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bin | ||
freqtrade | ||
scripts | ||
.coveragerc | ||
.dockerignore | ||
.gitignore | ||
.pylintrc | ||
.travis.yml | ||
config.json.example | ||
Dockerfile | ||
LICENSE | ||
MANIFEST.in | ||
README.md | ||
requirements.txt | ||
setup.py |
freqtrade
Simple High frequency trading bot for crypto currencies. Currently supports trading on Bittrex exchange.
This software is for educational purposes only. Don't risk money which you are afraid to lose.
The command interface is accessible via Telegram (not required).
Just register a new bot on https://telegram.me/BotFather
and enter the telegram token
and your chat_id
in config.json
Persistence is achieved through sqlite.
Telegram RPC commands:
- /start: Starts the trader
- /stop: Stops the trader
- /status [table]: Lists all open trades
- /count: Displays number of open trades
- /profit: Lists cumulative profit from all finished trades
- /forcesell <trade_id>|all: Instantly sells the given trade (Ignoring
minimum_roi
). - /performance: Show performance of each finished trade grouped by pair
- /balance: Show account balance per currency
- /help: Show help message
- /version: Show version
Config
minimal_roi
is a JSON object where the key is a duration
in minutes and the value is the minimum ROI in percent.
See the example below:
"minimal_roi": {
"40": 0.0, # Sell after 40 minutes if the profit is not negative
"30": 0.01, # Sell after 30 minutes if there is at least 1% profit
"20": 0.02, # Sell after 20 minutes if there is at least 2% profit
"0": 0.04 # Sell immediately if there is at least 4% profit
},
stoploss
is loss in percentage that should trigger a sale.
For example value -0.10
will cause immediate sell if the
profit dips below -10% for a given trade. This parameter is optional.
initial_state
is an optional field that defines the initial application state.
Possible values are running
or stopped
. (default=running
)
If the value is stopped
the bot has to be started with /start
first.
ask_last_balance
sets the bidding price. Value 0.0
will use ask
price, 1.0
will
use the last
price and values between those interpolate between ask and last
price. Using ask
price will guarantee quick success in bid, but bot will also
end up paying more then would probably have been necessary.
The other values should be self-explanatory, if not feel free to raise a github issue.
Prerequisites
- python3.6
- sqlite
- TA-lib binaries
Install
Arch Linux
Use your favorite AUR helper and install python-freqtrade-git
.
Manually
master
branch contains the latest stable release.
develop
branch has often new features, but might also cause breaking changes. To use it, you are encouraged to join our slack channel.
$ cd freqtrade/
# copy example config. Dont forget to insert your api keys
$ cp config.json.example config.json
$ python -m venv .env
$ source .env/bin/activate
$ pip install -r requirements.txt
$ pip install -e .
$ ./freqtrade/main.py
There is also an article about how to setup the bot (thanks @gurghet).*
* Note: that article was written for an earlier version, so it may be outdated
Docker
Building the image:
$ cd freqtrade
$ docker build -t freqtrade .
For security reasons, your configuration file will not be included in the image, you will need to bind mount it. It is also advised to bind mount a SQLite database file (see second example) to keep it between updates.
You can run a one-off container that is immediately deleted upon exiting with the following command (config.json must be in the current working directory):
$ docker run --rm -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
To run a restartable instance in the background (feel free to place your configuration and database files wherever it feels comfortable on your filesystem):
$ cd ~/.freq
$ touch tradesv3.sqlite
$ docker run -d \
--name freqtrade \
-v ~/.freq/config.json:/freqtrade/config.json \
-v ~/.freq/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
freqtrade
If you are using dry_run=True
it's not necessary to mount tradesv3.sqlite
.
You can then use the following commands to monitor and manage your container:
$ docker logs freqtrade
$ docker logs -f freqtrade
$ docker restart freqtrade
$ docker stop freqtrade
$ docker start freqtrade
You do not need to rebuild the image for configuration
changes, it will suffice to edit config.json
and restart the container.
Usage
usage: freqtrade [-h] [-c PATH] [-v] [--version] [--dynamic-whitelist]
{backtesting} ...
Simple High Frequency Trading Bot for crypto currencies
positional arguments:
{backtesting}
backtesting backtesting module
optional arguments:
-h, --help show this help message and exit
-c PATH, --config PATH
specify configuration file (default: config.json)
-v, --verbose be verbose
--version show program's version number and exit
--dynamic-whitelist dynamically generate and update whitelist based on 24h
BaseVolume
Backtesting
Backtesting also uses the config specified via -c/--config
.
usage: freqtrade backtesting [-h] [-l] [-i INT] [--realistic-simulation]
optional arguments:
-h, --help show this help message and exit
-l, --live using live data
-i INT, --ticker-interval INT
specify ticker interval in minutes (default: 5)
--realistic-simulation
uses max_open_trades from config to simulate real
world limitations
Hyperopt
It is possible to use hyperopt for trading strategy optimization.
Hyperopt uses an internal config named OPTIMIZE_CONFIG
located in freqtrade/optimize/hyperopt.py
.
usage: freqtrade hyperopt [-h] [-e INT] [--use-mongodb]
optional arguments:
-h, --help show this help message and exit
-e INT, --epochs INT specify number of epochs (default: 100)
--use-mongodb parallelize evaluations with mongodb (requires mongod
in PATH)
Execute tests
$ pytest freqtrade
Contributing
Feel like our bot is missing a feature? We welcome your pull requests! Few pointers for contributions: