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currency and other utilities

Project description

denarius

currency and other utilities

If you don’t find a way to make money while you sleep, you will work until you die. – Warren Buffett

introduction

denarius is a project, not a finished product. It features various utilities for collating cryptocurrency, mining, financial and other data, for plotting data and for accessing bank accounts. It features analyses of data for systematic descriptions, for predictions, for arbitrage etc.

setup

sudo apt-get install sqlite

wget https://github.com/mozilla/geckodriver/releases/download/v0.19.1/geckodriver-v0.19.1-linux64.tar.gz
tar -xvzf geckodriver-v0.19.1-linux64.tar.gz
rm geckodriver-v0.19.1-linux64.tar.gz
chmod +x geckodriver
sudo cp geckodriver /usr/local/bin/

sudo pip install denarius

Bitcoin values

The function ticker_Bitcoin returns data of the following form:

{'volume': 2050.1665002833397, 'last': 992.2553834529656, 'timestamp': 1487551580.0, 'bid': 991.8303740083114, 'vwap': 993.3415187004156, 'high': 1002.9278428409522, 'low': 981.3656970154892, 'ask': 992.2553834529656, 'open': 993.3887419720438}

It accesses data from Bitstamp.

feature description
last last Bitcoin price
high last 24 hours price high
low last 24 hours price low
vwap last 24 hours volume weighted average price
volume last 24 hours volume
bid highest buy order
ask lowest sell order
timestamp UNIX timestamp date and time
open first price of the day

The function data_historical_Bitcoin returns by default data of the following form:

{'bpi': {'2017-02-17': 992.1077, '2017-02-16': 969.2414, '2017-02-15': 952.6512, '2017-02-14': 954.1432, '2017-02-13': 940.7982, '2017-02-12': 940.1764, '2017-02-11': 949.3397, '2017-02-10': 933.4325, '2017-02-19': 991.254, '2017-02-18': 997.0854}, 'time': {'updated': 'Feb 20, 2017 00:20:08 UTC', 'updatedISO': '2017-02-20T00:20:08+00:00'}, 'disclaimer': 'This data was produced from the CoinDesk Bitcoin Price Index. BPI value data returned as EUR.'}

With the option return_list, it returns data of the following form:

[['2017-02-10', 933.4325], ['2017-02-11', 949.3397], ['2017-02-12', 940.1764], ['2017-02-13', 940.7982], ['2017-02-14', 954.1432], ['2017-02-15', 952.6512], ['2017-02-16', 969.2414], ['2017-02-17', 992.1077], ['2017-02-18', 997.0854], ['2017-02-19', 991.254]]

With the option return_UNIX_times, it returns data of the following form:

[[1486684800, 933.4325], [1486771200, 949.3397], [1486857600, 940.1764], [1486944000, 940.7982], [1487030400, 954.1432], [1487116800, 952.6512], [1487203200, 969.2414], [1487289600, 992.1077], [1487376000, 997.0854], [1487462400, 991.254]]

LocalBitcoins

LocalBitcoins data is available via its API. For example, the following URL gives data on current trades in GBP available by national bank transfer:

The data returned by the API is of a form like this.

The function values_Bitcoin_LocalBitcoin returns the price values returned by calling the API in this way.

import denarius
denarius.values_Bitcoin_LocalBitcoin()

The script loop_save_LocalBitcoins_values_to_database.py loop records LocalBitcoins data to database. To address closed gateways arising from repeat calls, the script could be used in a way like the following:

while true; do
    loop_save_LocalBitcoins_values_to_database.py --timeperiod=3600
    sleep 5400
done

databases

A database of Bitcoin values can be saved in the following ways:

import denarius
denarius.save_database_Bitcoin(filename = "database.db")
import denarius
denarius.save_database_Bitcoin(filename = "database_Bitcoin_EUR.db", currency = "EUR")
denarius.save_database_Bitcoin(filename = "database_Bitcoin_GBP.db", currency = "GBP")

graphs

The function save_graph_Bitcoin creates a graph of Bitcoin historical values over a specified time. The function save_graph_LocalBitcoins creates a graph of LocalBitcoins Bitcoin lowest prices in GBP as recorded in a database by the script loop_save_LocalBitcoins_values_to_database.py.

denarius_graph_Bitcoin

The script denarius_graph_Bitcoin.py displays a PyQt GUI with a graph of the last Bitcoin values.

denarius_graph_Bitcoin.py --help
denarius_graph_Bitcoin.py
denarius_graph_Bitcoin.py --currency=EUR --days=100

LocalBitcoins

A graph can be generated of Bitcoin GBP value versus LocalBitcoins GBP lowest value:

import denarius
denarius.save_graph_Bitcoin_LocalBitcoins()

A graph can be generated of Bitcoin GBP value versus LocalBitcoins GBP lowest 5 values:

import denarius
denarius.save_graphs_Bitcoin_LocalBitcoins()

A graph can be generated of LocalBitcoins normalized prices over days:

A graph can be generated of LocalBitcoins normalized prices over weeks:

A graph can be generated of LocalBitcoins non-normalized prices over weeks:

Bollinger bands

KanoPool

KanoPool records for addresses can be recorded to CSV in a way like the following:

denarius_loop_save_KanoPool.py --help

denarius_loop_save_KanoPool.py --addresses=1Miner7R28PKcTRbEDwQt4ykMinunhTehs --interval=10

The CSV data can be analysed using the Jupyter Notebook KanoPool.ipynb.

