e:\n", 51 | " print(\"ERROR: Failed getting order book data for pair={}\".format(pair))\n", 52 | " \n", 53 | " obook = response.get('result', {}).get(pair)\n", 54 | " if not obook:\n", 55 | " print(\"WARNING: Empty response.\")\n", 56 | " else:\n", 57 | " # add cumvolume\n", 58 | " asks = [limorder + ['ask'] for limorder in obook['asks']]\n", 59 | " bids = [limorder + ['bid'] for limorder in obook['bids']]\n", 60 | " tabular = asks + bids\n", 61 | " obook = pd.DataFrame(tabular, columns=['price', 'volume', 'timestamp', 'type']) \n", 62 | " obook = obook.apply(pd.to_numeric, errors='ignore')\n", 63 | " obook['cumvolume'] = obook[['type', 'volume']].groupby('type').cumsum() # assumes rows are sorted\n", 64 | " \n", 65 | " # add relprice\n", 66 | " minask = obook.loc[obook['type'] == 'ask', 'price'].min()\n", 67 | " maxbid = obook.loc[obook['type'] == 'bid', 'price'].max()\n", 68 | " spread = minask - maxbid\n", 69 | " midprice = (minask + maxbid) / 2\n", 70 | " obook['relprice'] = 100 * (obook['price'] - midprice) / midprice\n", 71 | " \n", 72 | " return obook\n", 73 | "\n", 74 | "\n", 75 | "def plot_order_book(ob):\n", 76 | " \"\"\"Plots the cumulative volume and order distribution of the order book.\n", 77 | " \n", 78 | " Paramaters\n", 79 | " ----------\n", 80 | " ob : dataframe\n", 81 | " Order book data frame with columns type, price, volume, cumvolume, relprice.\n", 82 | " \"\"\"\n", 83 | " \n", 84 | " # extract data series from df\n", 85 | " ask_prices = ob.loc[ob['type'] == 'ask', 'price']\n", 86 | " ask_relprices = ob.loc[ob['type'] == 'ask', 'relprice']\n", 87 | " ask_volumes = ob.loc[ob['type'] == 'ask', 'volume']\n", 88 | " ask_cumvolumes = ob.loc[ob['type'] == 'ask', 'cumvolume']\n", 89 | " bid_prices = ob.loc[ob['type'] == 'bid', 'price']\n", 90 | " bid_relprices = ob.loc[ob['type'] == 'bid', 'relprice']\n", 91 | " bid_volumes = ob.loc[ob['type'] == 'bid', 'volume']\n", 92 | " bid_cumvolumes = ob.loc[ob['type'] == 'bid', 'cumvolume']\n", 93 | " \n", 94 | " # plot cum vol plot\n", 95 | " fig, ax = plt.subplots(2, 1, figsize=(16, 10)) # sharex='col'\n", 96 | " ax[0].fill_between(bid_relprices, bid_cumvolumes, facecolor='maroon', edgecolor='k', alpha=0.7, zorder=10, label='bids')\n", 97 | " ax[0].fill_between(ask_relprices, ask_cumvolumes, facecolor='dimgrey', edgecolor='k', alpha=0.7, zorder=10, label='asks')\n", 98 | " ax[0].set_xlim([min(bid_relprices.min(), ask_relprices.min()), max(bid_relprices.max(), ask_relprices.max())])\n", 99 | " ax[0].set_ylim([0, 1.05* max(bid_cumvolumes.max(), ask_cumvolumes.max())])\n", 100 | " ax[0].set_xlabel('Distance to midprice (%)')\n", 101 | " ax[0].set_ylabel('Cumulative Volume')\n", 102 | " ax[0].legend(loc='upper center', facecolor='white', framealpha=1, frameon=False)\n", 103 | " \n", 104 | " # plot order distribution\n", 105 | " ax[1].stem(bid_prices, bid_volumes, 'maroon', markerfmt=' ', label='bids')\n", 106 | " ax[1].stem(ask_prices, ask_volumes, 'k', markerfmt=' ', label='asks')\n", 107 | " ax[1].set_ylim([0, 1.05* max(bid_volumes.max(), ask_volumes.max())])\n", 108 | " ax[1].set_xlabel('Price')\n", 109 | " ax[1].set_ylabel('Volume')\n", 110 | " \n", 111 | " plt.show()" 112 | ] 113 | }, 114 | { 115 | "cell_type": "code", 116 | "execution_count": 530, 117 | "metadata": {}, 118 | "outputs": [ 119 | { 120 | "data": { 121 | "text/html": [ 122 | "
\n", 123 | "\n", 136 | "\n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | "
pricevolumetimestamptypecumvolumerelprice
00.275192375.8711553289732ask2375.8710.034534
10.275313900.6361553289743ask6276.5070.078155
20.275329653.5811553289742ask15930.0880.081790
30.275343493.4771553289736ask19423.5650.089060
40.275364500.0001553289718ask23923.5650.096330
\n", 196 | "
" 197 | ], 198 | "text/plain": [ 199 | " price volume timestamp type cumvolume relprice\n", 200 | "0 0.27519 2375.871 1553289732 ask 2375.871 0.034534\n", 201 | "1 0.27531 3900.636 1553289743 ask 6276.507 0.078155\n", 202 | "2 0.27532 9653.581 1553289742 ask 15930.088 0.081790\n", 203 | "3 0.27534 3493.477 1553289736 ask 19423.565 0.089060\n", 204 | "4 0.27536 4500.000 1553289718 ask 23923.565 0.096330" 205 | ] 206 | }, 207 | "execution_count": 530, 208 | "metadata": {}, 209 | "output_type": "execute_result" 210 | } 211 | ], 212 | "source": [ 213 | "N = 200\n", 214 | "ob = get_order_book('XXRPZEUR', N)\n", 215 | "ob.head()" 216 | ] 217 | }, 218 | { 219 | "cell_type": "code", 220 | "execution_count": 531, 221 | "metadata": {}, 222 | "outputs": [], 223 | "source": [ 224 | "# How much information can we take out while still maintaining the shape of the order book?\n", 225 | "threshold = ob.volume.nlargest(int(0.6 * N)).min()\n", 226 | "ob_sub = ob.groupby('type').apply(lambda x: x[x['volume'] > threshold])" 227 | ] 228 | }, 229 | { 230 | "cell_type": "code", 231 | "execution_count": 532, 232 | "metadata": {}, 233 | "outputs": [ 234 | { 235 | "data": { 236 | "text/plain": [ 237 | "(119, 6)" 238 | ] 239 | }, 240 | "execution_count": 532, 241 | "metadata": {}, 242 | "output_type": "execute_result" 243 | } 244 | ], 245 | "source": [ 246 | "ob_sub.shape" 247 | ] 248 | }, 249 | { 250 | "cell_type": "code", 251 | "execution_count": 533, 252 | "metadata": {}, 253 | "outputs": [ 254 | { 255 | "data": { 256 | "image/png": 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IBPS/EhERERHp3jTmuQ0a8yzyaRHnWFdZyTt1dYRzc7nk299mwoQJhEIhr6OJiIiIiByWWMc8azYiETlsPjOOycjg8wUFjNuzh+m/+hVTzj2XJ554grq6Oq/jiYiIiIi0OxXPInJUClNSOKeggM+Ewzx9001cMGkS0x95hJqaGq+jiYiIiIi0GxXPItIu8pKTmVhQwFl+P//64x+5YNIkHpw6laqqKq+jiYiIiIgcNRXPItKuspOSOKuggHMSEnj1zjuZMmkS9919N7t27fI6moiIiIjIEVPxLCJxkZGYyGcLCjgvJYUl99/Pf0yezJ9vvZVt27Z5HU1ERERE5LCpeBaRuEoLhfhMQQFfTk9n5cMPc8nnPscfbr6Zjz/+2OtoIiIiIiIxU/EsIh2iVzDIyfn5fDUzk3WPP87XzzuPm379azZu3Oh1NBERERGRNuk9z23Qe55F4qOuoYG3d+zgPWDchAl87fLLKSsr8zqWiIiIiPQwes+ziHRqiYEAJ+TnMyUnh+o5c/juBRdw7Y9+xPvvv+91NBERERGRT1HxLCKeCvn9jM7L48K8PPa98grXXHQRP/rud1m+fDl6MkZEREREOgsVzyLSKQR8Pkbl5XFhfj6hJUv4+de/zvevvpolS5aoiBYRERERz6l4FpFOxe/zMTwnhykFBWS+8w43fOMbfPPSS1mwYIGKaBERERHxjIpnEemUfGYMyc7mgoICitau5ebvfpcrLryQtWvXeh1NRERERHogFc8i0qmZGQMzM/lKQQG569bx21/8goaGBq9jiYiIiEgPo+JZRLoEM2NEbi41q1bx1D/+4XUcEREREelhVDyLSJdhZpyamckDf/oT27Zt8zqOiIiIiPQgKp5FpEvJTEykf309t99yi9dRRERERKQHCXgdQETkcI3JzeXxuXNZuHAhY8eO9TqOiIiIiHRSzjlqa2vZvXs3NTU17N69u3m7pqaGqqoqzCwrlmupeBaRLifg8zG+Vy9+f8MNPPTEEyQmJnodSUREREQ6Eeccr7/+OnfeeScff/wxCQkJhEIhAoEAwWAQv99PIBDA7/eTnJycH8s1VTyLSJfUJy2N9z75hGn3389V3/wmfr/f60giIiIi0gksW7aMO++8k82bNzN8+HBOOukkzKzV42fMmBHTdVU8i0iXNT47m2enTuXvDz/MoPJyRp14IkOGDmXQoEFkZmZ6HU9EREREOtDq1au55557WLFiBeXl5UyaNOmQRfPhilvxbGYlwENAPuCAe51zt5rZ9cBVQNNUuT93zj0XPednwBVAGPi+c252tH0icCvgB/7inLsp2t4PmA5kA0uArznn6s0sIXrv0cAO4ALn3PpD3UNEup5ewSDnFxZS19DAJ6tW8dbSpcwNBNgWDpOem8vw447j2OOPZ/DgwZSVlREMBr2OLCIiIiLtbNOmTdx3333Mnz+fgQMHcs4558TlqURzzrX7RQHMrBAodM69aWapNBa3XwDOB3Y752454Phy4FHgBKAIeB4YGN29CjgL2AS8AVzonHvXzGYATzrnppvZ3cAy59xdZvZt4Fjn3DfNbArwRefcBa3dwzkXbu179MnKcj8cOZKCXr3a5w9GROLOOceuujo+rqlhazjMTp+PKuc4ZtAgRo4bR/nw4QwaNIjc3Nx2/W2kiIiIiHSc7du3M23aNGbPnk1ZWRnl5eVH1Fnyk5/8pHb37t1tFnxx63l2zn0MfBzdrjazlUDxIU45D5junNsLrDOzNTQWuQBrnHNrAcxsOnBe9HqfBf4jesyDwPXAXdFrXR9t/z/gz9b4N+TW7jH/6L+xiHQWZkZWUhJZSUkMjbbVh8Ns2bCBle++y2t+P9siERLT0xk6ahQjTjiBIUOG0L9/f00+JiIiItLJVVdX88gjj/Dkk09SVFTEpEmTOuTvcB0y5tnMSoFRwEJgPPBdM7sEWAz8l3NuF42F9YIWp23i38X2xgPax9L4qHaFc67hIMcXN53jnGsws8ro8Ye6h4h0YyG/n5K0NErS0oDG3umq+no+fu01XnjpJR73+9kVidCnrIzhY8bQu7SUnJwc+vXrR9++fT1OLyIiIiJ1dXX8/e9/56GHHiI7O5szzzyTXh34hHDci2czSwGeAH7gnKsys7uAX9M4DvrXwO+By+Od43CY2dXA1QC5yckepxGReDAz0hMSSE9IYHC0rSESYeuWLWyeMYP3IxHqAgG2BAI89I9/kJOT42leERERkZ7KOcesWbO45557SEpK4pRTTiEjI6PDc8S1eDazII2F88POuScBnHNbWuy/D3gm+nEzUNLi9N7RNlpp3wFkmFkg2vvc8vima20yswCQHj3+UPdo5py7F7gXGsc8H963FpGuKuDzUZSSQlFKSnPby598woxHHuHb3/++h8lEREREeqa6ujpuvvlmFi1axAknnOBph4YvXheOjjG+H1jpnPtDi/bCFod9EVgR3X4amGJmCdFZtAcAi2icIGyAmfUzsxAwBXjaNc509iLwlej5lwJPtbjWpdHtrwAvRI9v7R4iIgd1XFYWM6dPp6KiwusoIiIiIj3K5s2bufrqq3nvvfeYMGGC508Cxq14pnFs89eAz5rZ0ugyGbjZzJab2dvA6cAPAZxz7wAzgHeBfwLfcc6Fo73K3wVmAyuBGdFjAf4fcE104q9sGot1ouvsaPs1wE8PdY84/hmISBeXEgpRsm8fTzz+uNdRRERERHqM119/nauuuor09HTGjx9PINAh03UdUtxeVdVd6FVVIlK1dy8z9+zh0eeeIzU11es4IiIiIt1WOBxm2rRpPPbYY5x44onk5+fH/Z6xvqoqnj3PIiLdQlpCAvl1dTz8179SXV3tdRwRERGRbqmyspKf/OQnPPPMM5x99tkdUjgfjpj6vs3sZGCAc26qmeUCKc65dfGNJiLSeRyfmcm8adN44oEHKOzThxFjx3LscccxZMgQiouLaZzmQURERESOxOrVq/n5z39OSkoKZ5xxBj5f5+vnbbN4NrPrgDHAIGAqEAT+RuOYZhGRHiE9IYHP5+cTcY5tFRV89MQTPPLkk2wHSEpi2KhRjDrxRMrLyxkwYAAJCQleRxYRERHpEmbNmsWf/vQnhg8fTllZmddxWhVLz/MXgVHAmwDOuY/MTIP+RKRH8pmR36sX+S3mQdhdX89Hixbx4quv8n9+P7siEcoGDmTE2LEMHzmSIUOGeD47pIiIiEhnU19fz+23387zzz/PZz7zGU/e3Xw4Yime651zzswcgJlp5iwRkRZSQiEGZmUxMPq5IRJhy6ZNrH7/fRY8/DBbIxGKy8o4/ZxzGH/yyZSWluoxbxEREenRtm7dyn//939TUVHBxIkTCQaDXkdqUyzF8wwzuwfIMLOrgMuB++IbS0Sk6wr4fBSnplIcnZk74hwfffIJr912G4/dfjvJubl89pxzGH/qqZSXl3fKMT0iIiIi8fLmm29y3XXX0bt3b0499dQu06nQZvHsnLvFzM4Cqmgc9/xL59ycuCcTEekmfGb0Tk2ld2oqzjm279nDu1On8q8HH2RfcjKnTpjAKZ/9LKNGjSIUCnkdV0RERCQunHNMnz6dqVOncsIJJ1BUVOR1pMMS02zbzrk5Zraw6Xgzy3LO7YxrMhGRbsjMyE1OJjc5GYDKvXtZ8/e/88d//IPKQICxp57KsNGjycjI2G9JT0/vEo8ziYiIiLRm3rx5PPTQQ5x55pmkpKR4HeewxTLb9jeAXwF1QAQwwAGddxo0EZEuIj0hgdH5+YwGavftY+1LLzF3zhzq/X72+nzUAXsiEfaEwyQlJ5ORmUlWTg5ZOTnkFBSQU1CwX5GdmZlJfn5+l3n8SURERHqG6upq/vCHP3DCCSd0ycIZYut5/hEwzDm3Pd5hRER6suRgkGGtzMrtnKMuHKa2poaaigpqV65kQ0MD74XDjYW2308dUBuJ4EIhBg8fzrEnnMDgIUMYMGAAWVlZHftlRERERFq46667mn/J31XFUjx/ANTGO4iIiLTOzEgKBEgKBMhOSjrksbX79vHx8uW8sWgRcwIBtoXDpGZnc8Mf/kB5eXkHJRYRERFptHTpUubOncvkyZO9jnJUYimefwa8Hh3zvLep0Tn3/bilEhGRI5YcDHJMRgbHRN+V6Jxj3ubNLF26VMWziIiIdKi9e/dy0003MXLkyC4/f0ssxfM9wAvAchrHPIuISBdiZmQlJPDR+vVeRxEREZEe5q9//SsAffv29TjJ0YuleA46566JexIREYmbtFCIjz780OsYIiIi0oOsXbuWGTNmMGHCBK+jtAtfDMfMMrOrzazQzLKalrgnExGRdpMaCvHJRx95HUNERER6iHA4zE033cSQIUNIjr6is6uLpef5wuj6Zy3a9KoqEZEuJDUUYtvWrTjn9BorERERibuZM2eydetWzjrrLK+jtJs2i2fnXL+OCCIiIvET8vvxhcNUVVWRnp7udRwRERHpxurr65k2bRrHHXdct/qlfZvFs5ldcrB259xD7R9HRETiJTUQYM2aNYwePdrrKCIiItKN/etf/yIQCJCTk+N1lHYVy5jn41sspwDXA5+PYyYREYmD4X4///3tb3P/vfdSV1fndRwRERHphhoaGpg2bRpDhw71Okq7i+Wx7e+1/GxmGcD0uCUSEZG4GJiZSfG+fbx6333MmTmTH113HWPGjPE6loiIiHQjL7zwApFIhPz8fK+jtLtYep4PVANoHLSISBfUKxhkQkEBIyor+dU3v8kNv/gFO3fu9DqWiIiIdAPhcJipU6dSXl7udZS4iGXM80waZ9eGxmK7HJgRz1AiIhJfpenp9E5NZdG//sWUOXPo3bcvZYMGMWDYMPr27UtJSQmFhYX4fEfyO1YRERHpiV555RX27NlDYWGh11HiIpZXVd3SYrsB2OCc2xSnPCIi0kECPh8n5eczJhxmx7Zt7Ny4kXmzZlEdCFDhHHVmlPTpwzGDB9N/6FD69u1Lnz59KCgoUFEtIiIi+3HOcf/99zN48OBuNcN2S7GMeZ7XEUFERMQbIb+fwpQUClNS9muvjxbVOzZu5KVnn2V3MPiporqotJSc3Fxyc3MZMWIESUlJHn0LERER8dLrr79ORUUF48aN8zpK3LRaPJtZNf9+XHu/XYBzzqXFLZWIiHiuraJ658aNLN23jz1m1Pr97PT7OW3yZCZ+7nMMHz682/7WWURERPbnnOOBBx7o1r3OcIji2TmX2pFBRESka2itqK7dt4+VTz3FdU89RSg3l3PPP5+zzj6bgoICj5KKiIhIR1iyZAlbtmzhuOOO8zpKXMUy5hkzG0HjO54BXnbOvR2/SCIi0hUlB4OMzs9nNLClpobX/vxnHrnjDgaOHMnnzj+f8ePH67FuERGRbqZprPPAgQO7da8zxPCqKjP7T+BhIC+6PGxm3zv0WWBmJWb2opm9a2bvRK+DmWWZ2RwzWx1dZ0bbzcxuM7M1Zva2mR3X4lqXRo9fbWaXtmgfbWbLo+fcZtF/WkdyDxERaT/5vXrxmYICLsrNJfvdd3nw5z/nS2eeyf/+5je8/fbbOHewUUEiIiLS1SxfvpwNGzZQVlbmdZS4i2W61CuAsc65XzrnfgmMA66K4bwG4L+cc+XRc75jZuXAT4G5zrkBwNzoZ4BJwIDocjVwFzQWwsB1wFjgBOC6pmI4esxVLc6bGG0/rHuIiEh8+H0+BmRmMjk/n6+kprL16af5xeWX89dp07yOJiIiIu1g6tSpDBgwoEe8iSOWb2hAuMXncLTtkJxzHzvn3oxuVwMrgWLgPODB6GEPAl+Ibp8HPOQaLQAyzKwQOBuY45zb6ZzbBcwBJkb3pTnnFrjGLoyHDrjW4dxDRETiLDkYZEx+Pmemp/P09OlEIhGvI4mIiMhRWLlyJStXrqR///5eR+kQsRTPU4GFZna9mV0PLADuP5ybmFkpMApYCOQ75z6O7voEyI9uFwMbW5y2Kdp2qPZNB2nnCO5xYN6rzWyxmS2u27s3ti8pIiIxyU1OxlVUsHz5cq+jiIiIyFGYNm0axxxzDH6/3+soHaLN4tk59wfgcmBndPm6c+5Psd7AzFKAJ4AfOOeqDri24+Cvw2o3R3IP59y9zrkxzrkxiQkJcUolhkhFAAAgAElEQVQmItJzlTrHP2fO9DqGiIiIHKE1a9awdOlSBg8e7HWUDtNq8Wxmz5nZxWaW4pxb4py7Lbq8FevFzSxIY+H8sHPuyWjzlqZHpaPrrdH2zUBJi9N7R9sO1d77IO1Hcg8REelAQ7KymDdrFnV1dV5HERERkSPw4IMPUlZW1mN6neHQPc/3AOcA68xshpl90cxCsV44OvP1/cDKaO91k6eBphmzLwWeatF+SXRG7HFAZfTR69nABDPLjE4UNgGYHd1XZWbjove65IBrHc49RESkA/UKBsnct4/58+d7HUVERETa4Jyjrq6OnTt3snnzZubPn8+CBQt6VK8zHOI9z865p4CnzCwZ+ByNxeldZjYLeMQ5N6eNa48HvgYsN7Ol0bafAzcBM8zsCmADcH5033PAZGANUAt8PZpjp5n9GngjetwNzrmd0e1vA9OAJGBWdOFw7yEiIh2vfyDAs088wemnn+51FBERkR5t69atzJw5k02bNlFdXc3u3bupqamhtraWmpoa9uzZg8/nIxgMEgwGCQQCHHvssQSDQa+jdyg7nHdtmtmxNM5efaxzrkf0z/fJynI/HDmSgl69vI4iItKtNEQi/G3bNh6ZNYvs7Gyv44iIiPQ4GzZsYOrUqbz22msUFRWRlZVFKBTab2kqmLvz49k/+clPanfv3t1mwddqz3MTM8unsed2ClAIzAAuO9qAIiLSswV8Pno7xzPPPMPEiRPJzMwkFIp5dJCIiIgchQ8//JDvfOc7FBcXc8455+hncAxaLZ7N7CrgQmAQjZN+/dg593pHBRMRke5vaEoKz951F4/feSe1DQ0k9epFTm4uOXl55BUWkldcTHZuLllZWQwcOJC8vDyvI4uIiHR527dv54c//CH9+/dn4MCBXsfpMg7V83wicCMw1zkX6aA8IiLSgxSmpPDFlBSgcTKSPQ0N1FRUULNtG1VLl/JRfT11Ph91fj9bwmHySko4+ayzGHvSSZSXl/e4sVYiIiJHyjlHbW0tO3fu5NprryU3N1eF82E61IRhl3dkEBER6dnMjORgkORgkNyD7HfO8cmuXbzzwAM8/+CDVPv9jD7pJE4+4wzGjBlDTk5Oh2cWERHpKE3Fb9NEXrt3727+3LRUVVVRVVVFdXV188RfTUtVVRUASUlJFBUVceyxx3r8jbqeNsc8i4iIdAZmRmFKCoXRnurafftY/+qrPDZvHn90jsLSUk6ZMIHzp0whNTXV47QiIiKHp7a2lr/97W9s27atufitrq5uLoxra2sJBALNE3n5/f7mddMSCARISEhoPiY/P5/evXuTkJBAYmIigYDKv6OhPz0REemSkoNBynNyKAcizvHJ9u28cPvtVFdW8oMf/cjreCIiIodl2rRpzJkzhz59+hAKhcjMzCQ/P5+EhASCwSChUAifz+d1zB4tpuLZzE4GBjjnpppZLpDinFsX32giIiKx8ZlRlJJCZmIiMx5/nAsuuojCwkKvY4mIiMRk7dq1/OMf/2DixIkkJiZ6HUda0eavLszsOuD/AT+LNgWBv8UzlIiIyJFICgQY5BzT7rvP6ygiIiIxcc7xu9/9jsGDB6tw7uRi6ff/IvB5oAbAOfcRoMFkIiLSKR2Xk8PLM2eyfv16r6OIiIi0adasWWzevJlBgwZ5HUXaEEvxXO+cc4ADMLNe8Y0kIiJy5EJ+P0N9Pu6/6y6vo4iIiBxSZWUld9xxB8cffzxm5nUcaUMsxfMMM7sHyDCzq4DnAT0PJyIindaInBzefOEF3n//fa+jiIiItOruu+8mNzeX7Oxsr6NIDNosnp1ztwD/BzwBDAJ+6Zy7Pd7BREREjlTA5+NYv5/7btePKxER6ZxWrFjB3LlzGTVqlNdRJEZtzrZtZtcAjznn5nRAHhERkXYxNDubGQsXsmzZMkaMGOF1HBERkWYNDQ3cfPPNHHvssQSDQa/jSIxieWw7FfiXmb1iZt81s/x4hxIRETlafp+PkcEg9956K41Td4iIiHQOTzzxBDU1NfTr18/rKHIYYnls+1fOuaHAd4BCYJ6ZPR/3ZCIiIkdpcFYWm99+m0WLFnkdRUREBICtW7fywAMPMGbMGE0S1sXE0vPcZCvwCbADyItPHBERkfZjZoxJSuKu3/+empoar+OIiIhw66230qdPH9LT072OIocpljHP3wbOB3KBx4GrnHPvxjuYiIhIeyhLT2fT+vVc+uUvM3DIEALBIIFgkGB0HQiFCAaDBEOh/T4HAgF8Ph+BQAC/308gENhvu7V10zkZGRlkZWWpV0FERJotWLCAN998k8mTJ3sdRY5Am8UzUAL8wDm3NN5hRERE2puZ8Zn8fD7evZuaN94g7BwNzlHvHBHnCEeXiHNEIhEiQDgSwZnhzMDnIwzg8xEBnFnzOgw4+He7c0Sin2saGgilpNB/0CCGjBpF/4ED6devH7179yYQiOXHr4iIdCd1dXXccsstHHfccfj9fq/jyBFo9ae3maU556qA30U/Z7Xc75zbGedsIiIi7aYwJaVD7+eco2bfPra+8w4rFi/mNb+fCjNqnKNPv34MLC+nV2oq/qYe8ECguVfc7/fj8/nw+/379Wg3fT7YEgwGKSgoIDc3V73dIiKd0N/+9jcCgQDFxcVeR5EjdKhffT8CnAssofEX6y1/EjugLI65REREujQzIyUUIiUUoiwjo7m9Phxm+5Yt7Fi/nu2RSGNvdcul8eTmnm8HRMygqccbmvc1fY4AYaAqEqHB56OktJSyQYM4ZsgQ+vTpQ0lJCQUFBerxFhHxyIYNG5gxYwYTJkzwOoochVZ/ijrnzo2uNX+6iIhIOwn5/RSlpFAUp57wveEwOz/5hB3r1zPvmWeoDgSoAmqdo7C4mLKBAzmmvJy+paWUlJRQXFxMKBSKSxYREWl8Eun3v/89AwYMIDk52es4chRimTBsrnPujLbaRERExHsJfj+FKSmfeky9IRJhZ2UlO15+mUXPP8/cQIBKoDocpne/fvzh7rvJyso6+EVFROSIzZ07l3Xr1nH22Wd7HUWO0qHGPCcCyUCOmWXy78e20wA9qC8iItKFBHw+8pKTyTug1yMcifDP9et55513OOWUUzxKJyLSPVVXV3PrrbcyevRofL7DeUuwdEaH+if4DRrHOw+OrpuWp4A/xz+aiIiIxJvf5yMrEmHNqlVeRxER6Xbuu+8+MjMzycvL8zqKtINDjXm+FbjVzL7nnLu9AzOJiIhIB8pNTOTdt97yOoaISLfy3nvv8c9//pNJkyZ5HUXaSZtjnp1zt5vZMKAcSGzR/lA8g4mIiEjHyOvViyUrV3odQ0Sk2wiHw/zud79j6NChJCQkeB1H2kmbD96b2XXA7dHldOBm4PMxnPeAmW01sxUt2q43s81mtjS6TG6x72dmtsbM3jezs1u0T4y2rTGzn7Zo72dmC6Ptj5lZKNqeEP28Jrq/tK17iIiI9GSpwSB7qqrYtWuX11FERLqFp59+mp07d9K/f3+vo0g7iuWFj18BRgBvOee+bmb5wN9iOG8ajWOjD+yh/qNz7paWDWZWDkwBhgJFwPNmNjC6+w7gLGAT8IaZPe2cexf43+i1ppvZ3cAVwF3R9S7nXH8zmxI97oLW7uGcC8fwXURERLotMyM7EGDt2rWMHj3a6zgiIp2Wc459+/ZRX19PfX1983bLttraWu655x5OPfVUzKzti0qXEUvxvMc5FzGzBjNLA7YCJW2d5Jx7uWWvbxvOA6Y75/YC68xsDXBCdN8a59xaADObDpxnZiuBzwL/ET3mQeB6Govn86LbAP8H/Nka/61t7R7zY8woIiLSbaXv28cHH3yg4llEJGrq1Kk888wz7N27t7k4DofD+P1+fD4fgUAAn8+H3+9vbmtaDxo0iIyMDK+/grSzWIrnxWaWAdxH42zbuzm6gvO7ZnYJsBj4L+fcLhpffbWgxTGb+PfrsDYe0D4WyAYqnHMNBzm+uOkc51yDmVVGjz/UPURERHq07GCQlUuXwvnnex1FRMRze/fuZfr06YwfP57k5GT8fn9zsaze5J6rzTHPzrlvO+cqnHN30/j49KXOua8f4f3uAo4BRgIfA78/wuvElZldbWaLzWxx3d69XscRERGJu/xevVj1zjtexxAR6RQWLVpEamoqOTk5JCcnk5CQgN/vV+Hcw7Xa82xmxx1qn3PuzcO9mXNuS4tr3Ac8E/24mf0fBe8dbaOV9h1AhpkFor3PLY9vutYmMwsA6dHjD3WPA3PeC9wL0Ccryx3etxQREel6shIT2fLRR9TV1ZGYmNj2CSIi3disWbMoLtZDqrK/Qz22faheYUfjmOPDYmaFzrmPox+/CDTNxP008IiZ/YHGybwGAIsAAwaYWT8aC90pwH8455yZvUjjZGbTgUuBp1pc61IaHy3/CvBC9PjW7iEiItLj+czI9PtZt24dQ4YM8TqOiIhnqqurWbRoEeeee67XUaSTabV4ds6dfjQXNrNHgdOAHDPbBFwHnGZmI2ksvtcD34je6x0zmwG8CzQA32maBdvMvgvMBvzAA865pmfK/h8w3cx+A7wF3B9tvx/4a3RCsJ00FtyHvIeIiIhAejis4llEerxXX32VnJwcQqGQ11Gkk2lzwrDo5F6f4pw78BVUB+6/8CDN9x+kren43wK/PUj7c8BzB2lfy79n5G7ZXgd89XDuISIiIpBlxnvLlzN58mSvo4iIeOa5556jpKTNlwtJDxTLbNvHt9hOBM4A3uTT728WERGRLiwvOZn33n7b6xgiIp7Ztm0bK1eu5LzzzvM6inRCbRbPzrnvtfwcfW3V9LglEhEREU/kJifzrw8+aH6PqYhIT/PSSy9RUFCg/wfKQbX5qqqDqAH6tXcQERER8VbI7ycZ2Lz5oC+jEBHp9l544QU9si2timXM80waJ/iCxmK7HJgRz1AiIiLijQzn+OCDD+jTp4/XUUREOtyWLVsoLS31OoZ0UrGMeb6lxXYDsME5tylOeURERMRDGeEwTz/5JADDhg0jNzfX40QiIh3DOUdFRQVJSUleR5FOKpYxz/MAzCyt6Xgzy3LO7YxzNhEREelgo/LyWL5wIdMWLWJrJEJqbi7HjRvHcePGMXToUIqKijAzr2OKiLQb5xyrV6/m9ddfByAQiKV/UXqiWB7bvhq4AagDIoDR+Bh3WXyjiYiISEcL+f2Mzs8HGv9CubOujk3PPsv0Z55hCxBISWHU2LGMPukkysvL6du3r/6iKSJdzt69e1m6dCmvvvoqL7/8Mg0NDeTl5XHaaad5HU06sVh+2v0YGOac2x7vMCIiItJ5mBnZSUlkt3iEsXLvXja99BJPPf88D/j91ADHDBzI8OOPZ8iwYQwcOFC90yLSKe3YsYNFixYxb948lixZQlpaGrm5uYwbN4709HSv40kXEEvx/AFQG+8gIiIi0vmlJySQnpDA0Ojn+nCYLRs2sGblShb5/exwjkgoxKChQyk55hhCCQn7LYFAgGAwSCAQaN5u+nxg+4HrYDBIeno6Pt+RvCxERHqa6upqVq9ezTvvvMO8efNYt24deXl5FBQUMHnyZBISEryOKF1MLMXzz4DXzWwhsLep0Tn3/bilEhERkS4h5PdTkpZGSVpac1vtvn188s47fPLWW4SdIxKJEHaOBudwZkSii2uxDpsRoXF8WMSs8TzYbx12jnogOyeHgqIiivv0obhfP/ILCsjLyyMvL4+cnByCwaA3fxgi4pl9+/axdu1a3n//fZYvX87y5cvZunUrmZmZpKWlUVRUxIgRI/T+ZjkqsRTP9wAvAMtp/JkmIiIi0qrkYJCyjIy4XLshEqG6vp6q1aupXLGCjfv2sScQoNbnY3ckQk04TEZmJvmFhRSWlFBSVkZ+YWFzcZ2Xl0diYmJcsolIx3DO8dFHH7Fq1SpWrFjBsmXLWLduHSkpKaSmppKZmcnQoUM5+eST9aSKtKtYiuegc+6auCcRERERaUPA5yMzMZHMVgrgiHPs3rePqg8/pGr1apbOns0en48av58a56huaCA5JYW8ggIKe/empKyMguLi/YrrlJQUjdkW6WQqKiqYOXMmb731Fu+++y5mRkZGBqmpqRQWFjJ8+HA9dSJxF0vxPCs64/ZM9n9sW6+qEhERkU7FZ0ZaKERaKASpqZ/a75xjT0MDlVu2ULVxI++/9BJvmlEbCFDjHLvDYXzBIHkFBRQUF9O7Xz+K+vTZr7jOzMxUcS3SgRYsWMCNN95ISkoKxcXFnHnmmSQnJ3sdS3qgWIrnC6Prn7Vo06uqREREpMsxM5KDQZKDQQpbOaauoYHqykoqt25l48KFrIxE2BMIUGPG7nCYfWbk5uU1jrvu25fifv32K66zs7P1+i6RdrBnzx7uuOMO5syZwwknnEBhYWv/1Yp0jDb/z+6c69cRQUREREQ6g8RAgMRAgNxWerYaIhGq9uyhauVKti9dyrqGhv3GXe+JRMjMyiK/lUnN8vLyCIVCHfytRDqn+vp6qqur2b17N9XV1dTU1FBdXU11dTWPPfYYPp+PSZMm6b8Z6RTaLJ7N7JKDtTvnHmr/OCIiIiKdW8DnIyspiawW779uKRyJNI67XruWqvfeY3F9/acmNUtJSyO/sJCikhKK+/WjoKhov+K6V69eHfytROLHOcemTZtYunQpixYtYu3atc0FcyQSISEhgVAotN+r6wKBAH379qVv375exxdpFsszRce32E4EzgDeBFQ8i4iIiBzA7/M1vw/7YJxz1OzbR9VHH1G1bh3vPv88b/h87PH72R2d1CwpJYWRxx/P8aecwogRI1RASJfknOOll17izjvvpLq6muzsbHJzcxk8eDCJiYkkRN/9LtJVxPLY9vdafjazDGB63BKJiIiIdGNmRkooREooRFFKyqf2u+iM4Ztee42ZL73E3Wacdt55fPN73yOtxfu0RTqbvXv3UllZSVVVFdu3b+fBBx9k06ZNjBw5UuOVpVs4kl/11AAaBy0iIiISB2ZGaijEkOxshgD14TAL/vEPLnnhBb5/7bWcfvrpmu1bPLVt2zaefPJJVq9eTUVFBbt27aKyspKGhobmHuVgMEhRURETJ07Uv6/SbcQy5nkmjbNrA/iAcmBGPEOJiIiISKOQ38+pBQV8vHs3t/34x8z97Gf5zc03qyCRDrdx40YeffRR5syZQ1FREQUFBRQXF9O/f38SExMJBoP691K6tVh6nm9psd0AbHDObYpTHhERERE5iMKUFL6SnMxj8+bx4Ycfahy0dKhXXnmFX//61/Tt25dJkyaRmJjodSSRDtdq8Wxm/YF859y8A9rHm1mCc+6DuKcTERERkWZ+n498YPny5SqepUPNnj2b8vJyBg4c6HUUEc/4DrHvT0DVQdqrovtEREREpIMV+P288dprXseQHiQcDrN48WJ69+7tdRQRTx2qeM53zi0/sDHaVhq3RCIiIiLSqpK0NN5asADnXNsHi7SDVatWEQqFSE5O9jqKiKcOVTxnHGJfUnsHEREREZG2pYVCUFvLhx9+6HUU6SHeeOMNsrOzvY4h4rlDFc+LzeyqAxvN7EpgSfwiiYiIiMih5EUiLF/+qQcEReLitddeo6ioyOsYIp471GzbPwD+bmYX8e9ieQwQAr4Y72AiIiIicnAFgQBvvPoq5557rtdRpJurrq5mzZo1DBs2zOsoIp5rtefZObfFOXcS8CtgfXT5lXPuROfcJ21d2MweMLOtZraiRVuWmc0xs9XRdWa03czsNjNbY2Zvm9lxLc65NHr8ajO7tEX7aDNbHj3nNou+VO5I7iEiIiLSlZSkpfGmxj1LB1i2bBlZWVn4/X6vo4h47lCPbQPgnHvROXd7dHnhMK49DZh4QNtPgbnOuQHA3OhngEnAgOhyNXAXNBbCwHXAWOAE4LqmYjh6zFUtzpt4JPcQERER6WrSQiFszx42bNjgdRTp5ubPn09OTo7XMUQ6hTaL5yPlnHsZ2HlA83nAg9HtB4EvtGh/yDVaAGSYWSFwNjDHObfTObcLmANMjO5Lc84tcI2/cn3ogGsdzj1EREREupw85zTuWeLKOcf8+fMpKSnxOopIpxC34rkV+c65j6PbnwD50e1iYGOL4zZF2w7Vvukg7Udyj08xs6vNbLGZLa7buzfGryYiIiLScQr9fhbrfc8SR5s2baK2tpb09HSvo4h0Ch1dPDeL9hjHdaDOkd7DOXevc26Mc25MYkJCHJKJiIiIHJ3eGvcscbZkyRJycnKITi0k0uN1dPG8pelR6eh6a7R9M9DyeZDe0bZDtfc+SPuR3ENERESky9G4Z4m3119/nfz8/LYPFOkhOrp4fhpomjH7UuCpFu2XRGfEHgdURh+9ng1MMLPM6ERhE4DZ0X1VZjYuOsv2JQdc63DuISIiItIl6X3PEi/19fW89dZbFBcfdJSjSI8Ut+LZzB4F5gODzGyTmV0B3AScZWargTOjnwGeA9YCa4D7gG8DOOd2Ar8G3oguN0TbiB7zl+g5HwCzou2HdQ8RERGRrqooGOSNV1/1OoZ0Q++++y69evUiMTHR6yginUYgXhd2zl3Yyq4zDnKsA77TynUeAB44SPti4FNva3fO7Tjce4iIiIh0Rb1TU5m5cCHOOY1LlXa1ePFisrOzvY4h0ql4NmGYiIiIiByd1FAIn8Y9Sxy88soremRb5AAqnkVERES6sFyNe5Z2tnPnTjZt2kReXp7XUUQ6FRXPIiIiIl2Yxj1Le4hEIlRWVrJx40bmzJlDTk4OPp9KBZGW4jbmWURERETiryQ1lac17lliVFtby6OPPsq6devYtWsXu3btoqKigpqaGoLBIAkJCSQkJDBkyBCvo4p0OiqeRURERLqwlFAIf2UlGzZsoLS01Os40sm9/PLL/P3vf2fQoEFkZmZSWFhIUlISCQkJ6mkWaYOKZxEREZEuLjcc5u2331bxLG2qrKwkJyeHY445xusoIl2Ofr0kIiIi0sUVatyzxKiiooJQKOR1DJEuScWziIiISBdXkprK0kWLcM55HUU6uV27dpGYmOh1DJEuScWziIiISBeXEgrh37OH9evXex1FOrnKykoSEhK8jiHSJal4FhEREekG9L5niYWKZ5Ejp+JZREREpBsoDAZ545VXvI4hnVxlZaUe2xY5QiqeRURERLqBktRUlr7xhsY9yyFVV1er51nkCKl4FhEREekGmsY9r1u3zuso0kk551Q8ixwFFc/y/9u79zi76vre/69P5pILCQmBADEBAxJrQSuXiAgoioLBWrGttViPUGtNL1idaqvY9qe/aj0HayugUivVFPBUkWop0QMJCAKVI5Cgwx3qFKQECAmEXAZCQpLP+WOviZtxZlYme2bWnj2v5+OxH7P2d90+e8/ae+a9v9+1tiRJahFzdu7kzjvvrLoMNamtW7cC0N7eXnEl0vhkeJYkSWoRczs6WOX3PWsQ9jpLjTE8S5IktQjPe9ZQNm/eTGdnZ9VlSOOW4VmSJKlFTO/spO255zzvWQPq7e01PEsNMDxLkiS1kP0971mD2Lx5s+c7Sw0wPEuSJLWQF3neswaxefNmOjo6qi5DGrcMz5IkSS1k/owZ/OS229i+fXvVpajJbN68mba2tqrLkMYtw7MkSVILmd7ZyewtWzjjV3+VSy++mB07dlRdkprEhg0b7HmWGmB4liRJajFvOfBAXrd9O9/+4he55557qi5HTWLDhg1MmTKl6jKkccvwLEmS1ILmTJvGr0yZwl//+Z8boAXA008/7fc8Sw0wPEuSJLWoI2bP5pgtW/jz976Xf/vOd/z+5wlu06ZNhmepAYZnSZKkFnbIzJm8bdYs/vf//J98+hOfYMuWLVWXpIps3LjRYdtSA/yiN0mSpBY3c/Jkfv3AA7lp+XL+4L77OOVtb2PvmTOZPn06M2bMYMaMGUyfPn3Xfb8LuDXZ8yw1ppJ3xoj4GbAZ2AFsz8xFETEb+BawAPgZ8M7MfDoiArgAeAvwLPC7mfnjYjtnAX9VbPZvMvOSov0Y4GJgKnAV8KHMzMH2McoPV5IkqXLtkyZx8oEH0rNmDbddcAHPT5rEtkmTaj+BrZls3bmT53bsYK/p0zngwAN50YtfzEGHHsrcefM4+uijmTdvXtUPQw3o7e01PEsNqPJjxTdk5pN1988BrsvMcyPinOL+x4DTgIXF7dXAl4FXF0H4k8AiIIHbI2JZEYa/DLwfuJVaeF4MXD3EPiRJkiaEw/bZh8P22WfQ+ZnJlu3b2bh2LRtXr+a+66/n/+7cyXcOO4x//ta3qPVraLzZtm0bO3bscFSB1IBmOuf5dOCSYvoS4O117ZdmzS3ArIiYC7wZuDYz1xeB+VpgcTFv78y8JWtXxbi037YG2ockSZKAiGBaRwdzp0/nZbNnc+zcuZz6ohfx9M9+xgMPPFB1edpDfUO2/fBD2nNVhecEromI2yNiSdF2QGY+XkyvAQ4opucBj9Stu7poG6p99QDtQ+1DkiRJg4gIXgJ874orqi5Fe8gh21LjqgrPJ2bm0dSGZJ8dEa+rn1n0GI/qdykMtY+IWBIRqyJi1XNbt45mGZIkSePCEbNnc913v8vGjRurLkV7YPPmzXR2dlZdhjSuVRKeM/PR4uda4ArgWOCJYsg1xc+1xeKPAgfVrT6/aBuqff4A7Qyxj/71XZSZizJz0RQ/oZMkSWJaRwcLnn+ef/ziF6suRXtg8+bNnu8sNWjMw3NE7BURM/qmgVOBu4FlwFnFYmcBVxbTy4Azo+Y4YGMx9HoFcGpE7BMR+xTbWVHM2xQRxxVX6j6z37YG2ockSZJKHDdnDtcsW8bOnTurLkXD1Nvba3iWGlTFK+gA4IriYgXtwDcyc3lErAQuj4j3AQ8D7yyWv4ra11T1UPuqqvcCZOb6iPg0sLJY7lOZub6Y/mN+/lVVVxc3gHMH2YckSZJKdLa1MRl4+umn2XfffasuR8OwefNm2i7I3hUAACAASURBVNraqi5DGtfGPDxn5oPAKwdofwp44wDtCZw9yLaWAksHaF8FvHx39yFJkqTdM72tjSeeeMLwPM5s3LiRjo6OqsuQxrVm+qoqSZIkNblZ27dz//33V12Ghunpp5/2attSgwzPkiRJ2m1zOzr48c03V12GhmnDhg2GZ6lBhmdJkiTttnkzZnDH7bdTO7NO48WGDRuYMmVK1WVI45rhWZIkSbttRmcnPPssq1evrroUDcOmTZsMz1KDDM+SJEkalv2Be+65p+oyNAybNm1y2LbUIL/sTZIkScMyJ4Kf3HorixcvrrqUCSMzee6559iyZQvPPPMMzzzzDL29vbt+9vb2smnTJjZs2MCmTZvYuHEjmzdvZvPmzbvm2/MsNcbwLEmSpGGZP2MGN916a9VltJxVq1bx9a9/nS1btvDss8+yZcsWtm7dypYtW3j++edpa2ujvb2dzs5OOjs76ejooL29nfb2dtra2ujo6KCzs5PJkyczffp0Zs+ezeTJk5k8eTKdnZ1MmuSgU6kRhmdJkiQNy+wpU9j0xBM8+eST7LffflWX0xIyky9+8YvstddeHHjggbuCcUdHx67piKi6TGlC8+MnSZIkDUtEsP+kSZ73PILuu+8+1q1bxxFHHMGBBx7Ivvvuy8yZM5k2bRodHR0GZ6kJGJ4lSZI0bPvt3Mkdt99edRkt44orruDggw82JEtNzGHbkiRJGrZDZ87kiksv5aYVKzj4kEM45KUv5cWHHca8efOYN28e+++/v+fY7qaNGzdyww03cNppp1VdiqQhGJ4lSZI0bPtOncrvzZ/P5m3bWH/vvTz84x9zVybPtLezcedOOvfZhy8tXcqLXvSiqktteitWrGDOnDleDVtqcoZnSZIk7ZFJEcycPJmZkydzyMyZL5h315NP8qHf/30++6UvcfDBB9Pe7r+dA3nssce49NJLOfbYY6suRVIJ38UkSZI04l6x337w1FN88F3v4tkdO5ix997MOeAAXnvKKZz53vdO+HN7t2/fzte//nWuvPJKDjnkEK9aLo0DhmdJkiSNilfsuy+vAHZm8uzzz7N5zRq+e+GFbNywgQ9++MNVl1epr3zlK1xzzTUcddRR7L///lWXI2k3eBUHSZIkjapJEUzv7GTu9On82gEHsPyyy3jooYeqLqsyN954I9/97nd5/etfb3CWxhHDsyRJksZMZ1sbR0Tw1QsvrLqUSjzyyCOce+65HH/88UyePLnqciQNg+FZkiRJY+pX9tuP7htu4L777qu6lDG1c+dOPvGJT7Bw4ULPcZbGIcOzJEmSxlT7pEm8sqODi77wBTKz6nLGzC233ML69ev5pV/6papLkbQHDM+SJEkac0fsuy8PrlxJd3d31aWMie3bt3PppZeycOHCCX+lcWm8MjxLkiRpzE2K4KjJk/nKeee1dO9zZnLjjTfy7ne/myeffJIFCxZUXZKkPeRXVUmSJKkSL91nH+68915+9KMfcfzxx1ddTkOef/55nnzySZ588knWrVvHunXreOyxx7j//vtZvXo1Rx55JPPmzau6TEkNMDxLkiSpEhHBMVOn8pXzzuO4445j0qTxMyhyx44drFq1iiuuuIK7776b3t5epk6dytSpU5kyZQqdnZ1MnTqVWbNm8YpXvGJcPTZJAzM8S5IkqTKHzJzJHQ89xPXXX8+b3vSmqssZ0ubNm3nsscfo6elh6dKlbN++nQULFnDSSScxbdo0z2WWWpzhWZIkSZWJCF41fTr/dP75nHTSSXR0dFRdEgDXXXcdV1xxBRs3bqS3t5fNmzeTmUyfPp1p06Zx+OGHM3fu3KrLlDSGDM+SJEmq1PwZM+hes4aLly7l95csaYoe3O9///ts2bKFhQsXMnnyZCZPnkxHR0dT1CapGoZnSZIkVe71s2ez4qKL+P6yZUyfPp2p06bVbnvtxbTp05k2fTq/evrpvOQlLxn1WrZv387DDz/My172Mvbbb79R35+k8WFChueIWAxcALQBX83McysuSZIkaUKb3tnJr8+dy9PPPcfzW7bw/I4dPL9zJ9t27mTzzp3csWEDU/faa1TD8yOPPMJHP/pRHn/8cWbNmsXs2bNHbV+Sxp8JF54jog24EDgFWA2sjIhlmXlvtZVJkiRNbJMi2Hfq1AHntU+axIP3309mjsrQ6a1bt/L3f//37L333hx//PG0tbWN+D4kjW8TLjwDxwI9mfkgQERcBpwOGJ4lSZKa1MEzZrDspps450//lBcfeih7zZzJtGnTmDJlyq6fU6ZMecHXRfX9nDx58pCBe+3atSxZsoQpU6Zw4okn+rVSkgYUmVl1DWMqIt4BLM7M3y/uvwd4dWZ+oG6ZJcASgKnwyrmwtQ1IaA/YXkXdmpg85jTWPOY01jzmNBw7gU11nT/581sm5M6fT7Oz+JmQ22DzDngM2A94coBNz5w1a9ZBe++9t8eiRlRmtkeEx1WTW7t27eQtW7aUXup/IvY8l8rMi4CL+rdHxKrMXFRBSZqgPOY01jzmNNY85jSWPN401jzmWstEHJPyKHBQ3f35RZskSZIkSQOaiOF5JbAwIg6JiE7gDGBZxTVJkiRJkprYhBu2nZnbI+IDwApqX1W1NDPv2c3Vf2EotzTKPOY01jzmNNY85jSWPN401jzmWsiEu2CYJEmSJEnDNRGHbUuSJEmSNCyGZ0mSJEmSShiehyEijoyIWyKiOyJWRcSxVdek1hcRfxIR90fEPRHxt1XXo4khIj4SERkR+1Vdi1pbRHyueI+7MyKuiIhZVdek1hQRiyPigYjoiYhzqq5HrS0iDoqIH0TEvcX/cB+quiY1zvA8PH8L/HVmHgl8orgvjZqIeANwOvDKzDwC+LuKS9IEEBEHAacC/111LZoQrgVenpm/Avwn8PGK61ELiog24ELgNOBw4F0RcXi1VanFbQc+kpmHA8cBZ3vMjX+G5+FJYO9ieibwWIW1aGL4I+DczNwKkJlrK65HE8N5wEepvedJoyozr8nM7cXdW4D5VdajlnUs0JOZD2bmNuAyah9OS6MiMx/PzB8X05uB+4B51ValRhmeh6cL+FxEPEKtB9BPxzXaXgq8NiJujYgbI+JVVRek1hYRpwOPZuYdVdeiCen3gKurLkItaR7wSN391RhkNEYiYgFwFHBrtZWoURPue57LRMT3gQMHmPWXwBuBP83M70TEO4GvAW8ay/rUekqOuXZgNrXhPq8CLo+IQ9PvmFMDSo65v6A2ZFsaMUMdc5l5ZbHMX1Ib5vgvY1mbJI2miJgOfAfoysxNVdejxvg9z8MQERuBWZmZERHAxszcu2w9aU9FxHLgs5n5g+L+fwHHZea6aitTK4qIVwDXAc8WTfOpnZ5ybGauqawwtbyI+F3gD4A3ZuazJYtLwxYRrwH+/8x8c3H/4wCZ+b8qLUwtLSI6gO8BKzLz81XXo8Y5bHt4HgNOKqZPBn5aYS2aGP4deANARLwU6ASerLQitazMvCsz98/MBZm5gNqwxqMNzhpNEbGY2jn2bzM4axStBBZGxCER0QmcASyruCa1sKKj7WvAfQbn1uGw7eF5P3BBRLQDzwFLKq5HrW8psDQi7ga2AWc5ZFtSi/kSMBm4tva/Jrdk5h9WW5JaTWZuj4gPACuANmBpZt5TcVlqbScA7wHuiojuou0vMvOqCmtSgxy2LUmSJElSCYdtS5IkSZJUwvAsSZIkSVIJw7MkSZIkSSUMz5IkSZIklTA8S5IkSZJUwvAsSdIQImJHRHRHxD0RcUdEfCQiJhXzFkXEF4ZYd0FE/M7YVfuCfc+KiD8epW2/LSLOGWRe7x5s76sRcfgI1DU3Ir5XTJ8QEXdGxKqIWFi0zYqIa/p+f0Xb9yNin0b3LUlqfX5VlSRJQ4iI3sycXkzvD3wDuDkzP7kb674e+LPMfOvoVjngvhcA38vMl4/xfnc9X7u5fFtm7hihfX8O+GFmXhkR/wZ8EFgA/HpmfiQi/o7ac3JD3TpnAfMz8zMjUYMkqXXZ8yxJ0m7KzLXAEuADUfP6up7Ok4oe6u6I+ElEzADOBV5btP1p0RP9HxHx4+J2fLHu6yPihoj4dkTcHxH/EhFRzHtVRPzfotf7toiYERFtEfG5iFhZ9K7+wQDlngu8pNj354p6PxcRd0fEXRHx2/1XKOq7PyIujoj/LOp4U0TcHBE/jYhji+V+NyK+VEwfEhE/Krb5N3Xben1E3BQR/yciHoiIf6zrse+NiL+PiDuA1xSPfVExb3Hx3NwREdcVbXtFxNLi8f8kIk4f5Ff0m8DyYvp5YFpxez4iXgIcVB+cC8uAdw36S5ckqdBedQGSJI0nmflgRLQB+/eb9WfA2Zl5c0RMB54DzqGu5zkipgGnZOZzxVDibwKLivWPAo4AHgNuBk6IiNuAbwG/nZkrI2JvYAvwPmBjZr4qIiYDN0fENZn5UF095wAvz8wji33/JnAk8EpgP2BlRNyUmY/3exyHAb8F/B6wEvgd4ETgbcBfAG/vt/wFwJcz89KIOLvfvGOBw4GHqYXa3wC+DewF3JqZHylqo/g5B/gn4HWZ+VBEzC6285fA9Zn5exExC7gtIr6fmc/07SgiDgGezsytRdP/Ai4tnq/3AH8H/FW/+sjMpyNickTsm5lP9Z8vSVIfe54lSRoZNwOfj4gPArMyc/sAy3QA/xQRdwH/Si1Y9rktM1dn5k6gm9pw418CHs/MlQCZuanY7qnAmRHRDdwK7AssLKnvROCbmbkjM58AbgReNcByD2XmXUUd9wDXZe0cr7uKmvo7gdqHAABf7zfvtsx8sBiW/c2iBoAdwHcG2NZxwE19HwJk5vqi/VTgnOLx3gBMAQ7ut+5cYF3fnczszszjMvMNwKHA40BExLci4n9HxAF1664FXjRAPZIk7WLPsyRJwxARh1ILf2uBX+5rz8xzI+L/AG+h1hP85gFW/1PgCWq9v5Oo9U732Vo3vYOh/0YH8CeZuWKPHsTQ6uvYWXd/5xA1DXYBlf7tffefG+Z5zgH8ZmY+MMQyW6iF6heuWOvW/ivgDOCLwEepfQjwQWo92hTrbRlGPZKkCcieZ0mSdlMxrPgfgS9lvytuRsRLih7bz1Ib7vwyYDMwo26xmdR6kndSG0rcVrLLB4C5EfGqYh8zIqIdWAH8UUR0FO0vjYi9+q3bf9//Afx2cb70HOB1wG27+9iHcDO1YArw7n7zji3OiZ4E/Dbww5Jt3QK8rhiCTd2w7RXAn9SdB37UAOv+JwP3jJ8JXFX0Yk+j9iHAzmK6L1wfCPyspDZJ0gRnz7MkSUObWgwX7gC2Uxua/PkBluuKiDdQC2b3AFcX0zuKC2NdDPwD8J2IOJPaOcDPDLCdXTJzW3Fhry9GxFRqvaNvAr5KLSj+uAh/6+h3LnJmPlVc6OvuopaPAq8B7qDWA/zRzFwzzOdiIB8CvhERHwOu7DdvJfAlaudR/wC4YqgNZea6iFgC/FsRuNcCpwCfBs4H7izaHwLe2m/dZyLivyLisMzsgV3nmP8utWHfUPu9XQVso3YuN8AxwC2DDLOXJGkXv6pKkiSNuKjga7oi4teBYzLzFy4MNsQ6FwDLMvO60atMktQK7HmWJEktITOviIh9h7na3QZnSdLusOdZkiRJkqQSXjBMkiRJkqQShmdJkiRJkkoYniVJkiRJKmF4liRJkiSphOFZkiRJkqQShmdJkiRJkkoYniVJkiRJKmF4liRJkiSphOFZkiRJkqQShmdJkiRJkkoYniVJkiRJKmF4liRJkiSphOFZkiRJkqQShmdJkiRJkkoYniVJkiRJKmF4liRJkiSphOFZkiRJkqQS7VUX0Oz222+/XLBgQdVlSJIkSZJGwe233/5kZs4pW87wXGLBggWsWrWq6jIkSZIkSaMgIh7eneUcti1JkiRJUgnDsyRJkiRJJQzPkiRJkiSVMDxLkiRJklTC8CxJkiRJUgnDsyRJkiRJJQzPkiRJkiSVMDxLkiRJklTC8CxJktQElnd1sbyrq+oy1AS6urro8liQmk571QVIkiQJ1nR3V12CmkS3x4LUlOx5liRJkiSpRCXhOSJ+FhF3RUR3RKwq2mZHxLUR8dPi5z5Fe0TEFyKiJyLujIij67ZzVrH8TyPirLr2Y4rt9xTrxlD7kCRJkiRpKFX2PL8hM4/MzEXF/XOA6zJzIXBdcR/gNGBhcVsCfBlqQRj4JPBq4Fjgk3Vh+MvA++vWW1yyD0mSJEmSBtVMw7ZPBy4ppi8B3l7XfmnW3ALMioi5wJuBazNzfWY+DVwLLC7m7Z2Zt2RmApf229ZA+5AkSZIkaVBVhecEromI2yNiSdF2QGY+XkyvAQ4opucBj9Stu7poG6p99QDtQ+3jBSJiSUSsiohV69atG/aDkyRJkiS1lqqutn1iZj4aEfsD10bE/fUzMzMjIkezgKH2kZkXARcBLFq0aFTrkCRJkiQ1v0p6njPz0eLnWuAKaucsP1EMuab4ubZY/FHgoLrV5xdtQ7XPH6CdIfYhSZIkSdKgxjw8R8ReETGjbxo4FbgbWAb0XTH7LODKYnoZcGZx1e3jgI3F0OsVwKkRsU9xobBTgRXFvE0RcVxxle0z+21roH1IkiRJkjSoKoZtHwBcUXx7VDvwjcxcHhErgcsj4n3Aw8A7i+WvAt4C9ADPAu8FyMz1EfFpYGWx3Kcyc30x/cfAxcBU4OriBnDuIPuQJEmSJGlQYx6eM/NB4JUDtD8FvHGA9gTOHmRbS4GlA7SvAl6+u/uQJEmSJGkozfRVVZIkSZIkNSXDsyRJkiRJJQzPkiRJkiSVMDxLkiRJklTC8CxJkiRJUgnDsyRJkiRJJQzPkiRJkiSVMDxLkiRJklTC8CxJkiRJUgnDsyRJkiRJJQzPkiRJkiSVMDxLkiRJklTC8CxJkiRJUgnDsyRJkiRJJQzPkiRJkiSVMDxLkiRJklTC8CxJkiRJUgnDsyRJkiRJJQzPkiRJkiSVMDxLkiRJklTC8CxJkiRJUgnDsyRJkiRJJQzPkiRJkiSVMDxLkiRJklTC8CxJkiRJUgnDsyRJkiRJJQzPkiRJkiSVMDxLkiRJklTC8CxJkiRJUgnDsyRJkiRJJQzPkiRJkiSVMDxLkiRJklTC8CxJkiRJUgnDsyRJkiRJJQzPkiRJkiSVMDxLkiRJklTC8CxJkiRJUonKwnNEtEXETyLie8X9QyLi1ojoiYhvRURn0T65uN9TzF9Qt42PF+0PRMSb69oXF209EXFOXfuA+5AkSZIkaShV9jx/CLiv7v5ngfMy8zDgaeB9Rfv7gKeL9vOK5YiIw4EzgCOAxcA/FIG8DbgQOA04HHhXsexQ+5AkSZIkaVCVhOeImA/8KvDV4n4AJwPfLha5BHh7MX16cZ9i/huL5U8HLsvMrZn5ENADHFvcejLzwczcBlwGnF6yD0mSJEmSBlVVz/P5wEeBncX9fYENmbm9uL8amFdMzwMeASjmbyyW39Xeb53B2ofaxwtExJKIWBURq9atW7enj1GSJEmS1CLGPDxHxFuBtZl5+1jve3dl5kWZuSgzF82ZM6fqciRJkiRJFWuvYJ8nAG+LiLcAU4C9gQuAWRHRXvQMzwceLZZ/FDgIWB0R7cBM4Km69j716wzU/tQQ+5AkSZIkaVBj3vOcmR/PzPmZuYDaBb+uz8x3Az8A3lEsdhZwZTG9rLhPMf/6zMyi/YziatyHAAuB24CVwMLiytqdxT6WFesMtg9JkiRJkgbVTN/z/DHgwxHRQ+385K8V7V8D9i3aPwycA5CZ9wCXA/cCy4GzM3NH0av8AWAFtat5X14sO9Q+JEmSJEkaVBXDtnfJzBuAG4rpB6ldKbv/Ms8BvzXI+p8BPjNA+1XAVQO0D7gPSZIkSZKG0kw9z5IkSZIkNSXDsyRJkiRJJQzPkiRJkiSVMDxLkiRJklTC8CxJkiRJUgnDsyRJkiRJJQzPkiRJkiSVMDxLkiRJklTC8CxJkiRJUgnDsyRJkiRJJQzPkiRJkiSVMDxLkiRJklTC8CxJkiRJUgnDsyRJkiRJJQzPkiRJkiSVMDxLkiRJklTC8CxJkiRJUgnDsyRJkiRJJQzPkiRJkiSVMDxLkiRJklTC8CxJkiRJUgnDsyRJkiRJJQzPkiRJkiSVMDxLkiRJklTC8CxJkiRJUgnDsyRJkiRJJQzPkiRJkiSVMDxLkiRJmvC6urro6uqqugw1sfaqC5AkSZKkqnV3d1ddgpqcPc+SJEmSJJUwPEuSJEmSVMLwLEmSJElSCcOzJEmSJEklDM+SJEmSJJUwPEuSJEmSVMLwLEmSJElSCcOzJEmSJEklxjw8R8SUiLgtIu6IiHsi4q+L9kMi4taI6ImIb0VEZ9E+ubjfU8xfULetjxftD0TEm+vaFxdtPRFxTl37gPuQJEmSJGkoVfQ8bwVOzsxXAkcCiyPiOOCzwHmZeRjwNPC+Yvn3AU8X7ecVyxERhwNnAEcAi4F/iIi2iGgDLgROAw4H3lUsyxD7kCRJkiRpUA2H54h4cUS8qZieGhEzhlo+a3qLux3FLYGTgW8X7ZcAby+mTy/uU8x/Y0RE0X5ZZm7NzIeAHuDY4taTmQ9m5jbgMuD0Yp3B9iFJkiRJ0qAaCs8R8X5qYfQrRdN84N93Y722iOgG1gLXAv8FbMjM7cUiq4F5xfQ84BGAYv5GYN/69n7rDNa+7xD7kCRJkiRpUI32PJ8NnABsAsjMnwL7l62UmTsy80hqYftY4GUN1jGiImJJRKyKiFXr1q2ruhxJkiRJUsUaDc9bi6HRAEREO7Uh2LslMzcAPwBeA8wq1odaqH60mH4UOKhu+zOBp+rb+60zWPtTQ+yjf10XZeaizFw0Z86c3X04kiRJkqQW1Wh4vjEi/gKYGhGnAP8KfHeoFSJiTkTMKqanAqcA91EL0e8oFjsLuLKYXlbcp5h/fWZm0X5GcTXuQ4CFwG3ASmBhcWXtTmoXFVtWrDPYPiRJkiRJGlR7+SJDOofaFavvAv4AuAr4ask6c4FLiqtiTwIuz8zvRcS9wGUR8TfAT4CvFct/Dfh6RPQA66mFYTLznoi4HLgX2A6cnZk7ACLiA8AKoA1Ympn3FNv62CD7kCRJkiRpUA2F58zcCfxTcdvdde4Ejhqg/UFq5z/3b38O+K1BtvUZ4DMDtF9FLcjv1j4kSZIkSRpKo1fbfmtE/CQi1kfEpojYHBGbRqo4SZIkSZKaQaPDts8HfgO4qzinWJIkSZKkltPoBcMeAe42OEuSJEmSWlmjPc8fBa6KiBuBrX2Nmfn5BrcrSZIkSVLTaDQ8fwboBaYAnY2XI0mSJElS82k0PL8oM18+IpVIkiRJktSkGj3n+aqIOHVEKpEkSZIkqUk1Gp7/CFgeEVv8qipJkiRJUqtqaNh2Zs4YqUIkSZIkSWpWDYXniHjdQO2ZeVMj25UkSZIkqZk0esGwP6+bngIcC9wOnNzgdiVJkiRJahqNDtv+tfr7EXEQcH5DFUmSJEkalq6uLgDOP99/xaXR0mjPc3+rgV8e4W1KkiRJGkJ3d3fVJUgtr9Fznr8IZHF3EnAk8ONGi5IkSZIkqZk02vO8qm56O/DNzLy5wW1KkqQxsrwY6rnYoZ6SNG45bH9sNHrO8yUjVYgkSRp7axzqKUnjnsP2x8YeheeIuIufD9d+wSwgM/NXGqpKkiRJkqQmsqc9z28d0SokSZIkSWpiexSeM/PhvumIOAB4VXH3tsxcOxKFSZIkSZLULCY1snJEvBO4Dfgt4J3ArRHxjpEoTJIkSZKkZtHo1bb/EnhVX29zRMwBvg98u9HCJEmSJElqFg31PAOT+g3TfmoEtilJkiRJUlNptOd5eUSsAL5Z3P9t4KoGtylJkiRJUlPZ06+quhD4Rmb+eUT8BnBiMeuizLxixKqTJEmSJKkJ7GnP838CfxcRc4HLga9n5k9GrixJkiRJkprHHp2fnJkXZOZrgJOonee8NCLuj4hPRsRLR7RCSZIkSZIq1tDFvTLz4cz8bGYeBbwLeDtw34hUJkmSJElSk2j0e57bI+LXIuJfgKuBB4DfGJHKJEmSJElqEnt6wbBTqPU0vwW4DbgMWJKZz4xgbZIkSZIkNYU9vWDYx4FvAB/JzKdHsB5JkiRJkprOHoXnzDx5pAuRJEmSJKlZNXTOsyRJkiRJE4HhWZIkSZKkEoZnSZIkSZJKGJ4lSZIkSSpheJYkSZIkqYThWZIkSZKkEmMeniPioIj4QUTcGxH3RMSHivbZEXFtRPy0+LlP0R4R8YWI6ImIOyPi6LptnVUs/9OIOKuu/ZiIuKtY5wsREUPtQ5IkSZKkoVTR87wd+EhmHg4cB5wdEYcD5wDXZeZC4LriPsBpwMLitgT4MtSCMPBJ4NXAscAn68Lwl4H31623uGgfbB+SJEmSJA1qzMNzZj6emT8upjcD9wHzgNOBS4rFLgHeXkyfDlyaNbcAsyJiLvBm4NrMXJ+ZTwPXAouLeXtn5i2ZmcCl/bY10D4kSZIkSRpUpec8R8QC4CjgVuCAzHy8mLUGOKCYngc8Urfa6qJtqPbVA7QzxD4kSZIkSRpUZeE5IqYD3wG6MnNT/byixzhHc/9D7SMilkTEqohYtW7dutEsQ5IkSZI0DlQSniOig1pw/pfM/Lei+YliyDXFz7VF+6PAQXWrzy/ahmqfP0D7UPt4gcy8KDMXZeaiOXPm7NmDlCRJkiS1jCquth3A14D7MvPzdbOWAX1XzD4LuLKu/cziqtvHARuLodcrgFMjYp/iQmGnAiuKeZsi4rhiX2f229ZA+5AkSZIkaVDtFezzBOA9wF0R0V20/QVwLnB5RLwPeBh4ZzHvKuAtQA/wLPBegMxcHxGfBlYWy30qM9cX038MXAxMBa4ubgyxD0mSJEmSBjXm4TkzfwjEILPfOMDyCZw9yLaWAksHaF8FvHyA9qcG2ockSZIkSUOp9GrbkiRJCDG95gAAES5JREFUkiSNB4ZnSU1leVcXy7u6qi5DkiRJeoEqznmWpEGt6e4uX0iSJEkaY/Y8S5IkSZJUwvAsSZIkSVIJw7MkSZIkSSUMz5IkSZIklTA8S5IkSZJUwvAsSZIkSVIJw7MkSZIkSSUMz5IkSZIklTA8S5IkSZJUwvAsSZIkSVIJw7MkSZIkSSUMz5IkSZIklTA8S5ImhOVdXSzv6qq6DKll+JqSWk9XVxddvq4H1V51AZIkjYU13d1VlyA1nb7wu/j884e9rq8pqfV0+7oekuFZkiRpgjIAS9Luc9i2JEmSJEklDM+SJEmSJJUwPEuSJEmSVMLwLEmSJElSCcOzJEmSJEklDM/SCPC7LiVJkqTW5ldVSSPAr/qQJEmSWps9z5IkSZIklTA8S5IkSZJUwvAsSZIkSVIJw7MkSZIkSSUMz5IkSZIklTA8S5IkSWpaXV1ddPmVoGoCflWVJEmSpKbV7VeCqknY8yxJkiRJLcYe+5Fnz7MkSZIktRh77EeePc+SJEmSJJUwPEuSJEmSVMLwLEmSJElSiUrCc0QsjYi1EXF3XdvsiLg2In5a/NynaI+I+EJE9ETEnRFxdN06ZxXL/zQizqprPyYi7irW+UJExFD7kCQ1l+VdXSz3IieSJKmJVNXzfDGwuF/bOcB1mbkQuK64D3AasLC4LQG+DLUgDHwSeDVwLPDJujD8ZeD9destLtmHpAnGcNbc1nR3s8YLnUiS1PQm0lW9K7nadmbeFBEL+jWfDry+mL4EuAH4WNF+aWYmcEtEzIqIucWy12bmeoCIuBZYHBE3AHtn5i1F+6XA24Grh9iHNG70Bb7F559fcSXjm8FsaB5nkiRpd0ykq3o301dVHZCZjxfTa4ADiul5wCN1y60u2oZqXz1A+1D7kMYNQ5/GgseZJEnSCzXlBcOKXuasah8RsSQiVkXEqnXr1o1mGZIkSZKkcaCZwvMTxXBsip9ri/ZHgYPqlptftA3VPn+A9qH28QKZeVFmLsrMRXPmzGnoQUmSJEmSxr9mCs/LgL4rZp8FXFnXfmZx1e3jgI3F0OsVwKkRsU9xobBTgRXFvE0RcVxxle0z+21roH1IkqQm44X9JEnNpJJzniPim9Qu3LVfRKymdtXsc4HLI+J9wMPAO4vFrwLeAvQAzwLvBcjM9RHxaWBlsdyn+i4eBvwxtSt6T6V2obCri/bB9iFJkpqM595LkppJVVfbftcgs944wLIJnD3IdpYCSwdoXwW8fID2pwbahyRJkiRJQ2mmYduSJEmSJDUlw7MkjSLP2ZQkSWoNzfQ9z5LUcjxnU5IkqTXY8yxJkiRJUgnDsyRJkjRMXV1ddHlajjShOGxbkiRJGqZuT8uRJhx7niVJkiRJKmF4liRJkiSphOFZkiRJkqQShmdJkiRJkkoYntVUlnd1sdwrV0qSJElqMl5tW01ljVeulCRJktSE7HmWJBz1MFZ8niVJ0nhlz7Mk4aiHseLzLEmSxit7niVJkiRJKmF4liRJGkc8/UGSquGwbUmSpHGk0dMf+oL34vPPH4lyJoyu4nk73+dNmrDseZakJlZlD9P6nh57t6QWtKa72+sP7IHu7m66fd6kCc2eZ0lqYlX+g7utt9d/sCVJkgr2PEuSJElqKkcddRRHHXVU1WVIL2DPsySNEc8zlCRp9zz00ENVlzBqPH9+/DI8S9IYcQi0JKmV7EkINDjiufPjmOG5RdnDJWki8T1PksbenoRAg6PGM8Nzi7KHS9JE4nuexiM/9JGk8cXwLElSizCMjS/N8KHP+p6eqktoag4xllTP8CxJUosYiTBmAJ9YtvX2Vl1CU3OIsaR6hmdJ0m4zWLW+ZugNVWsaq/cPe4sljRbDsyRptxmsJO2psXr/sLdY0miZVHUBkiRJkiQ1O8OzJEmjbHlX164hq62g1R6P1Gq6urp2DV8fieUk1ThsW5KkUdZqw91b7fFo/PM85xfa3aHrDnGXhsfwLEmSNAEt7+pi+5YttE+dWnUpDRvLEGhPrTRxGZ6lCajZrpjcbPWMRz6HkoZrTXc3O3fsqLqMccfeWo0UR0yMP4ZnaQJqtiGXzVbPeNTKz6EfDEgvtL6np+oSJI0AP4gZfwzPUhMpCwnLu7pY39PD7MMOG8uypErd++1vA6MTng3mGo+29faO+DaHei0M53UyXl5TQ/X41Q/L7unp4bAx/JvrkHCpuRme9QvGyx++VlTWe7imu3tU/mmSoHlf+6N5zLdyj72aU7O+zoZ6LQw0b7APc8teU+t7elje1TWij7+rq2vYIXeoHr/u7m56it793jH+m9vsPZEOM9ZEZ3ge50bjj7D/TEoTR/17iK99afQ18job7OvBxiKQ9w+9e/ph7rbe3hF/r+nu7t4VcvckSA9krEPzcDUSYgdbd3e22ezhvgpj8YGCH1o0jwkZniNiMXAB0AZ8NTPPrbikPeY/u2pUs/aCaGz4HjI6PWETme8po2ew12vZ67jR38n6nh6eXbduxN4vRvM1Vx+k+xvrADKa+2skxA62rsF4z4zF8zaefzetFvwnXHiOiDbgQuAUYDWwMiKWZea91VamseQ/dz+3u/8M+ZwNz2A9RBNRsx87o9ETNlzN/hwNR9XPpX5Ro7+Tbb297Hj++V+4UNn2LVsGvHhZWTgezmuu7LXR1dXFqlWr2LJly66h1lu2bHnBfKj94/7t4voJjfwT37eP3enZbiTw9HhRuIY1Q2irr6HRenp6eujq6hp3IXQ8B/+BTLjwDBwL9GTmgwARcRlwOmB4nkD85274fM6Gx+fr53wuyvkcqell/sIw7Z07dgw4dHskP5Aq2053dzfPPfccO3fu3NXjvKPu67fq/3EfiaHYYzWcu9mHjY8HzRDa6mtotJ7e3t6meEwTXWRm1TWMqYh4B7A4M3+/uP8e4NWZ+YGBll+0aFGuWrVqLEscluuPPoa9nn+eA4888gXtfX9s+rfvjkbWbdRY7Xuk9zNS2yvbzprubrY900vnXtMb2lf9fna39tH83exJPSO5z4Huj9T2++zpYxvt389gz/1gx1qjj2FPHs9/3/xDAA4+4cTdWn44y4zUa6rM7ry2h5o/UvvZnXX29LlspIY92c94NdTrYXePk23P1IJV32tiuMdX/fvTc5s2Mqmtjc69pg+6jf+++Yfs3L6DSe1tL9hn37r1r82hXlN984Ddfs2t6e7mmY4OTv7x7QPOv/DoY5jx1JNkQnt7G1ALzzP3nsmRRx65K2wceeSR/LB4LznxhBNf0N5fd3c3vc/8PLxO32v6rm31tU8vnq/BttG3naHmD7VO335OHOR9b6Bt17eVPb6Bltudevsv08hjLFtnd39fje5nNNcpe677lO2v79jrOxZ3t45meQ5Wd3Rw9iCv4WYREbdn5qLS5QzPvxieI2IJsKS4+0vAA2Ne6MS2H/Bk1UWoUh4DAo8D1XgcyGNA4HGgmtE6Dl6cmXPKFpqIw7YfBQ6quz+/aNslMy8CLhrLovRzEbFqdz75UevyGBB4HKjG40AeAwKPA9VUfRxMqmrHFVoJLIyIQyKiEzgDWFZxTZIkSZKkJjbhep4zc3tEfABYQe2rqpZm5j0VlyVJkiRJamITLjwDZOZVwFVV16FBOWReHgMCjwPVeBzIY0DgcaCaSo+DCXfBMEmSJEmShmsinvMsSZIkSdKwGJ41qiJicUQ8EBE9EXHOAPM/HBH3RsSdEXFdRLy4bt6OiOgubsvq2i+OiIfq5rXOF4C2qAaPg4Mj4pqIuK9YZkHRfkhE3Fps81vFBQDVpEbpGPC9YJzZ0+MgIt5Q93vujojnIuLtxTzfC8aZUToOfD8YRxr8m/C3EXFP8TfhCxERRfsxEXFXsc1d7Wpeo3Qc3FBss++9YP8RLTozvXkblRu1C7L9F3Ao0AncARzeb5k3ANOK6T8CvlU3r3eQ7V4MvKPqx+dtzI6DG4BTiunpdctdDpxRTP8j8EdVP1ZvY34M+F4wjm6NHgd1y8wG1vteMD5vo3gc+H4wTm6NHAPA8cDNxTbagB8Bry/m3QYcBwRwNXBa1Y/VWyXHwQ3AotGq255njaZjgZ7MfDAztwGXAafXL5CZP8jMZ4u7t1D73m21lj0+DiLicKA9M68tluvNzGeLTxdPBr5drHMJ8PbRfyjaQyN+DIxd6RpBI/U34R3A1b4XjFsjfhyMarUaDY0cAwlMoRa2JgMdwBMRMRfYOzNvyVqCuhTfC5rdiB8HY1G04VmjaR7wSN391UXbYN5H7ZPCPlMiYlVE3NI3LKvOZ4ohHOdFxOQRqlejo5Hj4KXAhoj4t4j4SUR8LiLagH2BDZm5fTe3qWqNxjHQx/eC8aPRvwl9zgC+WUz7XjD+jMZx0Mf3g/Fhj4+BzPwR8APg8eK2IjPvK9ZfPYxtqnqjcRz0+ediyPb/N9LD9w3PagoR8T+ARcDn6ppfnJmLgN8Bzo+IlxTtHwdeBryK2rCtj41lrRo9AxwH7cBrgT+j9vs+FPjdSorTmBjmMeB7QYsa5G8CRe/SK4AVVdSlsTXM48D3gxbU/xiIiMOAX6bWAzkPODkiXltdhRoLwzwO3p2Zr6D2v8NrgfeMZC2GZ42mR4GD6u7PL9peICLeBPwl8LbM3NrXnpmPFj8fpHb+wlHF/cezZivwz9SGfah5NXIcrAa6iyE924F/B44GngJmRUT7UNtU0xiNY8D3gvGnob8JhXcCV2Tm88V93wvGn9E4Dnw/GF8aOQZ+HbilOIWnl1pP5GuK9euH9/te0PxG4ziozw+bgW8wwu8FhmeNppXAwuJKqJ3Uhlgtq18gIo4CvkLtBbG2rn2fviFXEbEfcAJwb3F/bvEzqJ3PcvcYPBbtuT0+Dop1Z0XEnOL+ycC9xflMP6B2zhvAWcCVo/gY1JgRPwaKdXwvGF8aOQ76vIu6obq+F4xLI34cFOv4fjB+NHIM/DdwUkS0R0QHcBJwX2Y+DmyKiOOKY+BMfC9odiN+HBT39yvW7QDeygi/F0Tt7440OiLiLcD51K6EtzQzPxMRnwJWZeayiPg+taFXjxer/Hdmvi0ijqf2YtlJ7UOe8zPza8U2rwfmULuaYjfwh8WnTmpSe3ocFOueAvw9td/37cCSzNwWEYdSu7jEbOAnwP8YoHdCTWKUjgHfC8aZBo+DBdSurnpQZu6s26bvBePMKB0Hvh+MIw38f9gG/APwOmoXjVqemR8utrmI2lXXp1LrifyTNOg0tZE+DiJiL+AmahcQawO+D3w4M3eMWM0eU5IkSZIkDc1h25IkSZIklTA8S5IkSZJUwvAsSZIkSVIJw7MkSZIkSSUMz5IkSZIklTA8S5LU4iJiR0R0R8TdEfGvETFtkOWuiohZY12fJEnjgV9VJUlSi4uI3sycXkz/C3B7Zn6+bn5Q+59g52DbkCRporPnWZKkieU/gMMiYkFEPBARlwJ3AwdFxM8iYj+AiDgzIu6MiDsi4utF25yI+E5ErCxuJ1T4OCRJGlPtVRcgSZLGRkS0A6cBy4umhcBZmXlLMb9vuSOAvwKOz8wnI2J2sfwFwHmZ+cOIOBhYAfzyGD4ESZIqY3iWJKn1TY2I7mL6P4CvAS8CHu4Lzv2cDPxrZj4JkJnri/Y3AYf3hWxg74iYnpm9o1e6JEnNwfAsSVLr25KZR9Y3FAH4mWFuZxJwXGY+N1KFSZI0XnjOsyRJ6u964LciYl+AumHb1wB/0rdQRBw5wLqSJLUkw7MkSXqBzLwH+AxwY0TcAfRdmfuDwKLiQmL3An9YVY2SJI01v6pKkiRJkqQS9jxLkiRJklTC8CxJkiRJUgnDsyRJkiRJJQzPkiRJkiSVMDxLkiRJklTC8CxJkiRJUgnDsyRJkiRJJQzPkiRJkiSV+H+LPVxQTfrEnAAAAABJRU5ErkJggg==\n", 257 | "text/plain": [ 258 | "
