Metadata-Version: 2.1
Name: btrccts
Version: 0.0.3
Summary: BackTest and Run CryptoCurrency Trading Strategies
Home-page: https://github.com/btrccts/btrccts/
Author: Simon Brand
Author-email: simon.brand@postadigitale.de
License: UNKNOWN
Keywords: btrccts
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Topic :: Software Development :: Build Tools
Classifier: Topic :: Office/Business :: Financial :: Investment
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Operating System :: OS Independent
Classifier: Environment :: Console
Description-Content-Type: text/markdown
Requires-Dist: appdirs
Requires-Dist: ccxt
Requires-Dist: numpy
Requires-Dist: pandas
Provides-Extra: dev
Requires-Dist: pycodestyle ; extra == 'dev'
Requires-Dist: pyflakes ; extra == 'dev'

# btrccts
## BackTest and Run CryptoCurrency Trading Strategies

### [Install](#install) - [Usage](#usage) - [Manual](#manual) - [Development](#development)

This library provides an easy way to backtest trading strategies and run them live with ccxt.
The purpose of this library is to provide a framework and an backtest exchange with the same
interface than ccxt - nothing less and nothing more.
If you want an library to compute performance metrics out of trades/orders,
you need an additional library.

## Install

The easiest way to install the BTRCCTS library is to use a package manager:

- https://pypi.org/project/btrccts/

You can also clone the repository, see [Development](development)

## Usage

For example algorithms see in [Examples](examples/)
```python
from btrccts import parse_params_and_execute_algorithm, AlgorithmBase


class Algorithm(AlgorithmBase):

    @staticmethod
    def configure_argparser(argparser):
        # Here you can add additional arguments to the argparser
        argparser.add_argument('--pyramiding', default=1, type=int)

    def __init__(self, context, args):
        # Context is used to create exchanges or get the current time
        self._context = context
        self._args = args

        # This will create a kraken exchange instance
        # The interface in backtesting and live mode is identical to CCXT.
        # See: [CCXT](https://github.com/ccxt/ccxt/wiki)
        # In live mode, this will be a plain ccxt instance of the exchange
        # The exchange keys will be read from the config directory (see --help)
        self._kraken = context.create_exchange('kraken')

        # You can access your own defined parameters
        print('Pyramiding:', args.pyramiding)

        # You can access predefined parameters like exchanges and symbols
        print('Exchanges:', args.exchanges)
        print('Symbols:', args.symbols)

    def next_iteration(self):
        # This method is executed each time interval

        # This is the current context date:
        print('context date', self._context.date())

        # Use the exchange to load OHLCV data
        ohlcv_len = 10
        ohlcv_offset = ohlcv_len * 60 * 1000
        ohlcv_start = int(self._context.date().value / 1000000 - ohlcv_offset)
        print(self._kraken.fetch_ohlcv(
            'BTC/USD', '1m', ohlcv_start, ohlcv_len))

        # Use the exchange to create a market order
        self._order_id = self._kraken.create_order(
            type='market', side='buy', symbol='BTC/USD', amount=0.1)

        # If you want to stop the algorithm in context or live mode, you can
        # do this:
        self._context.stop('stop message')

    def handle_exception(self, e):
        # This method is called, when next_iteration raises an exception, e.g.
        # because of an exchange error or a programming error.
        # If this method raises an exception, the algorith will stop with
        # reason EXCEPTION
        # If you are not in live mode, it is advicable to rethrow the
        # exception to fix the programming error.
        print(e)
        if not self._args.live:
            raise e

    def exit(self, reason):
        # This method is called, when the algorithm exits and should be used
        # to cleanup (e.g. cancel open orders).
        # reason contains information on why the algorithm exits.
        # e.g. STOPPED, EXCEPTION, FINISHED
        print("Done", reason)


# This method parses commandline parameters (see --help)
# and runs the Algorithm according to the parameters
result = parse_params_and_execute_algorithm(Algorithm)
# The result is an instance of Algorithm, you can now use saved
# information or exchanges to benchmark your performance.
print(result._kraken.fetch_closed_orders())
```

To run this algorithm, just execute the file with python.
e.g. `.venv/bin/python examples/algo_readme.py --start-date 2017-12-01 --end-date 2017-12-02 --timedelta 1h --exchanges kraken --symbols BTC/USD --start-balances '{"kraken": {"USD": 10000}}'`


## Manual

### Data and directories

Run your algorithm with `--help` to see the path to your config and data directories.

The data directory contains the ohlcv data:
`data_directory/ohlcv/EXCHANGE/BASE/QUOTE.csv`
e.g.
`data_directory/ohlcv/binance/BTC/USD.csv`

Data files are in the following format (readable with `pandas.read_csv`)
```csv
,open,high,low,close,volume
2019-10-01 10:10:00+00:00,200,300,100,300,1000
2019-10-01 10:11:00+00:00,300,400,200,400,2000
2019-10-01 10:12:00+00:00,400,500,300,500,3000
```
The data files are not yet provided with this library. You have to provide them yourself.


The config directory contains exchange keys.
e.g. `config_directory/binance.json`:
```json
{
    "apiKey": "key material",
    "secret": "secret stuff"
}
```
If an alias is provided (e.g. `--auth-aliases '{"kraken": "kraken_wma"}'`,
the file `config_directory/kraken_wma.json` is used.


### How orders get filled

- Market order

Market orders are executed immediatly with a price a little worse than current low/high.
Since we only have ohlcv data, we cannot use the next data, because this would introduce
a look-ahead bias
Some other backtesting libraries would wait until the next round to fill market orders,
but this is not what is happening in the real world (executing market orders immediatly).

- Limit order

Limit orders are filled, when the price is reached. Limit orders get filled
all at once, there is no volume calculation yet. If your bot uses huge limit orders,
keep in mind that the behavior on the exchange can be a partiall fill and leaving the
order open until filled.


### When next round is initiated in live mode / How interval is handled in live mode

When the algorithm is started, it will immediatly execute `next_iteration`.
Now the library waits until the next time interval and executes `next_iteration`.
If the `next_iteration` call takes longer than the interval, `next_iteration` is
called immediatly again. If `next_iteration` takes longer than multiple intervals,
only the last interval is rescheduled.

## Development

Setup a virtualenv:

```shell
git clone git@github.com:btrccts/btrccts.git
python3 -m venv .venv
.venv/bin/pip install -r etc/requirements.txt
.venv/bin/pip install -r etc/requirements_dev.txt
.venv/bin/pip install -e . --no-deps
```

### Run tests

```shell
.venv/bin/python -m unittest tests/unit/tests.py
.venv/bin/python -m unittest tests/integration/tests.py
```

## Contact us

btrccts@gmail.com