Nanopool

Nanopool records for addresses can be recorded to CSV in a way like the following:

denarius_loop_save_Nanopool.py --help

denarius_loop_save_Nanopool.py --addresses=0xbd3f1126d4c20f72a77e38dfda18622a6d663cd0

The CSV fields are, in order, as follows:

  • datetime
  • account
  • balance
  • earnings_per_day_BTC
  • earnings_per_day_ETH
  • earnings_per_day_EUR
  • earnings_per_hour_BTC
  • earnings_per_hour_ETH
  • earnings_per_hour_EUR
  • earnings_per_minute_BTC
  • earnings_per_minute_ETH
  • earnings_per_minute_EUR
  • earnings_per_month_BTC
  • earnings_per_month_ETH
  • earnings_per_month_EUR
  • earnings_per_week_BTC
  • earnings_per_week_ETH
  • earnings_per_week_EUR
  • hashrate
  • hashrate12hr
  • hashrate1hr
  • hashrate24hr
  • hashrate3hr
  • hashrate6hr
  • hashrate_pool
  • pool_miners
  • pool_workers

Slush Pool

Slush Pool records for an address can be recorded to CSV in a way like the following:

denarius_loop_save_SlushPool.py --help

denarius_loop_save_SlushPool.py --addresses=1Miner7R28PKcTRbEDwQt4ykMinunhTehs --interval=60 --alarm=11800000 --slushloginname=user --slushworkername=worker1

The CSV fields are, in order, as follows:

  • address
  • hash rate
  • shares
  • UNIX timestamp
  • unconfirmed reward in Bitcoin
  • confirmed reward in Bitcoin
  • total reward (confirmed + unconfirmed) in Bitcoin
  • total payout since script launch in Bitcoin
  • number of blocks found since script launch

The CSV data can be analysed using the Jupyter Notebook SlushPool.ipynb.

banks

The banks module provides utilities for getting transactions of a bank account (Monzo or RBS) using the Monzo API and the Teller API. To use this module, credentials files should be created.

Access the Monzo developers portal and create a new OAuth confidential client. Set the redirect URL for the client to https://github.com/pawelad/pymonzo. Create a URL of the following form using the client ID string:

https://auth.getmondo.co.uk/?response_type=code&redirect_uri=https://github.com/pawelad/pymonzo&client_id=<YOUR_CLIENT_ID>

Access this URL and authorize the client application. The confirmation e-mail sent contains a URL of the following form:

https://github.com/pawelad/pymonzo?code=<YOUR_AUTH_CODE>&state=

Copy the authorization code from this URL and then access the URL to authorize the client application.

Launch an interactive Python session and, using the client credentials and the pymonzo API interface, generate an access token and a refresh token.

client_id = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
client_secret = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
auth_code = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

from pymonzo import MonzoAPI

monzo = MonzoAPI(
    client_id     = client_id,
    client_secret = client_secret,
    auth_code     = auth_code
)

This saves an authorization that lasts 48 hours to the file ~/.pymonzo-token, which contains a dictionary of the following form:

{
    "access_token": "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
    "client_id": "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
    "client_secret": "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
    "expires_at": 1517106306.8881364,
    "expires_in": 172799,
    "refresh_token": "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
    "token_type": "Bearer",
    "user_id": "xxxxxxxxxxxxxxxxxxxxxxxxxxx"
}

Now, API access can be tested by calling the monzopy Monzo API interface without any arguments (such that it loads tokens from the file ~/.pymonzo-token and manages token refreshes).

from pymonzo import MonzoAPI

monzo = MonzoAPI()

print(monzo.accounts())
print(monzo.balance())
print(monzo.transactions())

For RBS, the credentials file (by default ~/.rbs) should have content of the following form:

token_teller        = "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"
account_code_teller = "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"

Pandas DataFrames of transactions can be retrieved in ways like the following:

import denarius.banks
df = denarius.banks.transactions_DataFrame_Monzo()
df = denarius.banks.transactions_DataFrame_RBS()

A payment can be searched for in ways like the following:

denarius.banks.payment_in_transactions_Monzo(reference = "271828182", amount = 314)
denarius.banks.payment_in_transactions_RBS(reference = "271828182", amount = 314)