" 259 | ] 260 | }, 261 | "metadata": { 262 | "needs_background": "light" 263 | }, 264 | "output_type": "display_data" 265 | } 266 | ], 267 | "source": [ 268 | "plot_order_book(ob_sub)" 269 | ] 270 | }, 271 | { 272 | "cell_type": "code", 273 | "execution_count": 534, 274 | "metadata": {}, 275 | "outputs": [ 276 | { 277 | "data": { 278 | "image/png": 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i+yUWiTAmkciqbnlBQb+f35JM0tzY2O/7RERERERyQXOeRWRIikciSp5FREREZMhQ8iwiQ1IsEqG5qSnsMEREREREAA3bFpEhKhaJ0KLkWURERGTUcXfa29txd1paWkilUrg7TU1NbN68mc2bN7N9+3ba2tpIJpM0NzeTTqdJpVKddZPJZOf7dDoNQDqdpq6ujubm5s620uk0iURiWjZxKXkWkSEpHonQ0uUHm4iIiIiMDtdccw1333030WiUaDTauTNLLBajuLiY/Px8CgsLiUQiRCIRYrEYts8OLtFodK9yyOwGU1lZSSKR2Gunl2XLlmW1QI+SZxEZkmKRCNt37mTNmjUAxONxysvLqaioCDkyEREREcmlTZs2sXjxYg4++OCwQ9mLkmcRGZLK8vN5cc0a/v388zu3uGrKz+fuhx8mkeUq3yIiIiIy/OzevZvJkyeHHca75GzBMDObYmaPm9laM3vJzL4ZlF9uZlvM7PngOKXLPZea2Xoze8XMPtyl/OSgbL2ZXdKlfLqZPROU32FmiaA8L3i/Prg+ra82RGRoGZufz2mVlZxaXs5p5eWcXl5OQVMT69atCzs0EREREcmhPXv2kJ+fH3YY75LL1baTwL+4+2zgWODrZjY7uHa1uy8IjqUAwbUzgDnAycAvzCxqZlHgWuAjwGzgzC7P+Y/gWTOAXcC5Qfm5wK6g/OqgXo9t5O5bICIDaUIqxfPPPRd2GCIiIiKSQ3V1daMreXb3re7+XHBeD7wM9Nb3fhpwu7u3uvsbwHrgmOBY7+4b3L0NuB04zTIzvD8I3BXcfzNwepdn3Ryc3wWcGNTvqQ0RGQYmFxTwzJNPhh2GiIiIiORIMpmkpaVlSE7TG5Q5z8Gw6SOBZ4AlwAVmdhawgkzv9C4yifXyLrdt5p1ke9M+5YuAcmC3uye7qT+54x53T5rZnqB+b22IyBA3ubiYP61cyRc+9jFisRixWIxoPE48WEmRYNXEaYcdlvm00myvlRS7M2bsWA4++GDGjBnD3LlzicW0FISIiIhIWOrr69+1GvZQkfO/Es2sGLgb+Ja715nZdcAPyaz/80PgJ8A5uY6jP8zsfOB8gPGFhSFHIyId4tEon62qoqW+nlQ6Tdq984DMD5W0O2+/8AK441k8syWdZlk8zq62Nk7/2tc458tfzunXICIiIjJabNiwgdWrV5NOp/GOv9fccXfS6TTt7e3U1dXh7tTX11NXV0dNTU3IUfcsp8mzmcXJJM63uvs9AO6+vcv1XwEPBG+3AFO63H5QUEYP5TXAWDOLBb3PXet3PGuzmcWA0qB+b210cvcbgBsAppaVZfP3t4gMkuJEguI+6kwrLe33c2ubm/njbbfx+bPOGpLDhERERESGm7vvvpulS5cyderUzrKuPcpmRiKRIBKJEI/HSSQSlJaW8oEPfCCMcPuUs+Q5mGN8I/Cyu1/VpbzK3bcGbz8GrAnO7wNuM7OrgEnATOAfgAEzzWw6mUT3DOCz7u5m9jjwSTLzoM8G/tjlWWcDTwfXHwvq99SGiIxyZQUFFG3bxl/+8hdOPPHEsMMRERERGfYaGho48sgjmTFjRtihDIhc9jwvAb4AvGhmzwdl/4fMatkLyIyw3Ah8BcDdXzKzO4G1ZFbq/rq7pwDM7ALgESAK3OTuLwXPuxi43cx+BKwik6wTvP7WzNYDtWQS7l7bEBGZk5/PHb/+NR/84AeH5DwbERERkeGksbGReDwedhgDJmfJs7v/lUyv8b6W9nLPvwP/3k350u7uc/cNdLNatru3AJ/qTxsiItNLS3nmlVd45ZVXOPzww8MOR0RERGRYa2hooKKiIuwwBkwu93kWERlWzIzDzLjx+utJp9NhhyMiIiIyrDU1NY2otWSySp7N7H1m9qXgfHww/1hEZMSZU17OM48+yoYNG8IORURERGRYa25uHlHDtvtMns3sMjJziy8NiuLA/+QyKBGRsOTHYkwdM4Zdu3aFHYqIiIjIsDbSep6zmfP8MeBI4DkAd3/bzMbkNCoRkRDlpdOsXr2aSCSCmRGJRIhGo8RiMeLxOPF4nGg02rmoWEedwsJCSkpKQo5eREREZGgYaT3P2STPbcE2Tw5gZkU5jklEJFRT43GeuPFGniCzLUDHkQZSQMqdjg3gO17TQGskwm/vvZeJEycOdsgiIiIiQ0oymSSVShGNRsMOZcBkkzzfaWa/BMaa2XnAOcCvchuWiEh4Dhs3jsP2474Hd+7kzTffVPIsIiIio15Hr/NI2v6zz+TZ3a80s5OAOmAW8G/uviznkYmIDDPF7e1s3ryZY4551w56IiIiIsPW7t27ee211zp3I3F33L3zvLm5mY0bN5JKpTqvNTc3E4vlbGfkUGT11bj7MjN7pqO+mZW5e21OIxMRGWbGRqMsW7qU3bXd/3g0M+iYQx2LMX/+fObOnTuiPpEVERGRkefWW2/ljjvuoKqqaq/yjr9hIpEIBQUFnUO0O8rnzZs3uIHmWJ/Js5l9Bfg+0EJmWp+RmeZ3SG5DExEZXmaMG8dLzz/PC6tWdXvdu5wn3fl9NErFoYfyybPPpry8fK8FyLq+dmVmlJSUUFVVRVGRlqAQERGR3Nu2bRsLFy5kxowZYYcSqmx6nv83MNfdd+Y6GBGR4awoHueYfT6R7Y278+bbb3PHZZeRCj6p9d7qB6+twJ5kkqopU5j/3vdy2qc+Nep/mYmIiEju7Nixg4qKirDDCF02yfPrQFOuAxERGW3MjGmlpUwrLe33vWl3duzezaZ77uEb993HQdOnA3DU4sV89cILBzpUERERGcV27tzJwQcfHHYYocsmeb4U+Hsw57m1o9Ddv5GzqEREpFcRMyYWFTGxqIjD29qo37qVxvZ2Htu1S8mziIiIDBh3p7a2lsLCwrBDCV02yfMvgceAF8nMeRYRkSGkOJGgOJEgmU7zxI4dpNNpIpFI2GGJiIjICNDQ0ICZEY/Hww4ldNkkz3F3vyjnkYiIyAGJRSIkyGwnUVZWFnY4IiIiMgKo1/kd2XRNPGRm55tZlZmVdRw5j0xERPqtOBplx44dYYchIiIiI0RtbS15eXlhhzEkZNPzfGbwemmXMm1VJSIyBBWRWRFz1qxZYYciIiIiw9BPf/pTli9fTiqVAjJ/V+y7v/No1Wfy7O7TByMQERE5cPnt7ezcqZ0FRUREZP88/vjjHHrooZ1bU0UiEQ3bDvSZPJvZWd2Vu/stAx+OiIgciEIzXl23DnfHzMIOR0RERIaZaDRKRUUFpfuxleZIl82c5/d2Od4PXA6cmsOYRERkP80aN47ld93Fdy68kK1bt4YdjoiIiAwz2rWjZ31+V9z9wi7HecBRQHHuQxMRkf4qycvjE5WV+LPPcu4nPsGdt99OMpkMOywREREZJpQ892x/viuNgOZBi4gMUREzFk6YwKnFxdzzn//JV846i1dffTXssERERGQYSKVSSp57kM2c5/vJrK4NmWR7NnBnLoMSEZEDV5qXx6lVVby8cSPf/NznOO7kkznz7LOZMWNG2KGJiIjIEJVKpbRuSg+y2arqyi7nSeBNd9+co3hERGQAmRmzy8uZkUrx4p/+xDcffpjDjzmGz3/5yyxYsEC/HEVERGQv6nnuWTZbVT05GIGIiEjuJKJRjp4wgQXpNOuee47LzzuPokmTKB4zprOOmRGLxZg4eTJFY8ZgQCQaZdyECYwfP54pU6Ywb9688L4IERERyTn1PPesx+TZzOp5Z7j2XpcAd/eSnEUlIiI5EY1EmFNRwWx3djQ2kqqvB975YZ92p/7VV9mRSnW+fz2VoiUS4W0zLrriCk488cSQohcREZFc04JhPesxeXb3MT1dExGR4c3MmFBY2K97apqb+cl3v8vEiROZO3dujiITERGRMGnYds+y+q6Y2XwzuyA4jsh1UCIiMvSUFxTw/kSC737jG9pDWkREZARyz4xF07Dt7vWZPJvZN4FbgQnBcauZXZjFfVPM7HEzW2tmLwXPwczKzGyZmb0WvI4Lys3MrjGz9Wb2gpkd1eVZZwf1XzOzs7uUH21mLwb3XGPBf+X9aUNERPo2rbSU9zQ3c/GFF1JbW0tLS0u3R3t7e9ihioiISD9pvnPvsllt+1xgkbs3ApjZfwBPAz/r474k8C/u/pyZjQFWmtky4IvAn939x2Z2CXAJcDHwEWBmcCwCrgMWmVkZcBmwkMy0vJVmdp+77wrqnAc8AywFTgYeCp6ZdRtZfA9ERCQwv6KCxk2b+MyHPvSuax1zp2OFhdz5wAOUlGh5DBERkeFCQ7Z7l03ybECqy/tUUNYrd98KbA3O683sZWAycBpwfFDtZuAJMontacAtnhkrsNzMxppZVVB3mbvXAgQJ+Mlm9gRQ4u7Lg/JbgNPJJM/9aiOIVUREsnTchAkc18v13+3YQUNDg5JnERGRYUQ9z73LJnn+NfCMmd0bvD8duLE/jZjZNOBIMj3EE7skq9uAicH5ZGBTl9s2B2W9lW/uppz9aGOv5NnMzgfOBxjfzwV1REQEYma0tbWFHYaIiIj0g3qee5fNPs9XmdmTwJKg6EvuvirbBsysGLgb+Ja713X9JMPd3cy62w5rwOxPG+5+A3ADwNSyspzGJyIyEkWB1tbWsMMQERGRftA2Vb3rbZ/npcBtwB/cfSWwsr8PN7M4mcT5Vne/Jyje3jFUOhiWXR2UbwGmdLn9oKBsC+8Mwe4ofyIoP6ib+vvThoiIDKAYSp5FRESGGw3b7l1vPc+/BM4Arjazx4HfAQ+6e1bj8IKVr28EXnb3q7pcug84G/hx8PrHLuUXmNntZBbx2hMkv48A/7djxWzgQ8Cl7l5rZnVmdiyZ4eBn8c4iZv1qI5uvR0REshcFDdsWEREZYtauXcuzzz5LOp0mnU6TTCY7j/b2dmpra2lubg47zCGrx+TZ3f8I/NHMCoF/IpOcXmdmDwG3ufuyPp69BPgC8KKZPR+U/R8yCe2dZnYu8Cbw6eDaUuAUYD3QBHwpiKPWzH4IPBvU+0HH4mHA14DfAAVkFgp7KCjvVxsiIjKw1PMsIiIyOB599FGeffZZkslk5z7N7k46nd6r3p49e3jppZeYNGkSsVgMMyMSiXS+dhxLlizprhkBrOMbnFVlsyPIrF59hLtHcxbVEDK1rMy/vWABlUVFYYciIjJsLNuxg/eefTaLFy9m4sSJVFVVhR2SiIjIiLF7925Wr15NU1MTV199NbNmzSIazaRnHcOu9x1+HY1GOeigg4jH44Me71D3r//6r00NDQ19Jnx9LhhmZhPJ9NyeAVQBd5LZq1lERKRbM/Py+PvNN/PX3/6WPXl53PnggxRq9wIREZEB8cgjj3DjjTdSUVHBUUcdxdSpU8MOaVTobcGw84AzgVlkFv36jrv/fbACExGR4WtaSQnTgvMnduzgkx/6ECeccgrHLFlCPB7v/HS8g5kxbdo0JkyYMOixioiIDDc7duzg0EMPZd68eWGHMqr01vO8GLgC+LO7p3upJyIi0qPjx4+noa2NdX/4A7++7z7cjM5fKu5gRiqdhqoqbrnrLmKxPgdFiYiIjCrt7e3s3r2b+vp6GhoaWL9+PQUFBWGHNer0tmDYOYMZiIiIjFzFiQQLJ07stc59W7awbNkyPvKRjwxSVCIiIuFxd9atW8e6detoaGjoTIwbGxuBzK4Vb731FjU1NTQ3N1NQUEAikSAejxOJRDjqqKNC/gpGH328LyIiQ8LC4mJ+ddVV1OzcSVFxcWe5mXUuehKJRFi0aJGGd4uIyJCydetW7r33XtLpNG1tbdTV1dHc3Ny5BVQqlSKVSu11z+7du6mrq6OiooJYLEY8HieRSOy1EvasWbMoLi4mLy9P+y8PAUqeRURkSJhUXMyCXbt45r/+i2TwB4KTSZ49ON+4Zw87L76YL517bpihioiI7GXFihU8+OCDTJ06lUgk0tlDHI/HycvL69wGqqtJkyZRXl6upHgYySp5NrP3ATPd/ddmNh4odvc3chuaiIiMNjPHjWPmuHE9Xt+Qn8+LK1aAkmcRERlCtm7dyqRJk7SA1wgX6auCmV0GXAxcGhTFgf/JZVAiIiLdqSwq4pWXXsLdww5FRESk06ZNmxgzZkzYYUiO9Zk8Ax8DTgX1oxTNAAAgAElEQVQaAdz9bUD/Z4iIyKArjMex1la2bt0adigiIiKdtm7dquR5FMgmeW7zzEf8DmBmRbkNSUREpGflZrz22mthhyEiItJp27ZtFHdZ7FJGpmyS5zvN7JfAWDM7D3gU+FVuwxIREeneuGSSdWvXhh2GiIgIkNlSqr6+nsLCwrBDkRzrM3l29yuBu4C7gVnAv7n7z3IdmIiISHcqi4pY8+yzYYchIiICwI4dOygqKtKq2aNAn6ttm9lFwB3uvmwQ4hEREenVxMJCHl+3jnQ6/a5tP0RERAZbdXU1BQUFYYchgyCbvzrGAH8ys7+Y2QVmNjHXQYmIiPQkPxYjP5Vi06ZNYYciIiKi5HkUyWbY9vfdfQ7wdaAKeNLMHs15ZCIiIj0oA9avXx92GCIiImzbto1EIhF2GDII+hy23UU1sA2oASbkJhwREZG+laXT3H/vvbzx+usAFBYVcdrpp1NUpA0hRERkcG3evFkrbY8S2cx5/hrwaWA88HvgPHfXMqciIhKaw8vKWLt8OS8tXw7A7nSa+++4g6//679SXl7+rvodi7h0t5hL17LeFnsxs87riUSCqqoqYrH+fAYtIiIj0dtvv83YsWPDDkMGQTa/9acA33L353MdjIiISDaKEwmOqaraq2zDrl1c+y//QnqfRcS8l+f0dq23em3pNM3AtEMP5fAjjuDwefM45JBDmD59Ovn5+Vk+VURERoJt27Zx0EEHhR2GDIIek2czK3H3OuA/g/dlXa+7e22OYxMREcnaIWPHcsggtteWSrFj61a2r1/PunvuYXc0Sm17O1UHHcRhc+Zw8GGHMXHiRMrLyw+4hzo/P1+JuYjIEJRKpaitrdWw7VGit9/mtwEfBVaS+dC961g2h0H9G0VERGRISUSjTC4uZnKXP5hS6TS19fVUP/YYKx5+mOZolJZoNOse7m6Z0ebO7lSKqYccwvz3vpc5CxZw+OGHM2nSJO0rKiISotraWhKJBNFoNOxQZBD0mDy7+0eD1+mDF46IiMjwFY1EGF9YyPjCwgF/djKdpnr7dt68/XZW3XknOwHPz2f2EUdwxDHH8J7ZsznssMMYM2bMgLctIiLdq66upjAHP/NlaMpmwbA/u/uJfZWJiIhI7sQiESYVFzOpS093Q1sb21at4umnn2ZpPM6OZJKyCRNIJBJEIhEM+H8/9Sk+dcYZ4QUuIjKCVVdXa0rNKNLbnOd8oBCoMLNxvDNsuwSYPAixiYiISC+KEwlmJBLMCN6n3dnd0kKqpQWA6qYmHlu6VMmziEiOVFdXa4/nUaS3nuevAN8CJpGZ99yRPNcBP89xXCIiItJPETPKCgo6349JJHh2/XrS6TSRfVYhFxGRA7dlyxaKiorCDkMGSW9znn8K/NTMLnT3nw1iTCIiIjIA8mMxEqkUW7duZfJkDRoTERloW7Zs0VoTo0ifc57d/WdmNheYDeR3Kb8ll4GJiIjIgRtnxhtvvKHkWUQkB7Zu3cp73vOesMOQQdLnGC4zuwz4WXCcAPx/wKlZ3HeTmVWb2ZouZZeb2RYzez44Tuly7VIzW29mr5jZh7uUnxyUrTezS7qUTzezZ4LyO8wsEZTnBe/XB9en9dWGiIjISFWaTPL6a6+FHYaIyIjj7mzfvl09z6NInz3PwCeB+cAqd/+SmU0E/ieL+35DZm70vj3UV7v7lV0LzGw2cAYwh8wc60fN7LDg8rXAScBm4Fkzu8/d1wL/ETzrdjO7HjgXuC543eXuM8zsjKDeZ3pqw91TWXwtIiIiw9L4/HzWrV4ddhgiIkOOu5NKpUgmk6RSKdyd+vp6Nm3ahLuTTqdxd9z9Xfe5Ozt37qSlpUULho0i2STPze6eNrOkmZUA1cCUvm5y96e69vr24TTgdndvBd4ws/XAMcG19e6+AcDMbgdOM7OXgQ8Cnw3q3AxcTiZ5Pi04B7gL+LmZWS9tPJ1ljCIiIsPOhMJClq1dG3YYIiKD6s033+Tb3/42sVisMxEGSKfTnecNDQ0kk0nMLLO9nxnxeJySkhIy6QOdr/syM8yMefPmDc4XJENCNsnzCjMbC/yKzKrbDRxYwnmBmZ0FrAD+xd13kdn6anmXOpt5ZzusTfuULwLKgd3unuym/uSOe9w9aWZ7gvq9tSEiIjIilSQS1G/fTn19vYYWisioUV1dTUtLC+9///uBd5LdrklxPB5Xr7H0SzYLhn0tOL3ezB4GStz9hf1s7zrgh4AHrz8BztnPZ+WMmZ0PnA8wvrAw5GhERET2n5lRFovxxhtvcMQRR4QdjojIoGhra6OkpITS0tKwQ5ERpMcFw8zsqH0PoAyIBef95u7b3T3l7mkyPdkdQ7O3sPdQ8IOCsp7Ka4CxZhbbp3yvZwXXS4P6PT2ruzhvcPeF7r4wPy9vf75UERGRIaM0mWTDhg1hhyEiMmja2tqIRqNhhyEjTG89zz/p5ZqTmXPcL2ZW5e5bg7cfAzpW4r4PuM3MriKzmNdM4B+AATPNbDqZRPcM4LPu7mb2OJnFzG4Hzgb+2OVZZ5MZWv5J4LGgfk9tiIiIjGhl0SivvPginH562KGIiAyK9vb2Hucri+yvHpNndz/hQB5sZr8DjgcqzGwzcBlwvJktIJN8bwS+ErT1kpndCawFksDXO1bBNrMLgEeAKHCTu78UNHExcLuZ/QhYBdwYlN8I/DZYEKyWTMLdaxsiIiIj2fiCAtasWdN3RRGREaKtrY1IpM9deUX6pc85z8HiXu/i7vtuQbXv9TO7Kb6xm7KO+v8O/Hs35UuBpd2Ub+CdYd9dy1uAT/WnDRERkZFsfGEhmzZuJJlMEotls1aoiMjw1tbWpp5nGXDZ/AZ9b5fzfOBE4DnevX+ziIiIDEGxSIRiMzZt2sT06dPDDkdEJOdaW1uVPMuAy2a17Qu7vg+2rbo9ZxGJiIjIgBvrzhtvvKHkWURGtJaWFurr69m5c6cWDJMBtz9jtxoB/eYVEREZRkpTKda/+iof/GC/1/sUERkWXnjhBS644AIKCwuJxWLMnj077JBkhMlmzvP9ZBb4gszWVrOBO3MZlIiIiAys8QUFvLxqVdhhiIjkzFtvvcXBBx/M4sWLww5FRqhsep6v7HKeBN509805ikdERERyYEJhIU+vW0djYyNFRUVhhyMiMuB27dpFPB4POwwZwfpcv93dn3T3J8lsB/Uy0GRmZTmPTERERAZMcSLBQU1NXHrRRbS0tIQdjojIgNu8eTMFBQVhhyEjWDbDts8HfgC0AGnAyAzjPiS3oYmIiMhA+sDEiTy6ciX/dsklnPapvXd1NDNisRixWIwxY8YwY8YMrVQrIsPGzp07eeKJJzjppJPCDkVGsGyGbX8HmOvuO3MdjIiIiOSOmXFiZSVPP/00v1q+vHNBk+AiaTKfkjem03hREe/7X/+LJccfz4IFCygsLAwnaBGRLPz2t79l0qRJ+lklOZVN8vw60JTrQERERCT3ImYsmTChz3p7WltZ/4c/8PM//pGdwBELF1I5eTIA+UVFjKuooLS0lJKSks4jkUh09labWed5JBLpsRygpKSEWGx/NgARkZGusbGRBx54gPb29m6vp9NpWlpaePDBBznllFMGOToZbbL5TXUp8HczewZo7Sh092/kLCoREREJVWleHkdPnMjRQFsqxVurV1OzciUA7ek061Ip2qJR2iIR2sxoBVLue/VmO0BQ9q7yQFNrK4tOOIEfX311jr8iERmOnn/+eW666SYmTZoE0O10EjNj0aJF5OfnD3Z4Mspkkzz/EngMeJHMaC4REREZRRLRKDPGjcvJs5PpNLf99a9s2rSJKVOm5KQNERm+XnvtNSZPnszChQvDDkUkq+Q57u4X5TwSERERGXVikQgzgD/cdRcXfvvbYYcjIjnS2NjI0qVLqa6upqlp7xmh6XQad+/2vlWrVjF16tTBCFGkT9kkzw8FK27fz97DtmtzFpWIiIiMGkeUlXHv3Xdzzvnnaw9qkRGoqamJ73znO1RXV1NeXk48Hu9x+PW+KisrNSpFhoxskuczg9dLu5RpqyoREREZEMWJBONra3l02TJOO/30sMMRkQHU3NzMxRdfTH19PSeccIK2wJNhrc/k2d2nD0YgIiIiMnrNKyrizt/8hlNPO01/XIsMc3V1dbzwwgts2bKFJ598krq6OhYvXqx/2zLs9Zk8m9lZ3ZW7+y0DH46IiIiMRpOKi/n7li389OqrKa+oIBaPE41GiUajxGIxIpFI53k0Gu3c5mp//hjPy8tj3Lhx5OfnZ3V/T3WybTsejzN27FitBCzDWm1tLXv27OEvf/kLTz31FKlUCqBzrnI6nVlXuL29ne3bt1NeXk5RURFFRUVKnGXEyGbY9nu7nOcDJwLPAUqeRUREZECYGceXlLDh1lt53T2zvYdZ5jUSIW2GQ+crHa/7oT3YWqu9hwWKsgw4q2ruTsqdVmDeUUex5KSTWLBgAdOnT1cyIcNCY2MjV111FU8++SR5eXmUl5czderUXuctH3fccUSj0RCiFcmtbIZtX9j1vZmNBW7PWUQiIiIyKo0vLGR8YWHYYeREWyrFWy++yMMrVnATcNgxx/Cd732PqqqqsEMT6VZ1dTVr167l2muvpaCggFNPPZVYLJt+N5GRa3/+BTQCmgctIiIikqWOvbJnAGl3nl+5knM/8QnOvegiTv/Yx9RLJ4Oura2NX/ziF+zatQvIjJJob28nlUrx1ltvUVNTw9ixYznkkEM4+OCDQ45WZGjIZs7z/dA5MioCzAbuzGVQIiIiIiNVxIyjxo9neksLd15xBX9eupT/uv56EolE2KHJKLJixQoeffRRZs6c2Tn8umM9gTlz5lBWVqapBSL7yKbn+cou50ngTXffnKN4REREREaFcfn5nFZVxW1r1lBTU6Mh3JJzV199Nffffz/5+fm0trYyZ84cZs6cGXZYIsNGj8mzmc0AJrr7k/uULzGzPHd/PefRiYiIiIxgZkY8GqW9vT3sUGQUWLVqFccccwwTJkwA0ArwIv0U6eXafwF13ZTXBddERERE5ABFQcmzDIqamhoqKirIz89X4iyyH3pLnie6+4v7FgZl03IWkYiIiMgoEiWzeJNILrW3t9PY2EhBQUHYoYgMW70lz2N7uaZ/dSIiIiIDIGqmnmfJuV27dpGfn69FwEQOQG/J8wozO2/fQjP7MrAydyGJiIiIjB4Rd/U8S87V1NSo11nkAPW22va3gHvN7HO8kywvBBLAx3IdmIiIiMhooGHbMhhqamrIy8sLOwyRYa3Hnmd33+7uxwHfBzYGx/fdfbG7b+vrwWZ2k5lVm9maLmVlZrbMzF4LXscF5WZm15jZejN7wcyO6nLP2UH918zs7C7lR5vZi8E911gwBmV/2hAREREJS8Rdw7Yl52pqarSXuMgB6m3YNgDu/ri7/yw4HuvHs38DnLxP2SXAn919JvDn4D3AR4CZwXE+cB1kEmHgMmARcAxwWUcyHNQ5r8t9J+9PGyIiIiJh0rBtGQzV1dVKnkUOUJ/J8/5y96eA2n2KTwNuDs5vBk7vUn6LZywHxppZFfBhYJm717r7LmAZcHJwrcTdl7u7A7fs86z+tCEiIiISmqh6nmUQbN26leLi4rDDEBnWcpY892Ciu28NzrcBE4PzycCmLvU2B2W9lW/upnx/2ngXMzvfzFaY2YqW1tYsvzQRERGR/lPyLIOhurqaoqKisMMQGdYGO3nuFPQY+1Bsw91vcPeF7r4wXwsriIiISA5ZOq1h25JzSp5FDtxgJ8/bO4ZKB6/VQfkWYEqXegcFZb2VH9RN+f60ISIiIhKaqDutGukmOeTu1NTUKHkWOUCDnTzfB3SsmH028Mcu5WcFK2IfC+wJhl4/AnzIzMYFC4V9CHgkuFZnZscGq2yftc+z+tOGiIiISGiiZrS1tIQdhoxgDQ0NAMTj8ZAjERneetvn+YCY2e+A44EKM9tMZtXsHwN3mtm5wJvAp4PqS4FTgPVAE/AlAHevNbMfAs8G9X7g7h2LkH2NzIreBcBDwUF/2xAREREJUywSoaWpKewwZASrqamhoKAg7DBEhr2cJc/ufmYPl07spq4DX+/hOTcBN3VTvgKY2015TX/bEBEREQlLNBKhVT3PkkM1NTXk5+eHHYbIsBfagmEiIiIikul51rBtyaXa2lrytAiuyAFT8iwiIiISoqiZFgyTnNq5cyeJRCLsMESGvZwN2xYRERGRvsUiEVqbm8MOQ0aYjRs3snPnTtydtWvXas6zyABQ8iwiIiISoqgZbep5ln5oamrid7/7HVu2bKG5uZmWlhZSqdRe1zdt2kRpaSmQ2arqiCOOCCtckRFDybOIiIhIiGKRiIZtS7889dRT3HvvvcyYMYN4PN65GFhmB1coKSlh/vz5RKPRMMMUGXGUPIuIiIiEKBaJqOdZ+qWhoYHy8nIOO+ywsEMRGVW0YJiIiIhIiDRsW/qrrq6OeDwedhgio46SZxEREZEQadi29FddXZ1WzxYJgZJnERERkRDFIhHa29rCDkOGkbq6Ou3bLBICJc8iIiIiIYqa0d7eHnYYMoyo51kkHEqeRUREREKkBcOkv+rr69XzLBICJc8iIiIiIYpFIrSp51n6oaGhQT3PIiFQ8iwiIiISoo5h2+4edigyTCh5FgmHkmcRERGREJkZESCZTIYdigwTDQ0NGrYtEgIlzyIiIiIhi0UitGnFbclCMpkkmUwSi8XCDkVk1FHyLCIiIhIyrbgt2WpsbCQej2NmYYciMuooeRYREREJWdRMPc+SlcbGRs13FgmJkmcRERGRkKnnWbKl5FkkPEqeRUREREIWU/IsWWpsbNR8Z5GQ6F+eiIiISMgiwIoVK9i+fTuRSIRoNLrXUVxczJQpU8IOU4YAJc8i4dG/PBEREZGQTY9EeOAnPyENOGRezTKv7tSlUvzT2Wcz6/DDOfHEE8MNVkKl5FkkPPqXJyIiIhKyI8vKer2+p7WVl379a37X1sacP/2JysrKQYpMhprGxkai0WjYYYiMSprzLCIiIjLEleblcVxVFUcVFXHxhReyefPmsEOSkNTX1yt5FgmJkmcRERGRYeLYigomb97MV888k+XLl4cdjoSgrq6OvLy8sMMQGZWUPIuIiIgMI/MqKvhgLMYPLriA39x0E+l0OuyQZBDt2bNHW1WJhERznkVERESGmariYj6Rl8fSa69l7QsvcOonP8ncuXMpLS0FwMxCjlBypa6uTsmzSEhCSZ7NbCNQD6SApLsvNLMy4A5gGrAR+LS777LMT/+fAqcATcAX3f254DlnA98LHvsjd785KD8a+A1QACwFvunu3lMbOf5yRURERAZcYTzOqZWVrH76aX65fDnbk0lau/RCmxkGJAoK+NIFF1BaWkpBQQHRaJT8/HwWLlyoJHsYqq+vp6yPBeZEJDfC7Hk+wd13dnl/CfBnd/+xmV0SvL8Y+AgwMzgWAdcBi4JE+DJgIZldHVaa2X1BMnwdcB7wDJnk+WTgoV7aEBERERl2opEIR02Y8K5yd8+8Apvr63nyyitJRaO0m+HAjmSSf7v2WhYtWjS4AcsBa2ho0GrrIiEZSnOeTwNuDs5vBk7vUn6LZywHxppZFfBhYJm71wYJ8zLg5OBaibsv98xvjlv2eVZ3bYiIiIiMGGaGmRExY2pJCR+oquKECRP40PjxfHj8eObHYjxw111hhyn7ob6+XguGiYQkrOTZgT+Z2UozOz8om+juW4PzbcDE4HwysKnLvZuDst7KN3dT3lsbIiIiIqPGrLIy/vHUU9TW1oYdivRTY2Oj5jyLhCSs5Pl97n4UmSHZXzezD3S9GPQYey4D6K0NMzvfzFaY2YqW1tZchiEiIiIy6BLRKFPSaR5+6KGwQ5F+SKfTtLS0KHkWCUkoybO7bwleq4F7gWOA7cGQa4LX6qD6FmBKl9sPCsp6Kz+om3J6aWPf+G5w94XuvjBfw2JERERkBDpq7Fhuve46duzYEXYokqWmpibi8bgWehMJyaAnz2ZWZGZjOs6BDwFrgPuAs4NqZwN/DM7vA86yjGOBPcHQ60eAD5nZODMbFzznkeBanZkdG6zUfdY+z+quDREREZFRZVx+PpUtLfzjH/8IOxTJkoZsi4QrjNW2JwL3Bp+YxYDb3P1hM3sWuNPMzgXeBD4d1F9KZpuq9WS2qvoSgLvXmtkPgWeDej9w946JO1/jna2qHgoOgB/30IaIiIjIqFMMVG/bFnYYkiUlzyLhGvTk2d03APO7Ka8BTuym3IGv9/Csm4CbuilfAczNtg0RERGR0WhMPM6mDRvCDkOy1NDQQCwW5k6zIqPbUNqqSkREREQGUWVRES+tWhV2GJKlpqYmJc8iIVLyLCIiIjJKleXns3vHDmpqasIORbLQ2Nio5FkkREqeRUREREYpM2NiNMrLL78cdiiShYaGBqLRaNhhiIxaSp5FRERERrHyZJIXNHR7WFDyLBIuJc8iIiIio9jk4mKee/rpsMOQLOzZs0erbYuESJMmREREREaxiUVFPLJ+Pc3NzRQUFIQdzojn7jQ2NtLa2oq7k06nSaVSpNNp3J3MRjN7e/PNN/nzn//Mo48+yqJFi0KIWkRAybOIiIjIqBaLRCiLRHjllVdYsGBB2OEMe+7OVVddxdatWwFIp9M0NDRQV1dHXV1d56JfsVgMM+s8gL3OO5gZeXl5VFZWcvrpp+sDDpEQKXkWERERGeUqkkleWrNGyfMAWLVqFY899hjz5s3rLKusrGTatGnk5eWRl5dHJKKZkyLDkZJnERERkVGuMj+f5/7+dz73+c+HHcqw94c//IFp06YxderUsEMRkQGmj71ERERERrnJY8awdvVqUqlU2KEMa7W1tfz9739n5syZYYciIjmg5FlERERklCuIxShqaeGSb3+bNWvW0NbWRjKZ7HbxKunZI488QmVlpVbEFhmhNGxbRERERDitspK1//gH312+nPpUKrMSNJkFqxJ5eVzxs59x9NFHhx3mkJVOp7n77ruZO3du2KGISI4oeRYRERERYpEIR4wfzxH7lLs7WxoauOyb3+RHP/858+fPf9eK0AJ33nkn7e3tTJgwIexQRCRHlDyLiIiISI/MjIPGjOGo2louPe88EmPGkEgkiMfjHH/KKZz31a+O6mS6urqaX/7ylzz11FOcdNJJYYcjIjmk5FlERERE+vSesjIOd6ehvZ10Ok2yuZmHf/Uranbs4NJ/+7ewwwvN3/72N1auXMnJJ5+sPZhFRjgtGCYiIiIiWTEzxiQSlOblUV5QwMeqqvjrfffx0ksvhR1aaHbt2kVFRYUSZ5FRQMmziIiIiOyXWCTC/Hic//75z0ftytw1NTXk5eWFHYaIDAIN2xYRERGR/TanvJw7VqzghRdeYP78+WGHMyjefvttfvjDH9Lc3Mzbb7/NggULwg5JRAaBep5F/v/27j5ezrI88PjvSkIIEiABIiJBAoKtSNugAXkREBSMrivY+oJ2hbastBZtT6WrqN26W5fPYnXlpbVaFBZwBaRYS9aFBETAQhuSIId3KUcoEkogEATCW0hy7R9zn3Q4nHOec87MnJkz8/t+PvM5z3M/b9cz9zxz5pr7fu6RJEkTNi2ChVttxbfOOadnWp8vvvhi1q9fz1577cWRRx7JHnvs0e6QJE0Ck2dJkiQ15Fd23JGHbruNW265pd2htNzjjz/O1VdfzcKFC9l5553ZYYcdenq0camXmDxLkiSpIdMi2H/rrfnW2Wd3bevzhg0buOSSS/jYxz7GXnvt5QBhUg/ynmdJkiQ17A1z53LbPfewfPlyDj744HaH01RPPvkkn/rUp3jxxRc5/PDD2WGHHdodkqQ2MHmWJElSwyKCN8+axbfOPpuDDjpoSnVlzkzOPPNMVq1a9YpygGeffZaddtqJQw45pB3hSeoQJs+SJElqitfPmUP/wAA33ngjhx12WLvDGVVm8k//9E9ccMEFPPHEE2zatIkDDjhg2KR/2rRpbL/99m2IUlInMXmWJElSU0QEi171Ks496ywOOeQQpk+f3u6QuOeee/jmN7/JU089xTPPPMPTTz/NSy+9xObNm9luu+3Yb7/92Guvvdhuu+2YNs3hgCSNzORZkiRJTbPH9ttz64MPsmTJEt7//ve3OxxWrFjBI488wpve9CZmzZrFzJkzmTFjBhFhsixpXEyeJUmS1DQRwdvnzuXcL32JH37ve8zfYw/mvfa1TKvrDj19q604/qMfbfnAW5s3b2ZgYIDXvva1zJs3r6XHktT9ejJ5jojFwNnAdODbmXlGm0OSJEnqGnNnzeL417yGJx59lKceeoh/3bDhZcvve+klfn3hwpaNyv3iiy9y6qmncvfddzNz5kyOPvrolhxHUm/pueQ5IqYDXweOBlYDKyNiSWbe3d7IJEmSusfM6dPZdfZsdh1m2TNr1rB69eqWHDcz+c53vsOjjz7Kcccdx4wZPfdxV1KL9OK7yYHAQGbeDxARlwLHAibPkiRJk2Cf2bM5/6tf5cGf/5zd9tiDHXfckblz5zJ37lxmzZq15V7kiCAimD17Nttuu+2W+eFs2rSJZ555hj//8z/ngQce4LDDDjNxltRUvfiOshvwUN38auCt9StExMnAyQCzYOOZ11330izYnDAjYOPkhape42tMk8HXmVrN15jGYgPELStWTAfYBLkZchOUX1YG6qYTGFz+EtxP7fW1M/A4sN2MGTN2nTZtWkybNi1mzpw5fc6cOZtuv/32yT0hdZ3MnBERvpf1gE2bNm09lvV6MXmulJnnAucOLY+IVZm5qA0hqUf4GtNk8HWmVvM1psng60yt5mtMQ/Xi+PwPA7vXzc8vZZIkSZIkDasXk+eVwD4RsWdEzASOB5a0OSZJkiRJUgfruW7bmbkxIj4JLKP2U1XnZ+ZdY9z8FV25pSbzNabJ4OtMreZrTJPB15lazdeYXiayfnVH7p0AACAASURBVFgGSZIkSZL0Cr3YbVuSJEmSpHExeZYkSZIkqYLJ8zhExMKIWB4R/RGxKiIObHdM6k4R8amI+FlE3BURf9nueNS9IuLUiMiI2Lndsai7RMRXyvvY7RHxg4iY0+6Y1B0iYnFE3BsRAxFxWrvjUfeJiN0j4rqIuLt8FvvjdsekzmDyPD5/Cfz3zFwI/HmZl5oqIo4EjgV+IzPfBHy1zSGpS0XE7sAxwC/aHYu60jXAfpn568C/AJ9rczzqAhExHfg68G5gX+AjEbFve6NSF9oInJqZ+wIHAaf4OhOYPI9XAtuX6R2Af2tjLOpenwDOyMwXATLzsTbHo+51JvAZau9tUlNl5tWZubHMLgfmtzMedY0DgYHMvD8zNwCXUvvCWWqazHwkM39app8B7gF2a29U6gQmz+PTB3wlIh6i1hrot+hqhTcAh0XEzRFxQ0Qc0O6A1H0i4ljg4cy8rd2xqCf8HnBVu4NQV9gNeKhufjUmNWqhiFgA7A/c3N5I1Al67neeq0TEj4DXDLPoC8A7gD/JzO9HxIeA84B3TmZ86g4Vr7MZwI7UugkdAFwWEXulvyuncap4nX2eWpdtacJGe41l5hVlnS9Q6wL53cmMTZIaFRGzge8DfZn5dLvjUfv5O8/jEBFPAXMyMyMigKcyc/uq7aTxiIilwJcz87oy/3PgoMxc297I1C0i4teAa4HnStF8arehHJiZa9oWmLpORPwO8PvAOzLzuYrVpUoRcTDw3zLzXWX+cwCZ+T/bGpi6TkRsBfwQWJaZX2t3POoMdtsen38DjijTRwH3tTEWda9/AI4EiIg3ADOBx9sakbpKZt6Rma/OzAWZuYBat8c3mzirmSJiMbV76t9n4qwmWgnsExF7RsRM4HhgSZtjUpcpjWTnAfeYOKue3bbH5+PA2RExA3gBOLnN8ag7nQ+cHxF3AhuAE+2yLWkK+mtga+Ca2udQlmfmH7Q3JE11mbkxIj4JLAOmA+dn5l1tDkvd51DgY8AdEdFfyj6fmVe2MSZ1ALttS5IkSZJUwW7bkiRJkiRVMHmWJEmSJKmCybMkSZIkSRVMniVJkiRJqmDyLEmSJElSBZNnSZJGERGbIqI/Iu6KiNsi4tSImFaWLYqIc0bZdkFEfHTyon3ZsedExB+2aN/vi4jTRli2fgL7+3ZE7NuEuHaNiB+W6UMj4vaIWBUR+5SyORFx9WD9lbIfRcTcRo8tSep+/lSVJEmjiIj1mTm7TL8auBi4KTO/OIZt3w78aWa+t7VRDnvsBcAPM3O/ST7uludrjOtPz8xNTTr2V4AbM/OKiPh74I+ABcD7M/PUiPgqtefk+rptTgTmZ+bpzYhBktS9bHmWJGmMMvMx4GTgk1Hz9rqWziNKC3V/RNwaEdsBZwCHlbI/KS3R/xgRPy2PQ8q2b4+I6yPi8oj4WUR8NyKiLDsgIv6ptHqviIjtImJ6RHwlIlaW1tXfHybcM4DXl2N/pcT7lYi4MyLuiIgPD92gxPeziLggIv6lxPHOiLgpIu6LiAPLer8TEX9dpveMiH8u+/wfdft6e0T8JCL+X0TcGxHfrGuxXx8R/ysibgMOLue+qCxbXJ6b2yLi2lK2bUScX87/1og4doQq+i1gaZl+CXhVebwUEa8Hdq9PnIslwEdGrHRJkooZ7Q5AkqSpJDPvj4jpwKuHLPpT4JTMvCkiZgMvAKdR1/IcEa8Cjs7MF0pX4kuARWX7/YE3Af8G3AQcGhErgO8BH87MlRGxPfA8cBLwVGYeEBFbAzdFxNWZ+UBdPKcB+2XmwnLs3wIWAr8B7AysjIifZOYjQ85jb+CDwO8BK4GPAm8D3gd8HjhuyPpnA9/IzIsi4pQhyw4E9gUepJbU/iZwObAtcHNmnlpio/ydB3wLODwzH4iIHct+vgD8ODN/LyLmACsi4keZ+ezggSJiT+DJzHyxFP1P4KLyfH0M+CrwZ0PiIzOfjIitI2KnzHxi6HJJkgbZ8ixJUnPcBHwtIv4ImJOZG4dZZyvgWxFxB/B31BLLQSsyc3Vmbgb6qXU3/hXgkcxcCZCZT5f9HgOcEBH9wM3ATsA+FfG9DbgkMzdl5qPADcABw6z3QGbeUeK4C7g2a/d43VFiGupQal8CAHxnyLIVmXl/6ZZ9SYkBYBPw/WH2dRDwk8EvATJzXSk/BjitnO/1wCzgdUO23RVYOziTmf2ZeVBmHgnsBTwCRER8LyL+T0TsUrftY8Brh4lHkqQtbHmWJGkcImIvasnfY8AbB8sz84yI+H/Ae6i1BL9rmM3/BHiUWuvvNGqt04NerJvexOj/owP4VGYum9BJjK4+js1185tHiWmkAVSGlg/OvzDO+5wD+K3MvHeUdZ6nllS/fMNas/afAccDfwV8htqXAH9ErUWbst3z44hHktSDbHmWJGmMSrfibwJ/nUNG3IyI15cW2y9T6+78q8AzwHZ1q+1ArSV5M7WuxNMrDnkvsGtEHFCOsV1EzACWAZ+IiK1K+RsiYtsh2w499j8CHy73S88DDgdWjPXcR3ETtcQU4LeHLDuw3BM9DfgwcGPFvpYDh5cu2NR1214GfKruPvD9h9n2Xxi+ZfwE4MrSiv0qal8CbC7Tg8n1a4B/rYhNktTjbHmWJGl025TuwlsBG6l1Tf7aMOv1RcSR1BKzu4CryvSmMjDWBcDfAN+PiBOo3QP87DD72SIzN5SBvf4qIrah1jr6TuDb1BLFn5bkby1D7kXOzCfKQF93llg+AxwM3EatBfgzmblmnM/FcP4YuDgiPgtcMWTZSuCvqd1HfR3wg9F2lJlrI+Jk4O9Lwv0YcDTwJeAs4PZS/gDw3iHbPhsRP4+IvTNzALbcY/471Lp9Q63ergQ2ULuXG+AtwPIRutlLkrSFP1UlSZKaLtrwM10R8X7gLZn5ioHBRtnmbGBJZl7busgkSd3AlmdJktQVMvMHEbHTODe708RZkjQWtjxLkiRJklTBAcMkSZIkSapg8ixJkiRJUgWTZ0mSJEmSKpg8S5IkSZJUweRZkiRJkqQKJs+SJEmSJFUweZYkSZIkqYLJsyRJkiRJFUyeJUmSJEmqYPIsSZIkSVIFk2dJkiRJkiqYPEuSJEmSVMHkWZIkSZKkCibPkiRJkiRVMHmWJEmSJKmCybMkSZIkSRVMniVJkiRJqjCj3QF0up133jkXLFjQ7jAkSZIkSS1wyy23PJ6Z86rWM3musGDBAlatWtXuMCRJkiRJLRARD45lPbttS5IkSZJUweRZkiRJkqQKJs+SJEmSJFUweZYkSZIkqYLJsyRJkiRJFUyeJUmSJEmqYPIsSZIkSVIFk2dJkiRJkiqYPEuSJLXI0r4+lvb1tTsMtUlfXx991r/UNWa0OwBJkqRutaa/v90hqI36rX+pq9jyLEmSJElShbYkzxHxrxFxR0T0R8SqUrZjRFwTEfeVv3NLeUTEORExEBG3R8Sb6/ZzYln/vog4sa78LWX/A2XbGO0YkiRJkiSNpp0tz0dm5sLMXFTmTwOuzcx9gGvLPMC7gX3K42TgG1BLhIEvAm8FDgS+WJcMfwP4eN12iyuOIUmSJEnSiDqp2/axwIVl+kLguLryi7JmOTAnInYF3gVck5nrMvNJ4BpgcVm2fWYuz8wELhqyr+GOIUmSJEnSiNqVPCdwdUTcEhEnl7JdMvORMr0G2KVM7wY8VLft6lI2WvnqYcpHO8bLRMTJEbEqIlatXbt23CcnSZIkSeou7Rpt+22Z+XBEvBq4JiJ+Vr8wMzMispUBjHaMzDwXOBdg0aJFLY1DkiRJktT52tLynJkPl7+PAT+gds/yo6XLNeXvY2X1h4Hd6zafX8pGK58/TDmjHEOSJEmSpBFNevIcEdtGxHaD08AxwJ3AEmBwxOwTgSvK9BLghDLq9kHAU6Xr9TLgmIiYWwYKOwZYVpY9HREHlVG2Txiyr+GOIUmSJEnSiNrRbXsX4Afl16NmABdn5tKIWAlcFhEnAQ8CHyrrXwm8BxgAngN+FyAz10XEl4CVZb2/yMx1ZfoPgQuAbYCrygPgjBGOIUmSJEnSiCY9ec7M+4HfGKb8CeAdw5QncMoI+zofOH+Y8lXAfmM9hiRJkiRJo+mkn6qSJEmSJKkjmTxLkiRJklTB5FmSJEmSpAomz5IkSZIkVTB5liRJkiSpgsmzJEmSJEkVTJ4lSZIkSapg8ixJkiRJUgWTZ0mSJEmSKpg8S5IkSZJUweRZkiRJkqQKJs+SJEmSJFUweZYkSZIkqYLJsyRJkiRJFUyeJUmSJEmqYPIsSZIkSVIFk2dJkiRJkiqYPEuSJEmSVMHkWZIkSZKkCibPkiRJkiRVMHmWJEmSJKmCybMkSZIkSRVMniVJkiRJqmDyLEmSJElSBZNnSZIkSZIqmDxLkiRJklTB5FmSJEmSpAomz5IkSZIkVTB5liRJkiSpgsmzJEmSJEkVTJ4lSZIkSapg8ixJkiRJUgWTZ0mSJEmSKpg8S5IkSZJUweRZkiRJkqQKJs+SJEmSJFUweZYkSZIkqULbkueImB4Rt0bED8v8nhFxc0QMRMT3ImJmKd+6zA+U5Qvq9vG5Un5vRLyrrnxxKRuIiNPqyoc9hiRJkiRJo2lny/MfA/fUzX8ZODMz9waeBE4q5ScBT5byM8t6RMS+wPHAm4DFwN+UhHw68HXg3cC+wEfKuqMdQ5IkSZKkEbUleY6I+cB/AL5d5gM4Cri8rHIhcFyZPrbMU5a/o6x/LHBpZr6YmQ8AA8CB5TGQmfdn5gbgUuDYimNIkiRJkjSidrU8nwV8Bthc5ncCfpmZG8v8amC3Mr0b8BBAWf5UWX9L+ZBtRiof7RgvExEnR8SqiFi1du3aiZ6jJEmSJKlLTHryHBHvBR7LzFsm+9hjlZnnZuaizFw0b968docjSZIkSWqzGW045qHA+yLiPcAsYHvgbGBORMwoLcPzgYfL+g8DuwOrI2IGsAPwRF35oPpthit/YpRjSJIkSZI0oklvec7Mz2Xm/MxcQG3Arx9n5m8D1wEfKKudCFxRppeUecryH2dmlvLjy2jcewL7ACuAlcA+ZWTtmeUYS8o2Ix1DkiRJkqQRddLvPH8W+HREDFC7P/m8Un4esFMp/zRwGkBm3gVcBtwNLAVOycxNpVX5k8AyaqN5X1bWHe0YkiRJkiSNqB3dtrfIzOuB68v0/dRGyh66zgvAB0fY/nTg9GHKrwSuHKZ82GNIkiRJkjSaTmp5liRJkiSpI5k8S5IkSZJUweRZkiRJkqQKJs+SJEmSJFUweZYkSZIkqYLJsyRJkiRJFUyeJUmSJEmqYPIsSZIkSVIFk2dJkiRJkiqYPEuSJEmSVMHkWZIkSZKkCibPkiRJkiRVMHmWJEmSJKmCybMkSZIkSRVMniVJkiRJqmDyLEmSJElSBZNnSZIkSZIqmDxLkiRJklTB5FmSJEmSpAomz5IkSZIkVTB5liRJkiSpgsmzJEmSJEkVTJ4lSZIkSapg8ixJkiRJUgWTZ0mSJEmSKpg8S5IkSZJUweRZkiRJkqQKJs+SJEmSpry+vj76+vraHYa62Ix2ByBJkiRJjerv7293COpytjxLkiRJklTB5FmSJEmSpAomz5IkSZIkVTB5liRJkiSpgsmzJEmSJEkVTJ4lSZIkSapg8ixJkiRJUgWTZ0mSJEmSKkx68hwRsyJiRUTcFhF3RcR/L+V7RsTNETEQEd+LiJmlfOsyP1CWL6jb1+dK+b0R8a668sWlbCAiTqsrH/YYkiRJkiSNph0tzy8CR2XmbwALgcURcRDwZeDMzNwbeBI4qax/EvBkKT+zrEdE7AscD7wJWAz8TURMj4jpwNeBdwP7Ah8p6zLKMSRJkiRJGlHDyXNE7BER7yzT20TEdqOtnzXry+xW5ZHAUcDlpfxC4LgyfWyZpyx/R0REKb80M1/MzAeAAeDA8hjIzPszcwNwKXBs2WakY0iSJEmSNKKGkueI+Di1ZPRvS9F84B/GsN30iOgHHgOuAX4O/DIzN5ZVVgO7lendgIcAyvKngJ3qy4dsM1L5TqMcQ5IkSZKkETXa8nwKcCjwNEBm3ge8umqjzNyUmQupJdsHAr/aYBxNFREnR8SqiFi1du3adocjSZIkSWqzRpPnF0vXaAAiYga1Lthjkpm/BK4DDgbmlO2hllQ/XKYfBnav2/8OwBP15UO2Gan8iVGOMTSuczNzUWYumjdv3lhPR5IkSZLUpRpNnm+IiM8D20TE0cDfAf93tA0iYl5EzCnT2wBHA/dQS6I/UFY7EbiiTC8p85TlP87MLOXHl9G49wT2AVYAK4F9ysjaM6kNKrakbDPSMSRJkiRJGtGM6lVGdRq1EavvAH4fuBL4dsU2uwIXllGxpwGXZeYPI+Ju4NKI+B/ArcB5Zf3zgO9ExACwjloyTGbeFRGXAXcDG4FTMnMTQER8ElgGTAfOz8y7yr4+O8IxJEmSJEkaUUPJc2ZuBr5VHmPd5nZg/2HK76d2//PQ8heAD46wr9OB04cpv5JaIj+mY0iSJEmSNJpGR9t+b0TcGhHrIuLpiHgmIp5uVnCSJEmSJHWCRrttnwX8JnBHuadYkiRJkqSu0+iAYQ8Bd5o4S5IkSZK6WaMtz58BroyIG4AXBwsz82sN7leSJEmSpI7RaPJ8OrAemAXMbDwcSZIkSZI6T6PJ82szc7+mRCJJkiRJUodq9J7nKyPimKZEIkmSJElSh2o0ef4EsDQinvenqiRJkiRJ3aqhbtuZuV2zApEkSZIkqVM1lDxHxOHDlWfmTxrZryRJkiRJnaTRAcP+S930LOBA4BbgqAb3K0mSJElSx2i02/Z/rJ+PiN2BsxqKSJIkSepRfX19AJx1lh+ppU7TaMvzUKuBNzZ5n5IkSVJP6O/vb3cIkkbQ6D3PfwVkmZ0GLAR+2mhQkiRJkiR1kkZbnlfVTW8ELsnMmxrcpyRJapKlpQvoYruASlJHsqv+1NHoPc8XNisQSZLUfGvsAipJHc2u+lPHhJLniLiDf++u/bJFQGbmrzcUlSRJkiRJHWSiLc/vbWoUkiRJkiR1sAklz5n54OB0ROwCHFBmV2TmY80ITJIkSZKkTjGtkY0j4kPACuCDwIeAmyPiA80ITJIkSZKkTtHoaNtfAA4YbG2OiHnAj4DLGw1MkiRJkqRO0VDLMzBtSDftJ5qwT0mSJEmSOkqjLc9LI2IZcEmZ/zBwZYP7lCRJkiSpo0z0p6q+Dlycmf8lIn4TeFtZdG5m/qBp0UmSJEmS1AEm2vL8L8BXI2JX4DLgO5l5a/PCkiRJkiSpc0zo/uTMPDszDwaOoHaf8/kR8bOI+GJEvKGpEUqSJEmS1GYNDe6VmQ9m5pczc3/gI8BxwD1NiUySJEmSpA7R6O88z4iI/xgR3wWuAu4FfrMpkUmSJEmS1CEmOmDY0dRamt8DrAAuBU7OzGebGJskSZIkSR1hogOGfQ64GDg1M59sYjySJEmSJHWcCSXPmXlUswORJEmSJKlTNXTPsyRJkiRJvcDkWZIkSZKkCibPkiRJkiRVMHmWJEmSJKmCybMkSZIkSRVMniVJkiRJqjDpyXNE7B4R10XE3RFxV0T8cSnfMSKuiYj7yt+5pTwi4pyIGIiI2yPizXX7OrGsf19EnFhX/paIuKNsc05ExGjHkCRJkiRpNO1oed4InJqZ+wIHAadExL7AacC1mbkPcG2ZB3g3sE95nAx8A2qJMPBF4K3AgcAX65LhbwAfr9tucSkf6RiSJEmSJI1o0pPnzHwkM39app8B7gF2A44FLiyrXQgcV6aPBS7KmuXAnIjYFXgXcE1mrsvMJ4FrgMVl2faZuTwzE7hoyL6GO4YkSZIkSSNq6z3PEbEA2B+4GdglMx8pi9YAu5Tp3YCH6jZbXcpGK189TDmjHEOSJEmSpBG1LXmOiNnA94G+zHy6fllpMc5WHn+0Y0TEyRGxKiJWrV27tpVhSJIkSZKmgLYkzxGxFbXE+buZ+fel+NHS5Zry97FS/jCwe93m80vZaOXzhykf7Rgvk5nnZuaizFw0b968iZ2kJEmSJKlrtGO07QDOA+7JzK/VLVoCDI6YfSJwRV35CWXU7YOAp0rX62XAMRExtwwUdgywrCx7OiIOKsc6Yci+hjuGJEmSJEkjmtGGYx4KfAy4IyL6S9nngTOAyyLiJOBB4ENl2ZXAe4AB4DngdwEyc11EfAlYWdb7i8xcV6b/ELgA2Aa4qjwY5RiSJEmSJI1o0pPnzLwRiBEWv2OY9RM4ZYR9nQ+cP0z5KmC/YcqfGO4YkiRJkiSNpq2jbUuSJEmSNBWYPEtqq6V9fSzt62t3GJIkSdKo2nHPsyRtsaa/v3olSZIkqc1seZYkSZIkqYLJsyRJkiRJFUyeJUmSJEmqYPIsSZIkSVIFk2dJkiRJkiqYPEuSJEmSVMHkWZIkSZKkCibPkiRJkiRVMHmWJEmSJKmCybMkSZIkSRVMniVJkiRJqmDyLEmSJElSBZNnSVLXWNrXx9K+vnaHIU1JXj/S1NbX10ef13BLzWh3AJIkNcua/v52hyC11WDyu/iss8a9rdePNLX1ew23nMmzJElSlzABlqTWsdu2JEmSJEkVTJ4lSZIkSapg8ixJkiRJUgWTZ0mSJEmSKpg8S5IkSZJUweRZaoC/iSlJkiT1Bn+qSmqAPwkiSZIk9QZbniVJkiRJqmDyLEmSJElSBZNnSZIkSZIqmDxLkiRJklTB5FmSJEmSpAomz5IkSZJaqq+vjz5/3lNTnD9VJUmSJKml+v15T3UBW54lSZIkqYPYUt+ZbHmWJEmSpA5iS31nsuVZkiRJkqQKJs+SJEmSJFUweZYkSZIkqUJbkueIOD8iHouIO+vKdoyIayLivvJ3bimPiDgnIgYi4vaIeHPdNieW9e+LiBPryt8SEXeUbc6JiBjtGJKkybe0r4+lDoYiSZKmiHa1PF8ALB5SdhpwbWbuA1xb5gHeDexTHicD34BaIgx8EXgrcCDwxbpk+BvAx+u2W1xxDEldzkSt86zp72eNA6JIktQWjug9fm0ZbTszfxIRC4YUHwu8vUxfCFwPfLaUX5SZCSyPiDkRsWtZ95rMXAcQEdcAiyPiemD7zFxeyi8CjgOuGuUYUkcbTPoWn3VWmyOZukzShudrS5Kk3uSI3uPXST9VtUtmPlKm1wC7lOndgIfq1ltdykYrXz1M+WjHkDqaiZ9axdeWJEnS2HTkgGGllTnbdYyIODkiVkXEqrVr17YyDEmSJEnSFNBJyfOjpTs25e9jpfxhYPe69eaXstHK5w9TPtoxXiYzz83MRZm5aN68eQ2dlCRJkiRp6uuk5HkJMDhi9onAFXXlJ5RRtw8Cnipdr5cBx0TE3DJQ2DHAsrLs6Yg4qIyyfcKQfQ13DEmSNEkcwE+SNBW15Z7niLiE2sBdO0fEamqjZp8BXBYRJwEPAh8qq18JvAcYAJ4DfhcgM9dFxJeAlWW9vxgcPAz4Q2ojem9DbaCwq0r5SMeQJEmTxHvtJUlTUbtG2/7ICIveMcy6CZwywn7OB84fpnwVsN8w5U8MdwxJkiRJkkbTSd22JUmSJEnqSCbPktQk3scpSZLUvTrpd54laUrzPk5JkqTuZcuzJEmSJEkVTJ4lSZLU0/r6+ujzthtJFey2LUmSpJ7W7203ksbAlmdJkiRJkiqYPEuSJEmSVMHkWZIkSZKkCibPkiRJkiRVMHlWx1ja18dSR7qUJEmS1IEcbVsdY40jXUqSJEnqULY8S+pJ9nRoHZ9bSZLUjWx5ltST7OnQOj63kiSpG9nyLEmSJElSBZNnSZKkNvN2B0nqfHbbliRJarNGb3cw8R6fvvJ8nXXWWW2ORNJUYsuzJHWAdrY6rRsY8IO3NMWt6e93vIFx6O/vp9/nS9I42fIsSR2gnR96N6xf74duSZKkCrY8S5IkSWq6/fffn/3337/dYUhNY8uzJLXAYDfoxd5PJ0nqUQ888EC7Q2gJ75nvXSbPktQCdoOWJE0FE0kEez159H753mXy3KVs9ZLUbXxfk6Tmm0giaPKoXmXy3KVs9ZLUbXxf01TglzyS1L1MniVJmgJMyqaGdn/Js25goK3H72S93tVYUuNMniVJmgKakZSZgHe/DevXtzuEjmVXY0mNMnmWJL2CSVZ3anerqLrLZLxP2FosqZOYPEuSXsEkS1KVyXifsLVYUieZ1u4AJEmSJEnqdCbPkiQ1aGlf35YurFNdN52LNNX19fVt6brejPUkNcZu25IkNaiburl307lo6jIRrBlrt3W7t0uTw+RZkiSpC6wbGGDj888zY5tt2h1KwyYrGTRJlzQeJs9Sj+i00ZPtFjoxnVaPkjrHhvXr2bxpU7vDmFJssVUjHA2+95g8Sz2i07pidlo8U0W3Pm9+KaBet25goN0hSBonv3zpPSbPUgeoShz8UKVud/fllwOtSZ5NzDUVbFi/vun7HOm1P55rYipcP6O1/tV3yx4YGGDvvfee1JgkdReTZ73MVPgn2Y2qWhNb8aFKvasTr/NWvsa7tbVenaMTrykY+bU/XPnSvj7WDQyw45Dksur6WTcwwNK+vqad+0SSztFa//r7+xkoX0Cvn8T/pZ3eIml3Y2liTJ6nuGb/w/ZDptSd6t8rvM6l5mrkmhpp/IdWJ+RDk941/f0T+hJrw/r1TX1PGZp0DjSh59VkJs3j1UgSO9K2Y9lnpyf3k2kyvkjwy4ru0ZPJc0QsBs4GpgPfzswz2hzShPkhWBPRqa0kap1ef69odutYr/K9o/nG0zpcr5G6WDcwwHNr1zbtfaGV19doie9kdo1uVfLTSBI70rYmxuMzGc/XVK0Tk/5X6rnkOSKmA18HjgZWAysjYklm3t3eyDQZ/OBXM9YPTJP1fE31xMaRw2s6+XloduvYeHXLe0+vfwnTSRqpi8FRuYe+9258/vlhx9ioeo8ez/VVdS0MDAzw6KOPsvXWW7No0SKef/75pfzZ2wAACnNJREFUly2vT5gvv/zyhu5hHmzVHss+Gkl+BgYG6Ovra2kC0owW+k7TrsSt/rgjTY9XK+pnMp6fqZr0t1LPJc/AgcBAZt4PEBGXAscCUzJ5Hvwn18oPZt3yoQ8m54PfZNXFWI/TSEIzWR+UBz94jeWclvb18eANN7DHEUdM6Dmufz5G+0A4nnpc09+/5Vocer/gRPfZTCPdy9iM/QIv6/Y5HusGBnjhl78c12t0qn7R0ulJZyOvzUZf1626Librelva18e/rVrFrDlzRlxntNftRAeEHOt26wYG+Nv99x/2PbM+6V03MMCmDRt4bu3aV+yj0S+fqm4bqU8C1q9fz8aNG8lMADYN+emt+g/z69evH1cCXG9gYIC1a9eyTQO/iT3W5GX9+vX09/e3NNmZSNf0waR+UCviq//yY7QEcrhjjzVxG2v39bGeX/1xh05PNAkeS/2M90uW8Sa2w30RMNLyRnR7a3UMvjn1ioj4ALA4M/9zmf8Y8NbM/ORw6y9atChXrVo1mSGOyw93fx07bDWDmdvOBuA1CxcC//5BbXB+rIbbbqL7Gq/JOE6zj9Gs56tqm1/cdCPAy+p5rMcZ+kFlItu2ok7q49rw7Pot51Z1vDX9/VvWn8hzXG+0/Yzn3AdjArbsb7yvjVbWydDnrH4fIz2fYznO0HWGe62N5hc33cjmTZuYtf0OW56/1x36tjGfx1hiGut2jap6vppxLTXjfX2kfYxlnZFiaPQ9r1XvM5P5v+uFp59i2vTpvO7Qtw17XYz2+ht8fx80eA2M9f9C/fobnq21KNfHMvS9aXDbwfUGy39x041s3rgJAhYcfsTLzm+k+Efa/3DP0eC5rOnv59mttuKon96yZfnX3/wW5r/0EgsXLuTGm25k48ZNRMAO2+/AU08/BcARJaaXJc/P/ntCMnvb2Swc5vj9/f3DrnfjTTeyadMmpk+fzuy6/z/D7aP+uPXLhysbumwwzpGOUbXfsRxj8HwA3nbo20bdpn7boXENF99o249l3Rt+cgNQq7/6GMeyv7EeY6Tna+j2I+1vtPWGTk/0ORvt3AePObjvqnhHirtqm+HOqz7+RuqgKq6t3/irvObznx/zPtohIm7JzEWV65k8vzJ5joiTgZPL7K8A9056oL1rZ+DxdgehSWe99y7rvndZ973Luu9d1n3v6vS63yMz51Wt1Ivdth8Gdq+bn1/KtsjMc4FzJzMo1UTEqrF866PuYr33Luu+d1n3vcu6713Wfe/qlrqf1u4A2mAlsE9E7BkRM4HjgSVtjkmSJEmS1MF6ruU5MzdGxCeBZdR+qur8zLyrzWFJkiRJkjpYzyXPAJl5JXBlu+PQsOwu35us995l3fcu6753Wfe9y7rvXV1R9z03YJgkSZIkSePVi/c8S5IkSZI0LibPapmIWBwR90bEQEScNszyT0fE3RFxe0RcGxF71C3bFBH95bGkrvyCiHigbllrf8BTE9Jg3b8uIq6OiHvKOgtK+Z4RcXPZ5/fKgH/qMC2qe6/7DjfReo+II+vqtT8iXoiI48oyr/kpoEV17zU/BTT4fv+XEXFXeb8/JyKilL8lIu4o+9xSrs7Sorq/vuxz8Lp/9WSe05hlpg8fTX9QG4zt58BewEzgNmDfIescCbyqTH8C+F7dsvUj7PcC4APtPj8fLa3764Gjy/TsuvUuA44v098EPtHuc/UxaXXvdd/Bj0brvW6dHYF1XvNT59HCuvea7/BHI3UPHALcVPYxHfhn4O1l2QrgICCAq4B3t/tcfUxa3V8PLGr3+VU9bHlWqxwIDGTm/Zm5AbgUOLZ+hcy8LjOfK7PLqf3mtqa+Cdd9ROwLzMjMa8p66zPzufKt5FHA5WWbC4HjWn8qGqem1/3kha4GNOv9/gPAVV7zU0rT676l0aqZGqn7BGZRS7y2BrYCHo2IXYHtM3N51rKpi/C670RNr/tJibpJTJ7VKrsBD9XNry5lIzmJ2jeMg2ZFxKqIWD7YjavO6aUbyJkRsXWT4lXzNFL3bwB+GRF/HxG3RsRXImI6sBPwy8zcOMZ9qj1aUfeDvO47V6Pv94OOBy4p017zU0Mr6n6Q13xnm3DdZ+Y/A9cBj5THssy8p2y/ehz7VHu0ou4H/e/SZfu/dmqXfZNntV1E/CdgEfCVuuI9MnMR8FHgrIh4fSn/HPCrwAHUunl9djJjVXMNU/czgMOAP6VWx3sBv9OW4NRS46x7r/suMcL7PaXF6deAZe2IS603zrr3mu8iQ+s+IvYG3kitNXI34KiIOKx9EapVxln3v52Zv0bts8BhwMcmP+JqJs9qlYeB3evm55eyl4mIdwJfAN6XmS8Olmfmw+Xv/dTugdi/zD+SNS8C/5ta1xF1lkbqfjXQX7oCbQT+AXgz8AQwJyJmjLZPtV0r6t7rvvM19H5ffAj4QWa+VOa95qeGVtS91/zU0Ejdvx9YXm7PWU+tVfLgsn19t36v+87Uirqv/+z/DHAxHXrdmzyrVVYC+5TRUmdS65K1pH6FiNgf+FtqF9VjdeVzB7toRcTOwKHA3WV+1/I3qN0Hc+cknIvGZ8J1X7adExHzyvxRwN3l3qfrqN0XB3AicEULz0ET0/S6L9t43Xe2Rup90Eeo67brNT9lNL3uyzZe852vkbr/BXBERMyIiK2AI4B7MvMR4OmIOKjU/Ql43Xeiptd9md+5bLsV8F469LqP2v8nqfki4j3AWdRG0zs/M0+PiL8AVmXmkoj4EbWuWo+UTX6Rme+LiEOoXXCbqX3Bc1Zmnlf2+WNgHrVRGPuBPyjfXKmDTLTuy7ZHA/+LWh3fApycmRsiYi9qg1LsCNwK/KdhWjDUZi2qe6/7DtdgvS+gNvrq7pm5uW6fXvNTQIvq3mt+Cmjgc9504G+Aw6kNILU0Mz9d9rmI2mjr21BrlfxUmqx0nGbXfURsC/yE2gBi04EfAZ/OzE2Te2bVTJ4lSZIkSapgt21JkiRJkiqYPEuSJEmSVMHkWZIkSZKkCibPkiRJkiRVMHmWJEmSJKmCybMkSV0uIjZFRH9E3BkRfxcRrxphvSsjYs5kxydJ0lTgT1VJktTlImJ9Zs4u098FbsnMr9UtD2qfCTaPtA9JknqdLc+SJPWWfwT2jogFEXFvRFwE3AnsHhH/GhE7A0TECRFxe0TcFhHfKWXzIuL7EbGyPA5t43lIkjSpZrQ7AEmSNDkiYgbwbmBpKdoHODEzl5flg+u9Cfgz4JDMfDwidizrnw2cmZk3RsTrgGXAGyfxFCRJahuTZ0mSut82EdFfpv8ROA94LfDgYOI8xFHA32Xm4wCZua6UvxPYdzDJBraPiNmZub51oUuS1BlMniVJ6n7PZ+bC+oKSAD87zv1MAw7KzBeaFZgkSVOF9zxLkqShfgx8MCJ2Aqjrtn018KnBlSJi4TDbSpLUlUyeJUnSy2TmXcDpwA0RcRswODL3HwGLykBidwN/0K4YJUmabP5UlSRJkiRJFWx5liRJkiSpgsmzJEmSJEkVTJ4lSZIkSapg8ixJkiRJUgWTZ0mSJEmSKpg8S5IkSZJUweRZkiRJkqQKJs+SJEmSJFX4/6IwmzOCCzBpAAAAAElFTkSuQmCC\n", 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" 281 | ] 282 | }, 283 | "metadata": { 284 | "needs_background": "light" 285 | }, 286 | "output_type": "display_data" 287 | } 288 | ], 289 | "source": [ 290 | "plot_order_book(ob)" 291 | ] 292 | }, 293 | { 294 | "cell_type": "code", 295 | "execution_count": null, 296 | "metadata": {}, 297 | "outputs": [], 298 | "source": [] 299 | }, 300 | { 301 | "cell_type": "code", 302 | "execution_count": 385, 303 | "metadata": {}, 304 | "outputs": [], 305 | "source": [ 306 | "def cash_in_orderbook(midpoint, diff, ob):\n", 307 | " \"\"\"A measure of the relative money needed to move the price from the midprice given the current orders. \n", 308 | " \n", 309 | " Note that this is normalized by the area of +/- diff about the midprice.\n", 310 | "\n", 311 | " midprice: float \n", 312 | " The midpoint of the spread.\n", 313 | " diff : float\n", 314 | " The relative amount in percentage terms you want the price to move from the midpoint.\n", 315 | " ob : dataframe\n", 316 | " The orderbook dataframe or ask / bid dataframe.\n", 317 | " \"\"\"\n", 318 | " \n", 319 | " oneside = ob[np.less_equal(np.absolute(ob['relprice']), abs(diff)) & (np.sign(ob['relprice']) == np.sign(diff))]\n", 320 | " bothsides = ob[np.less_equal(np.absolute(ob['relprice']), abs(diff))]\n", 321 | " onesidemoney = np.sum(oneside['price'] * oneside['volume'])\n", 322 | " totalmoney = np.sum(bothsides['price'] * bothsides['volume'])\n", 323 | " \n", 324 | " return onesidemoney / totalmoney\n", 325 | "\n", 326 | "\n", 327 | "def ask_to_total_fraction(ob, ub, n=100):\n", 328 | " \"\"\"Metric to calculate a series of relative strength of ask vs bid side.\n", 329 | " \n", 330 | " Basically the price weighted order volume on the ask side over the total price weighted order \n", 331 | " volume within x% of midprice. Returns a series of these at different x%s.\n", 332 | " \n", 333 | " Definition\n", 334 | " ----------\n", 335 | " A / (A + B), where\n", 336 | " A = sum(volume_i * price_i) for the ith order in the ask side between the midprice and the upper bound,\n", 337 | " B = similar metric just for bids.\n", 338 | " \n", 339 | " Parameters\n", 340 | " ----------\n", 341 | " ob : dataframe\n", 342 | " The orderbook with buy and ask orders.\n", 343 | " ub : float\n", 344 | " The relative price upper bound in percent difference from the midpoint.\n", 345 | " \n", 346 | " Returns\n", 347 | " -------\n", 348 | " prices : array\n", 349 | " A linear space of prices.\n", 350 | " metric : array\n", 351 | " The metric calculated at each price point.\n", 352 | " \"\"\"\n", 353 | " \n", 354 | " asks = ob[ob['type'] == 'ask']\n", 355 | " bids = ob[ob['type'] == 'bid']\n", 356 | " minask = asks.price.min()\n", 357 | " maxbid = bids.price.max()\n", 358 | " \n", 359 | " spread = minask - maxbid\n", 360 | " midprice = (minask + maxbid) / 2\n", 361 | " lb = max(asks.relprice.min(), abs(bids.relprice.max())) # lower bound\n", 362 | " prices = np.linspace(lb, ub, n)\n", 363 | " metrics = [cash_in_orderbook(midprice, p, ob) for p in prices]\n", 364 | " \n", 365 | " return prices, metrics\n", 366 | "\n", 367 | "\n", 368 | "def summarize_order_book(ob):\n", 369 | " \n", 370 | " asks = ob[ob['type'] == 'ask']\n", 371 | " bids = ob[ob['type'] == 'bid']\n", 372 | " minask = asks.price.min()\n", 373 | " maxbid = bids.price.max()\n", 374 | " \n", 375 | " spread = minask - maxbid\n", 376 | " midprice = (minask + maxbid) / 2\n", 377 | " spreadperc = 100 * spread / midprice\n", 378 | " \n", 379 | " # first cliff detection\n", 380 | " # define fc as largest vol order within delta% of midpoint\n", 381 | " fc_bounds = 2.0 # percent\n", 382 | " fc_ask = asks.loc[asks.loc[asks['relprice'] > fc_bounds, 'volume'].idxmax()]\n", 383 | " fc_bid = bids.loc[bids.loc[bids['relprice'] > -fc_bounds, 'volume'].idxmax()]\n", 384 | " \n", 385 | " # size of first cliff\n", 386 | " # define sfc as sum of volumes of orders within epsilon% of cliff\n", 387 | " sfc_bounds = 0.1 # percent\n", 388 | " sfc_ask = asks.loc[np.absolute(asks['relprice'] - fc_ask['relprice']) < sfc_bounds, 'volume'].sum()\n", 389 | " sfc_bid = bids.loc[np.absolute(bids['relprice'] - fc_bid['relprice']) < sfc_bounds, 'volume'].sum()\n", 390 | " \n", 391 | " # TODO:\n", 392 | "# Money / volume needed to move price to first cliff edge\n", 393 | "# Money / volume needed to move price to nth interval of first cliff edge\n", 394 | "# USD?\n", 395 | "# divide ask by bids or vice versa to see who has the relative upper hand against percent move from midprice\n", 396 | " \n", 397 | " return midprice, spread, spreadperc" 398 | ] 399 | }, 400 | { 401 | "cell_type": "code", 402 | "execution_count": 384, 403 | "metadata": {}, 404 | "outputs": [ 405 | { 406 | "data": { 407 | "image/png": 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\n", 408 | "text/plain": [ 409 | "
" 410 | ] 411 | }, 412 | "metadata": { 413 | "needs_background": "light" 414 | }, 415 | "output_type": "display_data" 416 | } 417 | ], 418 | "source": [ 419 | "prices, ask = ask_to_total_fraction(ob, np.absolute(ob['relprice']).max())\n", 420 | "fig, ax = plt.subplots(figsize=(16, 8)) # sharex='col'\n", 421 | "ax.plot(prices, y, 'k')\n", 422 | "ax.set_xlim([0, max(x)])\n", 423 | "ax.set_title('Ask side price weighted order volume as fraction of total price \\nweighted order volume within x% of midprice')\n", 424 | "ax.set_xlabel('Price difference from midprice (%)')\n", 425 | "ax.set_ylabel('Ask / Total')\n", 426 | "ax.grid()" 427 | ] 428 | }, 429 | { 430 | "cell_type": "code", 431 | "execution_count": null, 432 | "metadata": {}, 433 | "outputs": [], 434 | "source": [] 435 | }, 436 | { 437 | "cell_type": "code", 438 | "execution_count": 121, 439 | "metadata": {}, 440 | "outputs": [ 441 | { 442 | "data": { 443 | "image/png": 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\n", 444 | "text/plain": [ 445 | "
" 446 | ] 447 | }, 448 | "metadata": { 449 | "needs_background": "light" 450 | }, 451 | "output_type": "display_data" 452 | } 453 | ], 454 | "source": [ 455 | "sns.kdeplot(np.log(ob.volume));" 456 | ] 457 | }, 458 | { 459 | "cell_type": "code", 460 | "execution_count": 60, 461 | "metadata": {}, 462 | "outputs": [ 463 | { 464 | "data": { 465 | "text/plain": [ 466 | "665.1416330443618" 467 | ] 468 | }, 469 | "execution_count": 60, 470 | "metadata": {}, 471 | "output_type": "execute_result" 472 | } 473 | ], 474 | "source": [ 475 | "np.exp(6.5)" 476 | ] 477 | }, 478 | { 479 | "cell_type": "code", 480 | "execution_count": null, 481 | "metadata": {}, 482 | "outputs": [], 483 | "source": [] 484 | } 485 | ], 486 | "metadata": { 487 | "kernelspec": { 488 | "display_name": "Python 3", 489 | "language": "python", 490 | "name": "python3" 491 | }, 492 | "language_info": { 493 | "codemirror_mode": { 494 | "name": "ipython", 495 | "version": 3 496 | }, 497 | "file_extension": ".py", 498 | "mimetype": "text/x-python", 499 | "name": "python", 500 | "nbconvert_exporter": "python", 501 | "pygments_lexer": "ipython3", 502 | "version": "3.6.8" 503 | } 504 | }, 505 | "nbformat": 4, 506 | "nbformat_minor": 2 507 | } 508 | --------------------------------------------------------------------------------