These functions search the “counterparty_reference” and “description” fields of a DataFrame of bank account transactions for a specified reference. If the reference is found, the specified amount of the payment is compared to the sum of amounts found for the specified reference in the “amount” field. They return a dictionary of the following form:

{
    "reference_found":   bool,      # True if reference found
    "amount_correct":    bool,      # True if sum of amounts found is amount specified
    "valid":             bool,      # True if reference found and amount correct
    "transactions"       DataFrame, # DataFrame of matches
    "amount_difference": float      # difference between amount specified and sum of amounts found
}

So, these functions could be used in a straightforward boolean way to check if a payment has been made:

denarius.banks.payment_in_transactions_Monzo(reference = "271828182", amount = 314)["valid"]

They also could be used in a more involved way to account for occasions in which a payment is found but has an incorrect amount and a further payment with the same reference must be requested.

Both the transactions_DataFrame and payment_in_transactions functions have the option print_table which can print to terminal a table of the transactions under consideration:

df = denarius.banks.transactions_DataFrame_Monzo(print_table = True)

The script print_table_bank_account.py uses this functionality to print to terminal a table of transactions from a specified bank.

RBS

The RBS module provides utilities for getting the balance and recent transactions of an RBS account using the RBS banking web interface, Selenium and Firefox. For convenience, account details can be stored in a credentials file, which is assumed by default to be ~/.rbs. The account code is an alphanumeric code extracted from the web interface. The content of a credentials file is of the following form, which is Python code:

customer_number = "XXXXXXXXXX"
PIN             = "XXXXXX"
passcode        = "XXXXXXXXXXXXXXXXXXXX"
account_code    = "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"

A dictionary of the current balance and a pandas DataFrame of recent transactions is returned by the function RBS.account_status.

import denarius.RBS

status  = RBS.account_status()
balance = status["balance"]
df      = status["transactions"]

The DataFrame features the fields date, description and amount. A transaction containing a certain reference or description could be selected in the following way:

df[df["description"].str.contains("transaction reference 123")]

The existence of a transaction can be tested in a way like the following:

if df[df["description"].str.contains("transaction reference 123")].values.any():

    print("transaction found")

else:

    print("transaction not found")

The script get_account_balance_RBS.py is available to open an RBS account web interface and to display the current balance and recent transactions in the terminal, optionally in a loop.

get_account_balance_RBS.py --loop

The script detect_transaction_RBS.py is available to search for a transaction or transactions with a specified reference.

detect_transaction_RBS.py --reference=123

The script loop_save_RBS_to_CSV.py is available to loop save RBS transactions and balance to CSV, merging with recorded CSV data to avoid recording duplicates.

Santander

The Santander module provides utilities for getting recent transactions using the Santander banking web interface, Selenium and Firefox. Account details are stored in a credentials file, which is assumed by default to be ~/.santander. The content of a credentials file is of the following form, which is Python code:

customer_number          = "XXXXXXXX"
customer_PIN             = "XXXXX"
security_question_answer = "XXXXXXXX"

A DataFrame of recent transactions is returned by the function Santander.transactions_DataFrame. The script loop_save_Santander_to_CSV.py saves transactions to CSV in a continuous loop. The function Santander.payment_in_transactions_CSV can search in transactions recorded in CSV for a specified transaction reference together with a specified value and returns a boolean to indicate whether the transaction was detected.

arbitrage

The script loop_save_arbitrage_data_Kraken_LocalBitcoins_UK.py records data for arbitrage between Kraken and LocalBitcoins UK.

The script loop_display_arbitrage_data.py is available for display of recorded data and current prices for arbitrage between Kraken and LocalBitcoins UK.

paper wallets for Bitcoin, QR codes of keys

The script create_QR_codes_of_public_and_private_keys.py creates a QR code for a specified public key and private key and enables optional specification of the size of the resulting PNG images. It loads the keys from a Python file (keys.py by default) which defines the string variables key_public and key_private.

The script create_paper_wallet.py creates a QR code for a specified public key and private key. It then creates an image of a Bitcoin paper wallet. It loads the keys from a Python file (keys.py by default) which defines the string variables key_public and key_private.

Faster Payments Service

  • 2018-01-14 participants
  • Barclays
  • Citi
  • Clear Bank
  • Clydesdale Bank
  • The Co-operative Bank
  • HSBC
  • Lloyds Bank
  • Metro Bank
  • Monzo
  • Nationwide
  • NatWest
  • Northern Bank
  • Raphaels Bank
  • Royal Bank of Scotland
  • Santander
  • Starling Bank
  • Turkish Bank UK

SEPA Instant

  • 2018-01-14 participants
  • Austria
  • Belgium
  • Bulgaria
  • Estonia
  • France
  • Germany
  • Italy
  • Latvia
  • Lithuania
  • Malta
  • Netherlands
  • Spain

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